Hui Deng

Limin Wang

Jia Liu

Dandan Li

Zhongxin Chen

Qingbo Zhou

Study on Application of Scale Invariant Feature Transform Algorithm on Automated Geometric Correction of Remote Sensing Images

Keywords: remote sensing image, automatic geometric correction, SIFT algorithm, least squares

In recent years, data collected from remote sensing satellite and aerophotography have been showing a geometric sequence increase. A method of Scale Invariant Feature Transform (SIFT) algorithm could be employed for the automatic geometric fine correction. This method could avoid the impact of the rotation and zooming of template matching during the image matching process, and it can also save the labor during the image processing operation. Based on the SIFT algorithm, this paper proposes a two-step method, which firstly conducts coarse match on feature points, and then further conducts fine correction on the coarsely matched feature points by using the least squares technique. The result indicates that, this method is an effective automatic matching method for remote sensing images.

Introduction

Along with the rapid increase of huge amount of remote sensing data, the workload of geometric fine correction on manual image matching for the image data collected from different sources and in different time period is huge. Image matching is the preliminary work of a lot of remote sensing applications (such as multi-source data fusion, variation detection, image mosaic, motion detection, and object identification, etc, and its result directly affects the quality of the follow-up works [START_REF] Zhang | Research on the Line-Based Registration of Multi-Source Remote Sensing Imagery[END_REF]. Currently, the matching technology for the satellite remote sensing images from the same sensor, at different time period, with small angular variation has been relatively mature, and some classical feature extraction algorithms, such as arithmetic operators [START_REF] Smith | SUSAN-A New Approach to Low Level Image Processing[END_REF] , arithmetic operators [START_REF] Harris | A Combined Corner and Edge Detector[END_REF], are extracted. They proposes a multiscale registration technique using robust Scale Invariant Feature Transform (SIFT) features in Steerable-Domain [START_REF] Liu | Multiscale registration of remote sensing image using robust SIFT features in Steerable-Domain[END_REF]. The matching the images from multi-sensor with different resolutions is the focus and the difficulty of current researches [START_REF] Dufournaud | Matching Images with Different Resolutions[END_REF]. Scholars both at home and abroad have conducted extensive in-depth research on this topic, but no perfect method, which could correctly match the multi-source image under different radiation distribution, dimension, rotation, and translation, has been established.

In recent years, scale invariant Feature Transform (SIFT) feature has achieved great success in image matching. The matching method based on the SIFT descriptor has been successfully applied in many fields, such as object identification [START_REF] Lowe | Distinctive Image Features from Scale-Invariant Keypoints[END_REF] , motion panorama [START_REF] Brown | A. Multi-view Matching for Unordered Image Sets, or How do I Organize my Holiday Snaps[END_REF], and Structure From Motion (SFM) [8]. SIFT algorithm was introduced [START_REF] Lowe | Distinctive Image Features from Scale-Invariant Keypoints[END_REF] and [START_REF] Lowe | Object Recognition from Local Scale-Invariant Features[END_REF] in 1999 and was well established in 2004. They also conducted experiments on the many representative descriptors under six circumstances, such as different scenes, change in illumination, and geometry deformation of images, resolution difference, rotation, blurring, and image compression. The results indicate that SIFT descriptor has the best performance [START_REF] Mikolajczyk | A Performance Evaluation of Local Descriptors[END_REF].

Introduction to the algorithm

The extraction and matching algorithm based on SIFT point features mainly includes three steps: extraction of point features, computation of feature descriptors, and feature matching from [START_REF] Lowe | Distinctive Image Features from Scale-Invariant Keypoints[END_REF] and [START_REF] Li | Robust scale-invariant feature matching for remote sensing image registration[END_REF].

The Gaussian smoothing is applied on original image with different standard deviation σ, to obtain the Gaussian differential image. The maximum or minimum gray-scale values on differential images are taken as the feature points. Formula (1) is gaussian smoothing formula, and the extraction process of feature points is illustrated in the Figure 1 (1)

By taking feature point as the center, the image area with specified height and width is taken, to calculate the gradient direc0 tion and gradient strength of each pixel within the area. The total gradient strength of different gradient directions are calculated and are taken as the eigen vector. The calculation process of the eigen vector is illustrated in the Figure 3. In the Figure, the arrow in the left stands for the gradient strength and direction. The total gradient strengths of 8 directions within the 2×2 sub-areas are calculated separately, so as to obtain an eigen vector with 32 dimensions. The calculation of Lowe's study [START_REF] Lowe | Distinctive Image Features from Scale-Invariant Keypoints[END_REF] was on 8 directions within a 4×4 sub-area, so the feature descriptor of each SIFT point feature is the eigen vector of 4×4×8=128 dimensions.

Analysis and Conclusion

Firstly, feature point and eigen vector information of images to be corrected and Reference images is calculated. The partitioning technology is used to locate feature point pairs, so as to improve the feature point search efficiency, and then the coarse matching is conducted based on both images to find out the matched feature point pairs on both images. After the coarse matching, the fine matching on feature points by using least squares algorithm, to ensure that the total error of the feature points after fine matching is less than 0.5 pixel. Finally, the geometry fine correction based on finely matched feature points is conducted.

Figure 3. Flow Chart of geometry fine correction

Feature points and eigenvector refer to the local features of detected images (e.g. edge, angular point, outline, etc). Then it is necessary to combine and convert the features based on the need of matching targets, so as to form eigen vectors which are easy to be matched with good stability, to convert the image geometry correction issue into feature matching issue, and further to convert the feature matching issue into clustering issue of eigenvector of feature space. Eigen vector information is a set

Detection of feature points

Images to be matched  The error between simulation feature points and the feature points on Reference image is calculated and the maximum error is calculated.

 When the maximum errors are larger than the system preset default parameter of 0.5, the feature point is deleted and the procedure return to A total of 20 pairs of control points are randomly selected from the image after fine correction and the original Reference images, to conduct the accuracy analysis, and the final error result is obtained, which is illustrated in the following Figure .The error of the X direction is 1.1018, the error of Y direction is 0.8426, and the total error is 1.3870.

Reference image Image to be corrected

The paper analyses the limitation of the conventional SIFT algorithm in the matching of remote sensing images, and the extraction time and matching accuracy of feature points is optimized. Optimization algorithm can extract more matched feature points with higher accuracy. Based on the SIFT algorithm improved by this paper, and by firstly conducting coarse matching and then fine correction, the error is reduced from 7.0422 to 1.3870. The accuracy of the automatic matching of remote sensing images is improved, which is suitable for the business operation. However, the expenses for the system calculation time is relatively high. The further optimization on algorithm in the aspect of parallel computation could be conducted so as to reduce the operation time.

  . In the Figure, each pixel on the Gaussian differential image (expressed by crosshair) is compared with 26 adjacent pixels (expressed by dot) within the 3X3 area of current and upper and lower adjacent dimensions. If the average values of Difference of Gaussian of a pixel is larger or smaller than the adjacent pixels, the pixel is taken as the local extreme point. Then, some local extreme points are eliminated according to the preset threshold value of Difference of Gaussian and edge effect standard, and remaining extreme points are taken as the final SIFT feature points.

Fig. 1 .

 1 Fig. 1. SIFT point feature extraction process (Lowe,2004)Euclidean distance of the eigen vector of different feature points of image to be matched and Reference image is calculated, and the feature point with minimum distance is taken as the initial matching point. The error matching points are eliminated according to the nearest neighbor and second nearest neighbor Euclidean distance ratio.

Figure 2 .

 2 Figure 2. Feature descriptor calculation process (Lowe,2004)

  128 dimensions. The following Figure illustrates part information of feature points and eigen vector of the Reference image. The matching is conducted according to the similarity of eigen vector. Usually, various distance functions are taken as the similarity measurement of features, such as Euclidean distance and Mahalanobis distance, etc. The following figure is the schematic diagram of feature point location of two images after coarse matching. The SIFT algorithm is used to extract the 435 feature point pairs of two images. A total of 50 pairs of feature points are randomly selected from 435 pairs of feature points and they are taken as the ground control points input into the ERDAS geometric correction procedure, to eventually get the error result, which is illustrated in the following Figure. In the figure, the error of the X direction is 4.9251, error of Y direction is 5.0334, and the total error is 7.0422.

Figure 4 .

 4 Figure 4. Error result of coarse matching feature points as GCP The fine matching on the feature points of the coarse matching is conducted by using least squares algorithm, and the final matching accuracy is optimized. The specific operation process is as follows:  To establish the dualistic quadratic regression equation between the coarsely matched feature points.  The feature points are calculated based on the regression equation of each feature point of image to be corrected.

step 1

 1 to recalculate; otherwise, the fine matching of feature point pairs is completed. Then exit the calculation. The following figure is the schematic diagram of feature point location after fine matching. The number of the feature point pairs after fine matching is 134.

Figure 5 .

 5 Figure 5. Schematic diagram of feature point pair location after fine matching A total of 134 pairs of feature points are input into the ERDAS geometric correction module, to eventually get the error result, which is illustrated in the following Figure. The error of X direction is 2.4689, error of Y direction is 2.1418, and the total error is 3.2685.Resampling of corrected image is conducted by using the feature points after fine matching, to finish the final geometry fine correction.The following figure is the schematic diagram of the location of the image after fine correction and the original Reference image.

Foundation: The National High Technology Research and Development Program ("863" Program) of China (No.

2012BAH29B02, No. NC2010FB0002) and National Non-Profit Scientific Institution, Ministry of Finance of China(202-15) First Author:Key Lab. of Resources Remote Sensing & Digital Agriculture, Ministry of Agriculture, Beijing 10081, PR China. E-mail address: denghuizxy@126.com.


