Qiaoxue Dong

Lili Yang
email: llyang@cau.edu.cn

Mei Qu

Qinglan Shi

Shangfeng Du

Beta Function Law to Model the Dynamics of Fruit's Growth Rate in Tomato: Parameter Estimation and Evaluation

Keywords: Tomato, FGR model, Optimization, Parameter estimation

The calculation of fruit growth rate (FGR) is the main part of fruit-bearing crop growth models. In this paper, greenhouse tomato was used as a research material. The purpose of this study is to better understand the regulation of single fruit growth response to environmental or genetic factors. A FGR model of tomato fruit was described based on Beta function law because it has flexible ability to generate different curve shape when adjusting its parameters. A field experiments with 4 planting densities in 2012 spring was carried out in China, using the cultivar Weichi. The parameters of Beta-law FGR model were estimated with data from the fruits in the first truss by using the optimization algorithm Nelder-Mead The results showed that the optimization procedure described in this paper found best fitted and robust parameters, which implied that those parameters are cultivar specific, and little environmental dependence. The validation against the data from the second truss and third truss with variations in planting density was found to be acceptable (R2=0.9117). This modeling methodology and software program have the potential to become a powerful tool for optimization of ideotype design.

1.INTRODUCTION 

Tomato(solanum lycopersicum) is one of the most important horticultural crops and food preference in China. With the recent development of greenhouse technology, China has become the first tomato production country in the world as far as total amount of yields, but researches still indicated that the actual yield of the tomato in China is only 7.7% of the average potential productivity, and there is still a larger space to improve the potential production. [START_REF] Chu | Spatial distribution of potential productivity for greenhouse tomato variety Zhongza No.9 Based on Tomsim[END_REF] Crop growth modeling provides a powerful research tool for studying the potential production. [START_REF] Pogson | Sensitivity of crop model predictions to entire meteorological and soil input datasets highlights vulnerability to drought[END_REF][START_REF] Luquet | Functional-structural plant modeling to support complex trait phenotyping: Case of rice early vigour and drought tolerance using ecomeristem model[END_REF][START_REF] Soltani | Assessing crop management options with crop simulation models based on generated weather data[END_REF][START_REF] Van Ittersum | On approaches and applications of the Wageningen crop models[END_REF] Calculation of tomato fruit growth rate (FGR) is an important part of tomato simulation models, and the integration of FGR over time gives the accumulated fresh weight along different growth stages. It not only can be used to describe the potential performance in any given environment, but also to describe graded size distribution of final yields. Two kinds of models describing fruit dynamics can be distinguished (1) the empirical models and (2) physiological models. Empirical or regression models are built up based on direct field and experimental observation with some regression functions, for instance the typical S-shaped logistic curves. [START_REF] Van Ittersum | On approaches and applications of the Wageningen crop models[END_REF] Because they are descriptive and the parameters of regression models are possible to varies with different level of observation without botanical meaning. Physiological models, on the other hand, explain the observed growth rates from the underlying physiological process and in relation to the environmental factors. So the parameters in such models are usually divided into two categories: cultivar-specific parameters and environment-dependent parameters, where cultivar-specific parameters are assumed constant for some kinds of cultivar and will be estimated and evaluated through optimization technology. [START_REF] He | Modeling the relationship between tomato fruit growth and the effective accumulated temperature in solar greenhouse[END_REF] Physiological models simulate physiological process by using mathematical equations with different hierarchical level. For instance, in famous tomato growth model of Tomsim and Tomgro, both simulate tomato plant process based on compartment level, and the realization of fruit dynamics depend upon the partitioning of the photosynthesis production into fruit growth phases according to sink strength ratio among compartments, so the final yields is the main simulation objective, while individual fruit growth rate is seldom considered. [START_REF] Acutis | Optimization algorithm for calibrating cropping systems simulation models. A case study with simplex-derived methods integrated in the warm simulation environment Acutis M.e Confalonieri R[END_REF][START_REF] Jones | A dynamic tomato growth and yield model (TOMGRO)[END_REF] Tomato functional-structural model (TFSM)provides detailed hierarchical structure and it entails quantitative integration of mechanism at organ level to provide a better modeling frame explaining the behavior related to environment-dependent or genetic-control phenotypic plasticity . However, because of the complexity of the model and the data sets used to estimate model parameter values were limited, the prediction of fruit kinetics was not accurate enough, thus limiting the application into the real production. [START_REF] Heuvelink | Evaluation of a dynamic simulation model for tomato crop growth and development[END_REF][START_REF] Dingkuhn | Environmental and genetic control of morphogenesis in crops: towards models simulating phenotypic plasticity[END_REF] In practice, management requires well-parameterized predictive models. To accomplish this, a tailored fruit dynamic model from TFSM model for simulating FGR in tomato was presented, which objective is to determine and evaluate those parameters whether they are affected by environment conditions or not. If not, those parameters will be introduced into the physiological models as known invariable cultivar-specific parameters so that the number of parameters to be estimated and the complexity of optimization procedure will be reduced.