Jie Liang

Jianyu Yang
email: jianyuyangycjyyang@cau.edu.cn

Chao Zhang

Jiabo Sun

Dehai Zhu

, Liangshu Shi

Jihong Yang

An Object-Oriented Binary Change Detection Method Using Nearest Neighbor Classification

Keywords: Object-Oriented, Binary Change Detection, Nearest Neighbor Classification, RSIM

Threshold selection is a critical step in using binary change detection methods. The threshold determines the accuracy of change detection results but is highly subjective and scene-dependent, depending on the familiarity with the study area and the analyst's skill. Nearest neighbor classification is a nonparametric classifier, which was applied to remove the threshold. In order to find the most suitable feature to detect construction and farmland changes, a variety of single and multiple variables were explored. They were regional similarity (RSIM), brightness difference images (BDIs), multi-band difference images (MDIs), multi-band ratio difference images (MRDIs), a combination of RSIM and BDIs (RSIMBD), a combination of RSIM and a optimum band difference and a optimum band ratio difference (RSIMDR), MDIs and MRDIs multiple variable groups. All were tested for two study sites of the bi-temporal SPOT 5 imagery, the results indicated that RSIM, RSIMDR, RSIMBD were significantly better than other single and multiple variables.

Introduction

Change detection is the process of indentifying differences in the state of an object or phenomenon by observing it at different times. [START_REF] Singh | Digital Change Detection Techniques Using Remotely-Sensed Data[END_REF] It is a core problem in resource and environmental monitoring, disaster monitoring, land management, city expansion, geographic information update and dynamic monitoring of the military battlefield. In the past years, researchers have put forward a large number of change detection techniques which can be categorized into two groups: (1) those detecting binary change information (i.e., change vs. no change); and (2) those detecting detailed 'from-to' change information. [START_REF] Chan | Detecting the Nature of Change in an Urban Environment: A Comparison of Machine Learning Algorithms[END_REF] 'From-to' change detection algorithms (e.g., post-classification comparison) provide detailed information about the location and type of land_use/cover change (e.g., from farmland to building) for every pixel or object under investigation. The key in using these methods is to create accurate thematic classification images. The errors of individual-date thematic images will affect the final change detection accuracy. [START_REF] Lu | Change Detection Techniques[END_REF] Even though the ideal goal of change detection is often to extract 'fromto' change information, however, sometimes rapidly obtaining change and no change information is valuable. A variety of methods have been applied to binary change detection, for example, image differencing, image ratioing, vegetation index differencing and PCA. [START_REF] Lunetta | Impacts of Vegetation Dynamics on the Identification of Land-Cover Change in a Biologically Complex Community in North Carolina[END_REF][START_REF] Bruzzone | Automatic Analysis of the Difference Image for Unsupervised Change Detection[END_REF][START_REF] Ridd | A Comparison of Four Algorithms for Change Detection in an Urban Environment[END_REF] One critical step in using binary change detection approaches is to select the appropriate thresholds to generate binary change mask. Empirical threshold method is usually used for selection of thresholds, which is an interactive procedure or manual trail-and-error procedure. For example, the optimum threshold can be determined by using the mean and standard deviation of the training samples. [START_REF] Lu | Land-Cover Binary Change Detection Methods for Use in the Moist Tropical Region of the Amazon: a Comparative Study[END_REF] However, the threshold is highly subjective and scene-dependent, depending on the familiarity with the study area and the analyst's skill. In order to improve the change detection results, many semi-automatic or automatic threshold determination methods are developed. Im et al. used a calibration approach by autonomously testing thousands of thresholds and selected the optimum threshold(s) showing the highest accuracy. [START_REF] Im | An Automated Binary Change Detection Model using a Calibration Approach[END_REF] The systematic search technique and a Moving Threshold Window (MTW) were proposed to improve the efficiency and accuracy, respectively.[9, 10] Wang et al. put forward adaptive spatial neighborhood analysis and Rayleigh-Gauss distribution fitting for change detection in multi-temporal remote sensing images, while the optimal threshold was calculated automatically and effectively by the improved Kittler-Illingworth (KI) threshold selection algorithm. [START_REF] Wang | Adaptive Spatial Neighborhood Analysis and Rayleigh-Gauss Distribution Fitting for Change Detection in Multi-Temporal Remote Sensing Images[END_REF] Although some advanced threshold approaches have been developed, these are still only applied to specific applications for a specific environment due to its complexity. Nearest neighbor classification as a non-parametric classifier, is only determined by the quality of samples and the selection of features, which is independent of threshold.

Change detection is a complicated and integrated process. [START_REF] Li | Remotely Sensed Images and GIS Data Fusion for Automatic Change Detection[END_REF] Successful remote sensing change detection requires careful attention to: (1) remote sensor system considerations, such as radiometric resolution, spatial resolution, look angle, and (2) environmental characteristics of the study areas, including atmosphere condition, complexity of the landscape and topography. [START_REF] Jensen | Introductory Digtial Image Processing: A Remote Sensing Perspective[END_REF] After image data and study areas have been determined, selection of an appropriate change detection method assumes considerable significance. Traditional binary change detection methods usually use a single variable (e.g., band 1 difference). However, the use of multiple bands simultaneously might produce better results (e.g., band 1 difference and band 2 ratio difference). Thus, how to customize the optimum multiple variables becomes another problem. In addition, now there is great interest in indentifying change in high spatial resolution multi-temporal data, such as that provided by GeoEye, Inc., DigitalGlobe, Inc., Leica Geosystems, Inc. Unlike medium-to coarse-resolution imagery, there are ample spectral information, textural information, structure information in high spatial resolution imagery. Many traditional change detection methods based on per-pixel analysis may not function successfully in the high-resolution domain. A serious problem found in per-pixel approaches is the 'salt-and-pepper' phenomenon in change detection results, which is meaningless for application. Therefore, objectedoriented change detection methods have been put forward where homogeneous polygons found in two dates of imagery are compared. [START_REF] Im | Object-Based Change Detection Using Correlation Image Analysis and Image Segmentation[END_REF][START_REF] Walter | Object-Based Classification of Remote Sensing Data for Change Detection[END_REF] Hence, the objectives of this study included: (1) development of an object-oriented binary change detection method using nearest neighbor classification to replace the thresholds, (2) customization of a variety of single and multiple image variables to extract change information, and (3) validation of binary change classifications with the variables, especially focusing on a comparison between the detection results using single versus multiple image variables.

2

Materials and Methods

Study Area and Data

Two study sites representing new construction in Changping District, Beijing were selected to demonstrate the nearest neighbor classification for object-oriented binary change detection (Fig. 1). The first study site (Site A) is located in southeast Changping District, and exhibits new buildings and change between different growth farmlands. The second study site (Site B) is located 1km east of Site A, and exhibits change from farmlands and water to buildings. SPOT 5 imagery acquired over the two study sites in Changping District on October 01, 2002 and October 16, 2007 was used for validating the binary change detection. The area obtained spectral information in four multispectral bands (i.e., near-infrared (NIR), red, green, and short waved-length infrared (SWIR)) with 10meter spatial resolution and one panchromatic band with 2.5-meter spatial resolution (wavelength ranges from 0.48-0.71μm).

In the preprocessing, the bi-temporal images were geo-referenced to a Transverse Mercator (UTM) projection in WGS 84. Rectification errors were less than 0.5 pixel (RMSE). Even though they were obtained on near-anniversary dates, it was still necessary to radiometrically normalize the two dates of remote sensor data since the subsequent analysis (e.g., multi-band differencing, multi-band ratio differencing) directly compared the Digital Number (DN) values from the bi-temporal imagery. The radiometric normalization method using histogram matching was applied. Then Principal Component Analysis method was used to do image merge to get high spatial resolution and fine detail.

Methods

This object-oriented change detection research aimed to identify the single or multiple variable(s) that is/are most suitable for extracting buildings and farmlands change regions through a comparison of the customized features using the nearest neighbor classification. In order to generate one-to-one mapping relationship of the object with the bi-temporal imagery, two dates of imagery were used together in multi-resolution segmentation procedure. Then, several training/test data were randomly collected within each study site. The binary change information for each sample location (i.e., change vs no change) was identified through visual interpretation of the bi-temporal SPOT 5 imagery. A variety of variables associated with change information were customized and applied to the nearest neighbor classification. A brief summary of the change detection flow was found in Fig. 2.

Nearest Neighbor Algorithm to Binary Change Detection Method

Nearest neighbor classifier is a popular non-parametric classifier used in many field. [START_REF] Viswanath | Partition Based Pattern Synthesis Technique with Efficient Algorithms for Nearest Neighbor Classification[END_REF] When input several samples for each class, the nearest neighbor classifier computes the distance from the object to be classified to the nearest training object and assigns it to the class of the training object. Due to its simplicity and avoiding threshold problem, it is selected as a technique for change classification. However, there are two mainly disadvantages of nearest neighbor classifier. One is that its classification accuracy depends on the training data. Therefore, 24-26 objects as training samples were collected for each study site through visual inspection with at least 12 objects per class. For a reasonable comparison between the change detection results, the same training data for each site were used in the classification. And the other is called the 'curse of dimensionality'. Dimensionality means the number of features. Usually when the dimensionality is more than 20, the calculation is very slow. What's worse, the accuracy of the classification tends to deteriorate after the dimensionality up to a specific value. So it is necessary to optimize the feature space.

Single and Multiple Variables

In order to identify binary change detection methods that were most appropriate for new construction, a variety of variables were explored. These variables included regional similarity (RSIM), brightness difference images (BDIs), multi-band difference images (MDIs), multi-band ratio difference images (MRDIs) generated from the bi-temporal imagery were used as input variables. Every variable was introduced as follows: RSIM [START_REF] Zhang | Object-Oriented Change Detection Method based on Regional SIMilarity[END_REF] is used to detect change by evaluating the degree of regional similarity in the same region in two registered remote sensing images obtained on different dates. If the spectral changes of the object between two image dates are significant, the RSIM will fall off to a lower value. There exists three parts of RSIM, which are correlation, brightness ratio and contrast. See the equations: Brightness difference images (BDIs) is used to detect change by computing the brightness difference of the two date imagery. As some classes are quite different in brightness, such as buildings and farmland, this variable is suitable to extract those change regions.

11 12 2 12 2 1 2 22 2 2 2 2 2 2 11 1 2 1 2 1 2 2 2 4 2 2 == ( + ) ( + )[( ) +( ) ] ( ) +( ) BV BV BV BV RSIM BV BV BV BV           (1) Where 12 1 1 2 2 =1 1 = ( - )( - ) -1 B ii i BV BV BV BV B   (2)
21 =1 =1 11 _ = - BB ii ii Diff Brightness BV BV BB  (3)
Multi-band difference images (MDIs) are the differences of object mean spectral value in each band for the two date imagery, and multi-band ratio difference images (MRDIs) are the ratio differences of object mean spectral value in each band for the two date imagery.

A total of 20 single variables were tested with the nearest neighbor change classification for the two study sites. Each variable group (i.e., MDIs, MRDIs) was used as the inputs for multiple variables. The another group of multiple variables that consisted of one variable from the BDIs and MDIs, each showing the best performance in the experiments with the single variables, and RSIM were selected and tested, namely RSIMDR. In order to demonstrate BDIs provide change information that can be used as ancillary data to facilitate change detection with RSIM, a combination of RSIM and BDIs (RSIMBD) was also selected and tested. Thus, a total of 8 groups of multiple variables were evaluated with the binary change detection method for two study sites.

Accuracy Assessment

An accuracy assessment was accomplished for the binary change classifications (i.e., change and no change) of Site A and Site B. In each case, the accuracy assessment included overall accuracy (OA), the Kappa coefficient, the user's accuracy (UA) and the producer's accuracy (PA). And test samples for each class were randomly collected within each study site.

3

Results and Discussion

Object-Oriented Binary Change Detection Using Single Variables

A variety of single variables associated with change information were customized for the binary change detection, including regional similarity (RSIM), brightness difference images (BDIs), multi-band difference images (MDIs), multi-band ratio difference images (MRDIs). The image objects used in generating those variables were created from the multi-resolution segmentation technique in eCognition, one of the widely used object-oriented image processing software. A scale parameter of 70, a spectral factor of 80%, a shape factor of 20%, which was subdivided into smoothness (6%) and compactness (14%), and equal weights for the all eight bands were used in the image segmentation process. While the RSIM and BDIs had only one variable each, the MDIs and MRDIs contained four individual variables each, using the four bands of the SPOT 5 imagery.

The overall accuracy and Kappa coefficients of each single variable for the two study sites were shown in Table 1(a). RSIM achieved the best overall accuracy and Kappa performance for both two study sites, while the band 1(i.e., band NIR) difference resulted in the worst overall accuracy and Kappa performance for both two study sites. In addition, the single variable of the same band from the MRDIs was better than this from the MDIs.

The nearest neighbor change classification using the band 4 difference variable produced the highest overall accuracy and Kappa coefficient among the single variables from the MDIs for Site A. The band 3 ratio difference variable yielded the best performance (OA=85.19%, Kappa=0.707) among the single variables from the MRDIs for Site A. The single variables from the MDIs resulted in larger variation in the OA and Kappa than those from MRDIs (  OA=14.83% vs. 7.41%,  Kappa=0.295 vs. 0.147) for Site A. Dissimilarly to Site A, the band 3 difference variable from the MDIs, the band 2 and band 3 ratio difference variable from the MRDIs produced the same good performance (OA=76.92%, Kappa=0.538) for the Site B. However, the band 1 difference variable yielded the worst change detection result (Kappa=0.080) among all single variables for the Site B. In addition, the single variables from the MDIs resulted in larger variation in the OA and Kappa than those from MRDIs (  OA=23.07% vs. 11.54%,  Kappa=0.458 vs. 0.230) for Site B.

Object-Oriented Binary Change Detection Using Multiple Variables

Based on the change detection results using the multiple variables for the two study sites, as shown in Table 1(b), RSIMBD multiple variable group was slightly better or equal to that based on the RSIMDR group, which produced the highest classification accuracies. However, the change detection results using MDIs, MRDIs multiple variables resulted OA of 74.07% and 77.78% for Site A, which were even lower than the accuracy achieved by the single band 4 differencing and band 3 ratio differencing. Similarly to Site A, the MDIs, MRDIs multiple variables also did not produced higher accuracies than those yielded using single band 3 differencing and band 3 ratio differencing. Therefore, the multiple variables may not always do good performance.

On the other hand, as each variable had its own characteristics, representing different capabilities for distinguishing land-use/cover types, a suitable combination may improve the final accuracy of the change classification. For example, the BDIs single variable which only detected the change objects existing large brightness differences, resulted a bad accuracy, however, it facilitated change detection with RSIM. The OA of RSIMBD (RSIM+BDIs) multiple variables increased 11.14% and 3.84% compared with RSIM for Site A and Site B, respectively. As the performance of change detection results should not only be based on the indicators of OA and Kappa, but also consider other accuracy statistics such as user's and producer's accuracies. Thus, the PA and UA comparison using the single and multiple variables for the two study sites were shown in Fig. 3. Although the OA and Kappa values of single variables were lower than the accuracy using RSIMBD multiple variable group, it was found that some single variables were very suitable to identify certain change class. For example, the band 4 difference was good at identifying the change class and had small errors of omission as the producer's accuracy reached to 0.929 for Site A, while the band 3 difference was suitable to identifying the change class as the producer's accuracy reached to 0.923 for Site B. In addition, the band 3 difference, band 2 ratio difference and band 3 ratio difference all produced a higher producer's accuracy in extracting no change class than RSIM for Site A, and band 3 difference also yielded a higher producer's accuracy in extracting no change class than RSIM for Site B. Thus, the RSIMDR multiple variable group was good at identifying both change and no change class.

Conclusion

Binary change detection methods have many advantages and disadvantages. Generally, the binary change detection method is a threshold-based calibration process, which the optimum threshold was found through trial and error. However, such an approach is time-consuming and highly subjective. The research successfully employed the binary change detection method with nearest neighbor classification technique, removing the threshold problem existing in the traditional binary change detection method. Additionally, a variety of single and multiple variables were explored to extract change information for two study site. The results showed that not all multiple variables produced better performance than the single variables. However, a combination of the best performance in the experiments with the single variables, or those achieved good performance in a certain class, usually produces significantly better accuracy than others. The RSIM single variable yielded the best performance in the nearest neighbor change classification when using single variables. What's more, the combinations of RSIM and other single variables from MDIs and MRDIs, or BDIs, produced the highest overall accuracy and Kappa value than any other multiple variables. The fact that the RSIM combined the correlation, brightness ratio and contrast of all eight bands simultaneously explained the superiority.

The drawback of the proposed method concerned only spectral features of high spatial resolution SPOT 5 imagery, so objects of the same class with different spectral value is misidentified changed class easily. Future works could focus on developing other useful texture or structure features to binary change detection method and applying the method with different types of remote sensor data, such as QuickBird, WorldView.

Fig. 1 .

 1 Fig. 1. (a)-(d) Bi-temporal SPOT 5 imagery for two study sites (A, B), respectively.

Fig. 2 .

 2 Fig. 2. Flow diagram of the binary change detection method.

and 12  1  , 2  1 BV , 2 BV

 121212 is the covariance between object mean spectral values found in all bands of the two date imagery, are the standard deviation of the object mean spectral values found in all bands each date imagery, respectively. While1 iBV is the i th object mean spectral value of image 1, 2 i BV is the i th object mean spectral value of image 2, B is the total number of the bands for each image, and are the means of object mean spectral values found in all bands of the two date imagery, respectively.

Fig. 3 .Fig. 4 .

 34 Fig.3. PA and UA comparison using the single and multiple variables for Site A, Site B. Comparison of binary change detection for the best and the worst performance using the single and multiple variables for each site were shown in Fig.4. The band 1 difference variable misidentified many unchanged buildings as change objects in the

Table 1 .

 1 OA and Kappa using the single and multiple variables for Site A, Site B.

	(a) Single variables
	Site	Single varible	OA (%)	Kappa
	Site A	RSIM	85.16	0.702
		Diff_Brightness	74.07	0.485
		Diff_Band(1)	66.67	0.331

Acknowledgments. The research was funded by the Pre-Development of the High Resolution Application Demonstration System on Land Resource "Research on the Land Use Type Quickly Extraction with High Resolution Data"(E0202/1112/0104), and "Potential Study on Land Resource Management Applications of Archived Remote Sensing Data" (201109109188). The authors would like to thank the anonymous reviewers for their constructive comments and suggestions.


Tang Zhongshi

Ou Wenhao

Dai Yue

Xin Yu

Extraction Of Water Body Based On LandSat TM5 Imagery -A case Study in The Yangtze River

Keywords: Extraction of Water Body, Imagery Processing, Binarization

A improved method named vegetation normalized difference water index (VNDWI) has been proposed in this paper based on NEW of Xiao Yun-fang(2010), which uses Band 1 and Band 7 and the normalized difference vegetation index (NDVI) in TM5 imagery to construct the VNDWI. This method has been tested in the Yangtze River Basin, and compares the results with other methods is the best. Due to the reflection characteristics differences between water and cloud, NEW method cannot distinguish the differences effectively. However, joins the NDVI parameter, VNDWI can enhance the difference between water and cloud, so it can remove clouds and its shadow noise from water body information without using complicated procedures, which is particular difficult work should be done in pretreatment.

Introduction

The water body research is inseparable from the water scope and accurate extraction from the boundaries, it is particularly important to the coastline change monitoring, flood disaster monitoring, and disaster evaluation. In macro level, remote sensing monitoring of large scope area is having incomparable advantage. This paper extraction research based on the TM5 image of Yangtze River basin, and put forward the improved water extraction method.

Based on the research of water extraction from TM5 imagery, domestic and foreign scholars have made a lot of research, puts forward a variety of algorithm. In the foreign scholars' researches, 1990 Moller Jensen used the TM4 and TM5 * Corresponding author; E-mail: gis@tsinghua.edu.cn imageries and set threshold to extract water [START_REF] Jensen | Knowledge based classification of an urban area using texture and context information in Landsat TM imagery[END_REF], 1996 Mc Feeter using normalized water index NDWI = (TM2-TM4) / (TM2 + TM4) by inhibiting vegetation and soil information to extract water [START_REF] Feeters | The use of the Normalized Difference Water Index (NDWI) in the delineation of open water feature[END_REF]. And domestic scholars Xu HanQiu proposed improved normalization water index MNDWI = (TM2-TM5) / (TM2 + TM5) in 2005, and set certain threshold to extract water [START_REF] Han-Qiu | A Study on Information Extraction of Water Body with the Modified Normalized Difference Water Index[END_REF]. In 2004 Chen HuaFang, put forward the method: (TM2 + TM3)-(T4-TM5) > k, which can be well separated by mountain shadow and water [START_REF] Cun-Jian | Remote sensing information mechanism of water extraction method[END_REF]. Xiao YanFang using the Band 1 and Band 7 in TM5 imagery for water information extraction [START_REF] Yan-Fang | A study on information extraction of water body using band1 and band7 of TM imagery[END_REF], this method not only can extract water body like NDWI and MNDWI, but also has advantage in urban areas than other methods.

The above method has been widely used in practical work, but there are also have some disadvantages, such as the extraction of water information may contain cloud, vegetation and residents influences which may cause the inaccurate results, Salt and Pepper phenomenon as well. YanFang's research indicates that band 1 and band 7 of TM5 imagery makes the minimum difference between water and buildings, and their sum is far higher than water, so it can be used to eliminate buildings and roads influence to the water body extraction.

MATERIALS AND METHODS

But in actual study operation, the study area in the Yangtze River basin for this paper mentioned, buildings information may less than others, but cloud and its shadow are the main factors which may affect the accuracy of water extraction. For the cloud reflection characteristics of the bands in TM imageries, a statistic result table can be listed: From the table 1 we can see the largest difference of water and cloud is the NDVI value, so the proper way to distinguish between water and cloud is from NDVI value. This paper improves the NEW algorithm, join NDVI parameters, which named VNDWI here, the formula is:

VNDWI = (TM1-TM7) / (TM1 + TM7) > k * NDVI (Formula 1)
The calculate results from formula 1 eliminates the influence of cloud and make the water body extraction more precisely, and buildings and subsequent shadow elimination is well preserved. Statistical analysis showed that the new method in this paper can extract the water body like NDWI, MNDWI and NEW, it also indicates more accurate extraction in the Yangtze River basin water information from filter out the part of the clouds and the clouds' shadow influences.

RESULTS AND DISCUSSION

CONCLUSION

Use Band 1 and Band 7 from Landsat TM5 imagery to build up the water index NEW, can be used for rapid, simple and accurate extraction a wide range of water body information, like the NDWI, MNDWI methods, can extract the vegetation water, also it can be better than NDWI and MNDWI more accurate extraction in the city area;

But the NEW algorithm is not pointed to consider clouds influences in water extraction. However, this paper put forward a improved method called VNDWI algorithm, to some extent, it filter out the cloud influence from water body, and also keep its buildings and mountain shadows filtering characteristics, greatly improving the water body information extraction accuracy.

Fig. 1 2

 12 Fig. 1 Yangtze River Basin Fig. 2 Part of Yangtze River

FromFig. 3

 3 Fig.3The contrast results between NEW and VNDWI

Table 1 .

 1 The Average Value of Water and Cloud

	Band Band2 Band3 Band4 Band5 Band7 NDVI NDWI MNDWI NEW
	Water 106.5	56.9	35.9 25.1 14.6	9.6	-0.391 0.346	0.986	0.997
	Cloud 235.9	124.5 142.1 105.9 162.6	89.8	-0.0366 0.0429 0.753	0.909

Acknowledgements

The research is funded by the "Eleventh Five-Year" national satellite application of high technologies in China. All authors would like to appreciate many experts from Office of the State Council Three Gorges Project Construction Committee, Beihang University and Chongqing Normal University.


Junhua Liu

Chengming Zhang

Shujing Wan

The Classification Method of Multi-spectral Remote Sensing Images Based on Self-adaptive Minimum Distance Adjustment

The phenomenon of "Same Object with Different Spectra" in the issue of multi-spectral remote sensing images land use classification makes major effects on improving accuracy. The paper based on the analysis of modeling on classification problems, proposed a method based on minimum distance self-adaptive adjustment to realize the split of cluster centers and solved the problem of identified scope intersection leading to improving the accuracy in the classifying methods difficultly. By experiments compared with the traditional methods, it can improve classification accuracy about 4% and the results prove the validity of this method.

Introduction

Multi-spectral remote sensing images are the main data source in the land use applications.

Studying the appropriate classification algorithm according to the fundamental characteristics of the multi-spectral remote sensing images is the primary means to obtain high-precision land-use information.

In the 1980s, it was mainly used statistical pattern recognition methods to classify remote sensing images by computer [START_REF] Luo | The Research of Fuzzy Pattern Recognition and Decision Tree Methods Application in Remote Sensing Image Classification[END_REF] . Since the 1990s, it emerged a large number of remote sensing image classification methods, such as the artificial intelligence classification, combination of remote sensing and GIS, object-oriented classification, composite classification and so on which achieved better results [START_REF] Shi Zepeng | Review on the Classification Methods of Land Use/Cover Based on Remote Sensing Image[END_REF] . Dixon et al. took the support vector machine method to do land use classification of TM images that obtained the best classification accuracy [3] .

Wardlow et al. used the decision tree method to classify crop in the central Plains of United

States, which gained the overall classification accuracy of better than 80% [START_REF] Wardlow | Remote Sensing of Environment[END_REF] . Taochao et al. [START_REF] Tao Chao | A Probabilistic Latent Semantic Analysis Based Classification for High Resolution Remotely Sensed Imagery [J[END_REF] came up with a high-resolution remote sensing image classification method based on probabilistic platen semantic model for the phenomenon of "same spectrum with different objects" in high-resolution remote sensing image. Li Gang proposed an uncertainty classification technology based on high-dimensional cloud model and RBF neural network modified [START_REF] Li Gang | Uncertainty classification method of remote sensing image based on high-dimensional cloud model and RBF neural network[END_REF] .

It wasn't satisfied with the traditional remote sensing image classification methods in the aspects of automation, intelligent, and classification accuracy [START_REF] Qian | The Comparative Study of Remote Sensing Images Classification Methods[END_REF] ; although the technology based on neural network has the advantages of strong fault tolerance and adaptability, it existed structure selected difficult, too fast of local convergence and study process difficulty controlling in the practical application, which resulted that was not wholly better than the traditional technologies; the method based on decision tree was not ideal in the treatment of the boundary [START_REF] Jia Kun | A Review of Classification Methods of Remote Sensing Imagery[END_REF][START_REF] Guo Yaqin | Minimum distance classifier ensemble based on adaptive distance metric[END_REF] .

For the fundamental characteristics of multi-spectral remote sensing images, with comprehensive considering the speed and accuracy of land use classification, the paper proposed a classification method of introducing self-adaptive minimum distance adjustment in order to better meet the needs of the actual production.

Self-adaptive Minimum Distance Adjustment Classification Method Principle

The existing remote sensing classification methods research more on how to improve the formation of the cluster center, but in judgment the attribution problem of the pixel to be classified it usually adopts the way that calculation the distance of pixel to be classified to the cluster center first, then taking it into the category with minimum distance, which results in the difficult to improve the classification accuracy.

Remote Sensing Image Classification Problem Modeling

Under the ideal conditions, the eigenvector of pixels representing similar surface features in remote sensing images will distribute in the same feature space regions; however, as different surface features with different eigenvectors, they should distribute in different surface features, which means that any kind of feature in images has only one gray value scope in any band for a remote sensing image to be classifies with b bands and for some feature c, it can obtain the value set of the pixels in every band, as Figure 1 shown: To the band i of class c, if use li as the lower bound of the gray value and hi as the upper, in the band i, the value of its center mi is:

2

) ( i i i l h m  
and based on it, it can define m=(m1,m2,…,mb)T as the center feature vector of the class which can describe the adaptive changes of the cluster center better. Use ri as the permissible error radius surrounded, by the analysis above, the value of ri is |hi-li|/2 and it can name all the vectors made by maximum permissible error of all the bands as effective radius vector. For pixel x to be classified in class c, compare the distance of gray value of each band to the surrounding component of the center feature vector, and if all the distances don't exceed the surrounding permissible radius, take it into class c.

2.2

The Processing Method of "Same Object with Different Spectra" Problem

The "Same Object with Different Spectra" phenomenon for a variety of reasons makes the error value in some bands or all bands too large, which lead to the feature vector of the class distributing too scattering in the feature space and difficult to attribute to same cluster as excepted. To better illustrate this problem, now explain it with an example of a two-band image classification, assuming that the image will be divided into two classes A, B and the pixels distribution of each class are shown in Figure 2. From the analysis above, we can see that the root cause of classification accuracy reducing is too large of cluster centers and identified radius leading to the identified scope of two cluster centers intersect. For this, it can split cluster centers by adaptive minimum distance adjustment to eliminate intersection of identification range which can improve the classification accuracy effectively.

3

The Method Realization of Self-adaptive Minimum Distance Adjustment Classification

The Process of Self-adaptive Minimum Distance Adjustment

Based on the second analysis, it gives the method of self-adaptive minimum distance adjustment: doing the intersecting judgment to the cluster centers after the end of the sample study. If there are the intersecting parts generated on the basis of the two cluster centers' identification radius, it needs to split the cluster centers. The split steps are:

(1)Calculate the cluster centers generated one to another to determine whether they intersect. If intersect, process according to (2).

(2)Split the bigger one of identified scope in the two cluster centers. Select any sample from the classes to be split, choose a value of less than the biggest identified distance as threshold randomly, divide the samples into different subsets, study separately for each resulting in each cluster center and estimate with the classes not split. If there are still intersecting with the cluster center of some subset, select a less threshold and split again.

Algorithm Design

The algorithm uses K-Means algorithm to split the cluster center into two subsets each time and do sample set division in the way of binary tree. Each class generates a binary tree named as the subset tree of the class. A node of binary tree is corresponding to a sphere. Record each radius of sphere centre and the surrounding sample subset. The center of the sphere is defined as the center of the sample subset in this node and the radius is the maximum of Euclidean distance from the sample point on that node to the center of the sphere. The sphere obtained by the subdivision of a node's subset is said in two child nodes of it.

The classification steps of adaptive minimum distance classifier towards points to be classified i are shown in Figure 3, the specific steps: (2) Give the points to be classified to the class of a subset tree with minimum distance to D.

With using binary tree search in the distance calculation, there are a part of nodes joined in finding out the distance D to reduce the number of joining in the real calculation which guarantees that calculated amount will not increase much under the condition of sample number or sphere subdivided increasing.

Distance calculation using the only part of the nodes participate in the strike of the distance D, can effectively reduce the actual participation of computing d, the number does not guarantee the number of samples increases, or broken down to get the sphere increased computation does not increase.

The Experimental Results and Analysis of Different Classification Methods in XueYe Basin

The data sources used in the experiment are ETM+ images OF 30m with a total of eight bands and the size of 1500 pixels × 1200 pixels acquired in May 31, 2007. From the classification results of figure 4, it can be seen that with using the minimum distance method, the number of pixels to be classified increased significantly, the phenomenon of mixed classification with forest is more serious and the misclassification of the water affected by shadow was existence. K-nearest neighbor method can distinguish water and bare soil well, but recognize forest not well with classifying a part as water. Although the maximum likelihood classification method can classify water well, it exits serious mistake phenomenon of classifying the forest and bare soil into the kind to be classified. K-nearest neighbor method can distinguish water and bare soil well, but it cannot recognize forest commendably with classifying a part to water. BP neural network has better effects to classify each kind, but a part of vegetation still not distinguishes. The classification accuracy is shown in Table 1. Contrasting the classification results of different methods, it showed that using the method of paper to classify, the pixel in the results obviously reduced and the accuracy increase 1.06% in average.

Fig. 1 .

 1 Fig. 1. The schematic diagram of spectral range under the ideal conditions

Fig. 2 .

 2 Fig. 2. The sketch map of "Same Object with Different Spectra" resulting in classification error In Figure 2, for the pixel of class B distributing in two regions, the distance of cluster centers generated with two regions is too large leading to the range identified (blue circles) too large. As a more extreme case, the area of another class A is surrounded in the scope identified by class B and the part surrounded by an orange rectangle should be divided into pixels of class A but to class B.

Fig. 3 .

 3 Fig. 3. The flowchart of self-adaptive minimum distance classification algorithm (1) Find out the distance D of the point to subset tree corresponded by each class. The distance D(T,P) of unclassified point P to the subset tree is defined as: A. If the centre of sphere Euclidean distance d corresponds to the root node of P to T is greater than twice sphere radius of the node, ignore all globules subdivided by the node and let D = d; B. If the root of T is already the leaf node, let D=d; C. it is not satisfied with A and B, definite recursively as the less distance D1 and D2 of P to the left and right sub-tree T1 and T2 of T.

  Xueye basin of Laiwu City, Shandong Province as the study area. It selected three different kinds land use categories of water, forest and bare soil to classify and compare.

Fig. 4 .

 4 Fig. 4. The classification results of XueYe Reservoir

4. 1 . 2

 12 The Process and Result of Self-adaptive Minimum Distance Classification While using the self-adaptive minimum distance classification, the experimental results are shown in the Figure 5.

Fig. 5 .

 5 Fig. 5. The extraction results of XueYe reservoir The classification accuracy of different training samples and category schema is shown in

Table 1 .

 1 Classification accuracy of common ways

	Classification	water forest	Bare soil	Average
	Maximum likelihood	85.6	84.1	86.5	85.4
	Minimum distance	87.7	86.8	85.3	86.5

Table 2 .Table 2 .

 22 The classification accuracy of different training samples and category schema

	Water	Forest	Bare soil	Average
	91.3	92.8	89.2	91.1

Acknowledgments. This study has been Funded by (1) Basic Research Fund of Chinese Academy of Surveying and Mapping under Grant (Contract Number: 7771109), (2) The Research Award Fund for Outstanding Young and Mid-career Scientists in Shandong (Contract Number: BS2011DX031), (3) Technology and Evolving Countries Program in Shandong (2012GGB01093), (4) Basic Research Fund of Chinese Academy of Surveying and Mapping under Grant (Contract Number: 7771211). Among them, (1) and (4) are the basic business founding of CASM.


Jian Cao
email: c_jcaojian@163.com

Yubin Wang

Analysis on Agricultural E-Commerce Platform Construction in Developed Areas Based on Rural Residents' Needs--Take the Case of Beijing 

Keywords:

This paper takes Beijing as an instance, launches investigation and survey on 152 rural residents, and analyzes the real demands of rural residents in the progress of e-commerce implementation in developed areas to provide government with support for the further construction in the relevant aspect. 71.71% of rural residents have ever heard about this concept, but among all the interviewees, 24.34% of them don't know what e-commerce is. There are 61.84% of rural residents who once conducted online shopping, but there are only 32.89% of rural residents having tried to sell or market their own agricultural products on the internet. Econometric model is used to find out the factors influencing rural residents to choose E-commerce. Rural residents with younger age are more willing to launch agricultural business through e-business, rural residents with higher annual income are more willing to attempt; and the ones with experiences of e-commerce online shopping are more willing to accept e-commerce of agricultural products. These suggestions are to provide with more trial opportunities and to provide rural residents with more training opportunities.

Agriculture is based on production, which is determined by two important factors: market and circulation. Information technology, network technology and e-commerce, such a new business pattern have injected new vitality and vigor into the circulation of agricultural products in China. The agricultural e-commerce has inspired the government and the society to promote the agriculture modernization in China to realize the great-leap-forward development of agriculture.

The agriculture modernization was put on the agenda since the 3rd Plenary Session of the 17th CPC. The informationized agriculture was increasingly valued, which brings a direction to the agriculture e-commerce. Since the development of ecommerce in the late 20th century in China, agricultural e-commerce is no longer a new buzz word for Chinese, but its development has been limited greatly due to the immature conditions in all aspects. At present, the agriculture e-commerce is mainly headed by the government and the agricultural enterprises, while the individuals rarely go further under the present agricultural economic structure and confined by the capital condition. The government just advocates to promote the agriculture modernization by the agriculture industrialization and to optimize the agriculture industrialization by the agriculture informationization.

The agriculture informationization development in China achieved a lot during the past years. Yet, it is rarely researched on the topic what the proper information technology and e-commerce platform that the rural residents---the main users are in great need. In this article, the authors, taking 152 rural residents in Beijing as objects, analyze the real need of the rural residents in the developed rural areas in China, in order to provide a strong support for the further step related on such an issue.

1

The Development Level of Agricultural Informationization and E-commerce both in Beijing and China

Level of Agricultural Informationization in Beijing and China

The development of agricultural e-commerce relies on agricultural informationization. As a weak industry, agriculture lags behind industry in terms of the development degree of informationization. According to a report by CNNIC in January 2012, the number of the netizens in China has exceeded 50 million, reaching 51.3 million, and the Internet penetration has arrived at 38.3% by the end of 2011. The rural netizen number in China is about 13.6 million, which is approximately equal to one third of that of the urban on the corresponding period of last year, and the Internet penetration is about equal to one sixth. Compared with the year of 2010, the proportion of rural netizens decreased by 0.8%, its growth rate was still less than that of urban netizens. The number of the netizen who make a living on agriculture, such as farming, forestry, animal husbandry and fishery, takes about 4.0% of that of the total netizen, which means most of the rural residents in China don't surf online. The information services provided by agricultural websites can reach rural residents only if with the help of other information facilities or channels. The issue of information landing has become one of the bottlenecks restricting rural citizens from using information [START_REF] Tianshi | Study on E-commerce Business Model for Chinese Agricultural Products[END_REF] . Beijing ranks top among other areas in China in terms of netizen popularity, its ratio is up to 70.3%, doubling that of the national level. This means that the internet surfing level of rural citizens of Beijing also ranks top nationwide.

The proportion of administrative villages with internet access has been enhanced to 91.5% (CNNIC, 2010). At present, the target of "four ones", namely "equip each village with one information service station, equip each town with one information service point, build one website for each village and launch one online program of agricultural and sideline products for each town", has been realized principally in 15 provinces.

1.2

The Current E-Commerce Situation on Agricultural Products According to the analysis of Hu, there are seven business models of e-commerce of agricultural products in China, which can be sorted into three types: the first type are the primary ones, including catalog model, information model, medium model and virtual community model; the second type are the advanced models, including e-shop model, e-procurement model and value-chain integration model; the later type of models is the third party trading market model suitable for China's national conditions at the present stage [START_REF] Yanyan | Study on the Countermeasures of Developing Electronic Business of Agricultural Produce--A Case Study of Vegetables in Shouguang[END_REF] .

Currently, the application of the e-commerce of agricultural products can be classified into types, the first is service provision of supply and demand information hosted by government, represented by http://www.agri.gov.cn/ (Chinese agriculture information network hosted by China's Ministry of Agriculture), and other agriculture related websites organized by governments at all levels. Websites hosted by governments also include the styles of B2B and B2C, such as http://12582.10086.cn/ (Nongxintong), http://www.118326.com/index.html (Info Village) and http://nxw.ln.10109555.com/web/index.asp (Jinnongtong) hosted by three basic telecommunications enterprises, and other basic comprehensive information platforms that have already covered the whole country. The second type is business service provision hosted by various economic entities. These websites mainly engage in providing with the service of commercial electronicalization of business links related to the production, supply and sales of agricultural products. Their main customers are enterprises with a certain scale. B2B and B2C are the major two forms being applied [START_REF] Tianshi | Study on E-commerce Business Model for Chinese Agricultural Products[END_REF] .

From the aspect of e-commerce application, there are a futures market for wheat, cotton and bean, etc, and a spot market online dealing with agricultural products. it has enlarged the online product category that includes poultry, vegetables, local specialty, flowers, fruits, aquatic products, tea and so on, instead of the former products mainly concerning on grain, oil and fertilizer.

However, from a research by CNNIC, the first five online applications of the rural netizens are online music, online news, online game, searching engine and instant messaging. The application of online games tops among the five and only 17.6% rural netizens has experienced online shopping. Therefore, it can be concluded that few rural netizens take a good advantage of the current agricultural e-commerce platform in China.

2

The Recognition of the Rural Citizens on E-Commerce in Beijing

The Information Facilities in Rural Areas

The survey shows 88.16% of the rural residents have their own computer and 80.92% can surf online. That means 9.7% of the rural residents have not possessed the skills to surf online. Among those who surf online, 74.34% prefer to be online at home. This indicates the basic network building in Beijing is excellent, which will play a good role in promoting e-commerce.

Viewing from the internet access, at present, there are three ways including DUN (dial-up networking), ADSL and broadband. 79.53% of rural residents can log on the internet through broadband, showing that the internet speed is guaranteed to lay a foundation for the e-business.

With the popularity of internet, the channels for famers to get information have undergone great changes; internet has become the major information source of rural residents, and there are 44.73% of rural residents choosing internet to gain information. But through our survey, we find that only 30 interviewees (accounted for 19.74% among all the interviewees) often surf on the relevant agriculture websites to gain information, showing that at present rural residents still haven't formed the habit of gaining information from professional agriculture websites, that is to say the first primary phase of e-commerce of agricultural products hasn't been universal among rural residents currently.

The Recognition of the Rural Residents on E-Commerce

The concept of "e-commerce" has been advocated in recent years that 71.71% rural residents have heard as the survey shows. However, there are different understandings. Among all the interviewees, 24.34% of them don't know what e-commerce is, 21.71% of them think e-commerce just means issuing relevant sales information on the internet, only 26.31% of them believe that e-commerce indicates "sales on the internet". As to the question what will benefit the agricultural products by e-commerce, 46.05% rural residents believe they can learn more information and 28.95% think ecommerce will help them sell their products with good price.

Although rural residents can't figure out what is e-business, some of them have started practicing. There are 61.84% of rural residents who once conducted online shopping, but there are only 32.89% of rural residents having tried to sell or market their own agricultural products on the internet. Meanwhile, the passion among rural residents to sell through the model of e-commerce is not relatively high, accounting for less than 40% among all the rural residents.

The Factors to the Rural Residents to Do E-Commerce

Through this survey, we have found that rural residents' awareness of e-commerce is still vague at present. As a result, scene description to rural residents is needed before the research on the prerequisites influencing on rural residents when they are choosing the operation model of e-commerce. First, investigators explain the basic concept of e-commerce to rural residents in straightaway language, then describe to them the various advantages of e-commerce comparing to the traditional sales mode. After the situation description, conduct the investigation about whether rural residents are willing to use agricultural e-commerce platform as the main operation form. In this survey, there are three options to the question of "Are you willing to sell your agricultural products or to get various commercial opportunities through ebusiness?", "Yes", "No" and "I don't care". In the data processing, "Yes" is defined as "1," "No" and "I don't care " are defined as "0". Therefore, the explained variable in researching on the e-commerce demand of rural residents is (0, 1); this paper chooses Logistic model for regression analysis.

When y takes a certain value (such as y = 1), the relation between the probability of occurrence (p) and some factors (x) is as following:

P (y=1/x), ie. p=f(x) (1)
P (probability) is valued between 0~1.

The Logistics model is used with a grouped data to predict the relation between a number of x and a dependent variable with different values, thus, showing the reliable relationship.

The General Equation is as followed:

(2)

Sample Selection

Obviously, e-commerce is based on internet access, therefore, when discussing whether rural residents will choose e-commerce or not, we only select a subsample with 121 interviewees who have mastered the skills of surfing on the internet from the sample with152 interviewees.

Variable Selection

In general, the individual character, the related facilities and so on are what will be taken into consideration of the action of the rural residents. Therefore, we will focus on the following four factors in this article: a. The individual character of the rural residents: Age, education, job and income of very month are taken as the dependent variables.

b. The situation of information infrastructure in rural areas: The distance to the county and the way to surf online as the dependent variables represent the situation of information infrastructure in rural areas.

c. The internet usage of the rural residents: Three options "Occasional", "often" and "every day" are taken to represent the online frequency.

d. The recognition on e-commerce of the rural residents: The question "have you ever shopped online" is indicated whether they know e-commerce, as it is hard for them to tell the concept of e-commerce.

3.3

Presumption to Be Testified a. The one who is younger and with higher education will be more likely to do ecommerce. b. The one who has the e-commerce experience will be more likely to do ecommerce with the agricultural products. c. The development of the internet will be a factor to affect e-commerce. d. The one with a higher frequency will be more likely to do e-commerce.

Assessment and Results

We use EVIEWS7.0 to assess the model in this article. We find there is a great relation between the age and the education. Therefore, we eliminate the variable of education to make an assessment. The relative result is as following:

Conclusion and Further Discussion

In developed areas (eg. Beijing), all the villages and towns have built up their own websites principally, the degree of convenience for rural residents to connect to the internet has been increased greatly; furthermore, most peasant households have had their own computers, mainly realizing the target of surging on the internet at home for rural residents. Under such background, the personal characteristic of a farmer, such as the age and income status, is the dominant factor influencing one's decision to launch online business of agricultural products or not. Meanwhile, it is more acceptable for newly emerging things after some trying experience; thus appropriate increase of trial opportunities can be beneficial. Through analysis, we can find that the internet speed doesn't significantly influence the application of e-commerce of agricultural products; therefore, hardware is not the main restrictive factor.

Based on the analysis above, we would like to propose some suggestions to the construction of e-commerce of agricultural products in China:

1. More attempts to do e-commerce with free charge should be provided by the platform.

With the attempts to do e-commerce, the rural residents will be encouraged and inspired, which will make a solid foundation for further development of the ecommerce on agricultural products.

2. More training should be provided to the rural residents.

In the survey, 51.25% of the rural residents think what they need most is the training on e-commerce. It is the greatest investment in the aspect of the software after the platform as the hardware is finished.

Table 2

 2 Basic Information of Variables

	Variable Name	Variable Abbreviation	Variable Explanation	Variable Statistics
	Dependent Variable	Y		Willing to use d-business
	Age	AGE	Physical Age	33.44
	Education	EDU	Represented by number of years one receiving education	12.49
			Rural residents engaging in
			breeding and cultivation are set as
	Industry	IND	1, rural residents engaging in other tertiary industries are set as 0 ( leisure and sightseeing	28.10% are engaging in primary industry
			industry is also regarded as
			tertiary industry)

Table 3 :

 3 The result of the assessment Viewing from the estimation result, below the significance level of 10%, there are three options including age, annual household income and the past experience of online shopping that show significance. That is to say rural residents with younger age are more willing to launch agricultural business through e-business, rural residents with higher annual income are more willing to attempt; and rural residents with experiences of e-commerce online shopping are more willing to accept e-commerce of agricultural products.Variables including internet connection mode and online frequency indicate insignificance.

	Variable	Coefficient	Std. Error	z-Statistic	Prob.
	AGE	-0.052831	0.017224	-3.067221	0.0022
	IND	0.400549	0.489629	0.818066	0.4133
	INC	2.49E-05	1.40E-05	1.782153	0.0747
	DIS	-0.005285	0.007459	-0.708533	0.4786
	MODE1	0.424002	0.964057	0.439810	0.6601
	MODE2	-0.567187	0.596467	-0.950911	0.3416
	FRE1	0.460137	0.501401	0.917703	0.3588
	FRE2	0.061557	0.611150	0.100724	0.9198
	BUY	1.194978	0.447902	2.667942	0.0076

The 3 rd industry mainly refers to the leisure travel and service industry.


Xiaofei An

Minzan Li

Lihua Zheng

Yumeng Liu

Yajing Zhang

Real time detection of soil moisture in winter jujube orchard based on NIR spectroscopy

Keywords: Soil moisture, Real time detection, Soil sensor, Near infrared reflectance spectroscopy

The measurement and control of soil moisture are the key technologies of precision agriculture. In order to real-time detect soil moisture content faster and more accurately, a portable soil moisture sensor based on NIR spectroscopy was developed. With the sixty soil samples collected from a winter jujube orchard, a linear regression model was established. The determination coefficients of the calibration ( 2 c R ) and validation ( 2 v R ) reached 0.88 and 0.92, respectively. The model passed F-test and t-test and showed robust. Subsequently, two spatial distribution maps of soil moisture were generated based on the data obtained by the portable soil moisture detector and the data obtained by oven drying method, respectively. Finally, the correlation between these two maps was investigated by using the software of Surfer 8.0.

The zones of dry and wet soil could be distinguished easily in both maps. The results of the study showed that the developed detector was practical.

Introduction

Soil moisture content is one of the most critical soil components to plant growth and land management, especially in dryland [START_REF] Shang | Establishment of calibration equation for soil moisture content by NIRS[END_REF] . It is usually measured by the oven drying method with a quite high precision. On the other hand, the oven drying method is a difficult, costly and time consuming procedure, so that it is necessary to develop a real time soil moisture detector for the precision agriculture practice.

The near-infrared (NIR) reflectance spectroscopy is a promising non-contact measurement method available to provide rapid information about some soil physical and chemical properties, such as soil organic matter content, soil total nitrogen, potassium, phosphorous, pH, soil moisture and particle size et al. When understanding the interaction between the NIR incident light and the soil properties, those physical and chemical parameters can be estimated based on Chemometrics.

Many scholars such as Shibusawa, Christy, Xiao W [START_REF] Mouazen | Towards development of on-line soil moisture content sensor using a fiber-type NIR spectroscopy[END_REF][START_REF] Hummel | Soil moisture and organic matter prediction of surface and subsurface soils using an NIR soil sensor[END_REF][START_REF] Peng | Analysis of soil moisture organic matter and total nitrogen content in loess in china with near infrared spectroscopy[END_REF][START_REF] Christy | Real-time measurement of soil attributes using on-the-go near infrared reflectance spectroscopy[END_REF][START_REF] Gashaw | Development of a prototype infrared reflectance moisture meter for milled peat[END_REF][START_REF] Xiao | Adaptability of the model for soil moisture measurement based on near-infrared spectroscopy[END_REF][START_REF] Shibusawa | Site-specific crop response to temporal trend of soil variability determined by the real-time soil spectrophotometer[END_REF][START_REF] Liang | Study of sample temperature compensation in the measurement of soil moisture content[END_REF][START_REF] Liang | NIR Spectroscopy Based on DWT and LS-SVM for Prediction of Soil Moisture[END_REF][START_REF] Minasny | Removing the effect of soil moisture from NIR diffuse reflectance spectra for the prediction of soil organic carbon[END_REF][START_REF] Pan | Development of soil organic matter fast determination instrument based on spectroscopy[END_REF][START_REF] Li | Development of a portable SOM detector based on NIR diffuse reflection[END_REF][START_REF] Li | Spectral analysis and application[END_REF][START_REF] Chen | Twelve Kinds of Gridding Methods of Surfer 8.0 in Isoline Drawing[END_REF][START_REF] Li | Near-infrared spectral detection of soil moisture based on feature extraction of FFT[END_REF][START_REF] Liang | The Research on Sample Temperature Compensation for Soil Moisture Content Based on Multi-Wavelength and LS-SVM[END_REF][START_REF] Krapez | A combination of temperature, vegetation indexes and albedo, as obtained by airborne hyperspectral remote sensing, for the evaluation of soil moisture[END_REF][START_REF] Song | Research on the Method for Rapid Detection of Soil Moisture Content Using Spectral Data[END_REF] have researched on estimation of soil moisture based on NIR spectroscopy in recent years. Mouazen [START_REF] Mouazen | Towards development of on-line soil moisture content sensor using a fiber-type NIR spectroscopy[END_REF] established a soil moisture forecasting model with correlation coefficient of 0.982 in the laboratory. The model method was partial least squares regression (PLSR) with 306.5~1710.9 nm. But in the field measurement, the correlation coefficient was just 0.75 and the maximum error of measurement was above 2.5%. Hummel [START_REF] Hummel | Soil moisture and organic matter prediction of surface and subsurface soils using an NIR soil sensor[END_REF] analyzed relationship between soil moisture and near infrared spectroscopy, and a model was established by the multiple linear regression (MLR) method. Peng Y.K. [START_REF] Peng | Analysis of soil moisture organic matter and total nitrogen content in loess in china with near infrared spectroscopy[END_REF] adopted the NIR spectra (Neotec51A) to forecast soil moisture, organic matter and total nitrogen of Loess Plateau and also obtained several estimations models. Shibusawa et al. reported a development of a real-time NIR spectroscopy measurement system for soil moisture [START_REF] Shibusawa | Site-specific crop response to temporal trend of soil variability determined by the real-time soil spectrophotometer[END_REF] . The light illumination and reflection fibre were attached to a spectrophotometer with300 -1700 nm VIS and NIR light source.

Most of above researches established forecasting models in the laboratory conditions, and the real-time detection of soil moisture has not been realized with higher practical applicability. Thus, we decided to develop a portable and reliable soil moisture sensor for the real-time management. A comparison between thematic maps of soil moisture obtained by the developed detector and oven drying method respectively was conducted. And the field experiments were executed in a winter jujube orchard to test the performance of the detector.

Materials and methods

The winter jujube orchard was located at Beijing suburb, with the area of 1000 m 2 .

Cultivated variety was Zhanhua winter jujube planted as south-north row direction.

The range of experiment zone was E116.15268541~116.15269492 and N 40.030982 ~ 40.031201. The soil texture was typical loam in north China.

The detector was designed with an optical unit and a control unit. Fig. 1 shows the overall structure of the soil moisture detector, and Fig. 2 shows the prototype of the detector. The optical unit included a near infrared lamp-house at 1450 nm, a shared lamp-house drive circuit, a Y-type optical fiber shared by incidence and reflectance, a probe and an InGaAs photoelectric sensor. The control unit included an amplifier circuit, a filter circuit, an A/D circuit, a LCD display circuit and a storage circuit with U-disk. Light from near infrared lamp-house was adopted in the detector instead of the sunlight.

When the detector worked, it was pushed into soil under the surface of 30 cm firstly. Then the optical signal at single wavelength was transferred from the 1450 nm near-infrared lamp-house to the surface of the target soil via the incidence optical fiber. The reflected light from the soil surface was acquired and transferred to the InGaAs photoelectric sensor, where the optical signal was converted to the electrical signal. Subsequently, the obtained electrical signal was processed and stored temporarily by an 89S52 MCU. Finally, the calculated soil moisture content was displayed on a LCD and stored in a U-disk at the same time.

i i i V V r ( 1
)
) / 1 lg( i i r A  (2) 6 6 1 avg    n i i A A (3)

Results and discussion

Data preprocessing

Soil moisture contents of all soil samples were also measured by the oven drying method in the laboratory condition. Soil samples were subjected to oven drying at 60C for 24 hours, and soil moisture content was obtained through the Equation (4).

% 100 0 0 1    m m m w (4)
where:

w is soil moisture content, 1 m is original soil weight, 0
m is soil weight after oven drying.

Statistic characteristics of soil moisture contents in the winter jujube orchard are shown in Table 1. All soil samples were divided into two groups: calibration group (including 40 soil samples) and validation group (including 20 soil samples). In the calibration group, the selected soil moisture range of 3.15%~14.19% covered the most acquired plant and organic activities during the winter jujube season. In the validation group, the range was 4.54%~12.09%, and the average was 8.03% and the standard deviation was 2.09%.

Estimation modeling of soil moisture

The object of this task was to build a statistical model between the absorbance of 1450nm and soil moisture content. There were several statistical modeling techniques for the proper calibration performance, such as the linear and non-linear multiple regression analyses and the principal component analysis (PCA).

In this work, the forecasting model of soil moisture content was established by the linear regression method with the average absorbance spectral data. Fig. 5 shows the comparison between measured data and predicted data of soil moisture. The forecasting model is shown in Equation ( 5). The determination coefficient of the calibration ( 2 c R ) was 0.88, and the determination coefficient of the validation ( 2 v R ) was 0.92. The model passed the F-test and t-test, which illustrated that both the model and the parameters were significant.

Discussion

Use of infrared emitting diode of 1450 nm could make it possible to increase the frequency of sampling at a faster rate. It would also make the unit run on low power, compact and light, so that it would require little space and mechanical support.

Soil moisture content could be obtained by the soil moisture sensor. A linear regression model could satisfy the basic needs. The determination coefficient of the calibration ( 2 c R ) and validation ( 2 v R ) were 0.88 and 0.92 respectively. Root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP) and residual prediction deviation (RPD) were 0.94, 0.85 and 3.26 respectively. From the thematic maps of soil moisture, the zones of dry and wet soil could be distinguished easily. Both the results indicated that the sensor could In order to increase the accuracy of the NIR measurement, other influencing parameters such as soil texture and density should be taken into account. Both the texture and density affected the results seriously. Extending the sensor application for all soil textures requires incorporating extra laboratory measurements of spectra taken from soil samples of another soil texture.

Conclusions

Soil moisture content is one of the most critical soil components to plant growth and land management, especially in dryland. In this paper, a rapid method to measure soil moisture was proposed. According to the result and discussion, the following conclusions could be obtained.

(1) A portable soil moisture detector was developed based on NIR spectroscopy. In the detector, LED of 1450 nm, Y-type fiber and circuits were adopted to insure the sensor stable and reliable enough. It also made the unit run on low power, compact and light so that it would require little space and mechanical support.

(2) The field experiment showed that the instrument could work stably. The established model showed that there were high correlation coefficient between average absorbance of 1450 nm and soil moisture content, with 2 c R and 2 v R of 0.88, 0.92 respectively. Similar spatial patterns of soil moisture measured with the developed detector and oven drying method were observed. Both above results indicated that the detector could satisfy the basic agriculture requirements.

(3) In order to improve the accuracy of soil moisture detection, soil texture should be paid more attention in the next research step.

Fig. 1 .Fig. 2 .Fig. 3 .Fig. 4 .

 1234 Fig.1. Overall structure Control unit Optical unit

  represents the dependent variable of soil moisture content; 1450 x is average absorbance of soil at the wavelength of 1450 nm.

Fig. 6 .

 6 Fig.6. Thematic maps of soil moisture

  of precision agriculture.

Table 1 .

 1 Statistics of soil samples

		Calibration group(40)	Validation group(20)
	Parameter
		Range	Average	Standard	Range	Average	Standard
				deviation			deviation
	Soil moisture (%)	3.15~14.19	8.79	2.41	4.54~12.09	8.03	2.09

Acknowledgements

This study was supported by Chinese National Science and Technology Support Program (2011BAD21B01), NSFC projects (61134011, and 31071330), and 863 programs (2011AA100703, 2011AA100704).


Wei Yang

Nick Sigrimis

Minzan Li

Hong Sun

Lihua Zheng

Correlations between Nitrogen Content and Multispectral Image of Greenhouse Cucumber Grown in Different Nitrogen Level

Keywords: CCD camera, vegetation indexes, multispectral image, LS-SVM

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The short fruit cucumber imported recent years is a kind of new variety of cucumber with better taste. It is favored by consumers and has enormous market potential. The short fruit cucumber can grow all year round using soilless culture in greenhouse, and the nitrogen content is the most important factor to cucumber's quality and quantity. The traditional measurement method of nitrogen content is destructive and time-consuming. When cucumber is in deficient nitrogen state, there will be long serials of changes in color, thickness and morphological characteristics of cucumber leaves. It will make light reflectance characteristic (reflectance intensity, color, texture and so on) change. Thus it is important to conduct research on nondestructive measurement of nitrogen content based on reflection spectra and multispectral images. Scholars both in China and abroad have carried out many researches on this subject based on multispectral images. However, complicated algorithm is the main problem of most multispectral cameras used for analysis of plant multispectral image, and besides high cost it also has very strict requirements of condition for taking photos and choosing object. In this paper, we tried to provide a new approach using a CCD camera with special filters to take spectral images. The demands for the camera were easy to operate, low cost, and wide application.

Material and methods

The research chose short fruit cucumber as object. In order to do research in different nutrition level, different nutrition conditions were applied to cultivate plants. Since soilless cultivation technology can make sure samples are not affected by other factors like soil, it was used in planting cucumbers. All cucumbers were divided into four groups in four different nitrogen content conditions, and each group had 10 cucumbers. The standard nitrogen content (N) was set as 0.067 kg/L. The first group was planted in double standard nitrogen content condition (2N); the second group was in standard nitrogen content condition (N); the third group was in half standard nitrogen content condition (0.5N); and the last group was in one fourth of standard nitrogen content condition (0.2N).

The research chose a normal CCD camera with filters to get multispectral images. The central wavelengths of the filters were 670 nm at red band and 780 nm at near infrared (NIR) band with much more information, low correlation between reflectance data, and good separability. The CCD/CMOS used by digital camera can induce infrared light. In order to avoid color shift produced by infrared ray, digital camera manufacturers often put an Infrared Cut Filter (ICF) in front of CCD to shut off infrared ray. When moving away ICF and adding special filters in front of camera, the lens can get multispectral images at specific wavelengths. Figure1 (a) (Figure2 (a)) and Figure3 (a) (Figure4 (a)) were the examples of the images taken by the camera.

Result and discussion

In order to get characteristic parameters from multispectral image, we needed to do preprocessing of images to decrease the influence of soil background and random factors as much as possible. The preprocessing of multispectral images mainly contained digital image denoising, image enhancement and image segmentation. Spectral information can be decomposing into background noise (normally belongs to low frequency), component information (frequency) and random noise (high frequency). After using Wavelet Packet Transform to spectra, every part of spectra could be totally separated in frequency domain, and then all noise could be easily filtered out.

Steps of the wavelet transform to image denoising were as following. The first was choosing suitable basis functions and decomposition layers (N) to decompose image. The second was quantifying high frequency coefficient and getting rid of high-frequency component of images. The final was, filtering images and eliminating noise according to multi-resolving coefficients of the wavelet transform and reconstructing of wavelet. The 1-5 lay's image decomposing results were compared based on three basic functions such as bior3.1, coif3, db4. The three-lay image decomposing method based on bior3.1 was chosen to process cucumber multispectral images. Images from wavelet transform are showed in Fig. 1 Comparing binary images with two different methods, similar good results were obtained. When processing images, any of these two methods was available. When using these two methods on red images, we also obtained good results, and both of them could be used in image processing.

After filtering and segmentation to multispectral image, the average gray value of NIR, R and G wavelength were got from multispectral image of cucumber leaves. And then vegetation index could be calculated according to the reflectance of NIR, R and G of cucumber leaves. All vegetation indexes we used in research are shown in Table1.

Table1. Vegetation value based on multispectral image

Name Formula DVI NIR IA -R IA GDVI NIR IA -G IA RVI NIR IA /R IA GRVI NIR IA /G IA NDVI (NIR IA -R IA )/(NIR IA +R IA ) SAVI ) 5 . 0 L ( ) L 1 ( L R NIR R NIR IA IA IA IA      GNDVI (NIR IA -G IA )/(NIR IA +G IA ) NDGI (G IA -R IA )/(G IA +R IA ) CNDVI (2NIR IA -R IA -G IA )/(NIR IA +R IA +G IA )
Analyzing correlation coefficient between nitrogen content of cucumber leaves and all kinds of vegetation index in different growth, the result shows in Table2. The results from correlation analysis showed that the correlation between nitrogen content of cucumber leaves and spectral characteristics of the multispectral images was close in teen of cucumbers in different nitrogen levels. The correlation between CNDVI and nitrogen content was higher than others and reached to 0.67. All of them, the correlation coefficients between GNDVI, NDGI, NDVI and nitrogen content were all higher than 0.50. In early flowering stage, the correlations between nitrogen content of cucumber leaves and spectral characteristics of the multispectral images in different nitrogen levels were close. In fruit stage, the correlations between nitrogen content of cucumber leaves and spectral characteristics of the multispectral images in different nitrogen levels existed remarkable difference, and the correlation of 2N level and N level was higher than the correlation of 0.2N level. The results suggested that vegetation index based on multispectral image had been used to reflect nitrogen content of cucumbers in different growth. CNDVI, GNDVI, NDVI and NDGI these four indexes have better ability in nitrogen content detection than other indexes.

Table2. the correlation analysis between nitrogen content and multispectral images of cucumber

Index

After image processing, reflectance of cucumber leaves could be calculated from image, and then vegetation index of cucumber leaves could be obtained from the calculation of reflectance of three wavelengths. The vegetation indexes were important land vegetation ecosystem status parameters and have been found wide application in agriculture, environment and ecological environment. According to chief content of the research, four indexes, including CNDVI、 GNDVI、NDGI and NDV, were chosen, and then building the correlation model between these four vegetation indexes and nitrogen content of cucumber leaves. Least squares support vector machines (LS-SVM) model was proposed to construct model in the paper.

Support Vector Machine (SVM) was a new machine learning method based on Statistical Learning Theory (SLT). The data space was mapped to high dimension feature space with nonlinear Kernel function, so that it could be used to process Higher-Order Nonlinearity classification and regression. It could overcome the shortcomings of ANN BP Networks, like long training time, easy to fall in local optima and limited recognizing range. SVM was trained with LIBSVM algorithm and radial base kernel function. Radial base kernel function had fewer parameter and value limit, and reduced the complexity of model and improved training speed. Cross-validation was used to determinate related parameter, and ample data were divided into training part and testing part.

In modeling, four kinds of vegetation indexes were chosen as input, and the nitrogen content of cucumber leaves was set as output. SVM was used to fit and forecast the nitrogen content of cucumber and built the correlation model. Fig. 5 shows the forecasting model of nitrogen content of cucumber leaves based on SVM, calibration 2 R was 0.92 and verification 2 R was 0.69. The result showed high correlation between vegetation index and nitrogen content of cucumber leaves. This method provided feasibility of collecting and testing growth information of cucumber.

Conclusions

The research chose a CCD camera with special filters as multispectral image gathering platform to gather images of cucumber leaves in different growth. After Wavelet De-noising and Image Segmentation, we got reflectance of cucumber leaves, and vegetation indexes of cucumber leave samples. Correlation analysis was made between vegetation indexes and nitrogen content of cucumber leaves, and the results shows that four indexes, CNDVI, GNDVI, NDGI and NDVI, had much higher correlation with nitrogen content of cucumber leaves. Least squares support vector machines (LS-SVM) algorithm was proposed to construct models between vegetation indexes and nitrogen content. The correlation coefficient between vegetation indexes and nitrogen content of cucumber leaves was 0.92, and verifying coefficient was 0.69. These results had ideal estimations, and the vegetation indexes were comprehensive reflection of nitrogen content of cucumber.

1 . 2 . 3 .Fig. 4 .

 1234 Figure1 shows the near infrared binary images separated by Genetic Algorithm. Fig. 1(a) are two original near infrared images, Fig. 1(b) are images with wavelet transform, Fig. 1(c) shows the near infrared binary image. Fig. 2 shows the near infrared binary image separated by watershed algorithm. Fig. 2 (a) shows two original near infrared images, Fig. 2(b) are images with wavelet transform Fig. 2(c) shows near infrared binary images. Comparing binary images with two different methods, similar good results were obtained. When processing images, any of these two methods was available. When using these

5 .

 5 (a) Prediction result of SVM model (b) Verifying result of SVM model Fig.SVM prediction result of nitrogen content of cucumber leaves

Acknowledgments

. This study was supported by Chinese National Science and Technology Support Program (2011BAD21B01), Chinese National Programs for High Technology Research and Development Research Fund (2011AA100703, 2011AA100704), Doctoral Program of Higher Education of China (20110008130006, 20100008110030).