2.MATERIALS AND METHODS

Field experiments

An experiment was conducted during spring 2012 (sowing date, 18 January) on tomato (Solanum lycopersicum , WeiChi) at the Xiaotangshan Vegetables Production Zone , Beijing, China (39.55 N, 116.25E). Plants were planted in a multi-span greenhouse. Rock wool were used as soil-free cultivation medium and four density treatments were applied: high density (HD, 4.2 plants m -2 ), intermediate density (MD1, 3.5 plants m -2 and MD2, 2.8plants m -2 ) and low density (LD; 2.1plants m -2 ). Irrigation and fertilization were similar for all density treatments. Air temperature, relative humidity and CO2 were collected every ten minutes by AR5-type data logger. The instrument AV-19Q was used to measure photosynthetic photon flux density.

The first flowering and fruit-set date were recorded. For each treatment, there were three replications and in each replication block, 4 plants were sampled to calculate the average diameter of tomato fruit. The leaf appearance rate was recorded shortly after planting date. After fruit thinning, 8 measurement date were carried out to evaluate the influence of varying density on fruit growth rate(FGR).

Model description

The FGR model is executed at time steps corresponding to organogenetic growth cycles (GC), equal to the thermal time (degree days, o Cd) needed to generate a new metamer(or a leaf). According to the measurements and analysis, we found that there is a strong linear relationship between number of leaf and accumulated thermal time(Fig. 1). According to figure 1, the definition of one GC was derived as follows:

GC = ATT N ( 1
)
where GC is the definition of one growth cycle (about 24.78 degree days); GC is accumulated thermal time((degree days, o Cd) which base temperature is 10 o C.

N is the total number of leaves along main stem.

With the definition of time step GC , we formulate a dynamic model for tomato fruit growth rate as the following form:

( ) * ( ; ) i c i c D GC P f GC par   (2)
where i GC means th i growth cycle; D  is the real incremental size(mm) at the i GC ;

P is unit cycle expansion strength( mm/GC) determining the potential maximum incremental size for each cycle;

(...) c f will determine the variation shape of growth rate and is assumed as inner law only determined by cultivar type or gene control but not affected by environmental conditions ; Here we apply Beta function to model the normalized growth rate for tomato fruit along GCx because it can generate flexible curve shape when adjusting its parameters.

1 1

1 0.5 0.5 ( ) (1 ) ( ) ( ) p q x x x T x x GC GC T T f GC f GC         (3)
where

(1, 2,... ) xT 
and T is maximum expansion cycles and can be observed from field experiments; ( , ) pq are the parameters of Beta function to be estimated.

Parameter calibration and evaluation

To obtain the parameters of Beta law, a reliable calibration procedure was required. We firstly defined a criteria to evaluate the performance of the FGR model where non-linear least square sum was used as the objective function.

N NLS i i i Z y f x     (4)
Where i y is the observation(

where p is predicted data, o is observation data and n is the number of observations. The smaller the RMSE , the better the prediction.

Statistical analysis

The data were analyzed using R package 'stats' (version3.0.2). R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna,Austria. URL http://www.R-project.org/.

RESULTS AND DISCUSSION

In this section, the FGR model's ability based on beta-law to simulate the expansion rate of tomato fruit was evaluated in two steps. First, the measurements on the fruits' dynamics of the first truss were used for calibration . Second, a validation with parameterization was made using the observations from the fruits in the second and third truss along the main stem .

Those data for calibration were collected every five days from the date of fruit-set (20 Feb. 2012) to the maturity date (11 Apr.2012). To convert the calendar date to growth cycles which are required in the model, we first compute the accumulated thermal time from fruit-set for each observation date, then use the relationship described in Fig. 1 to calculated the passing growth cycles. So the incremental diameter on each measurement will be divided by number of growth cycles and the calculated value was considered as the average growth rate on the observation date. To capture the essence of the tomato fruit's characteristics and to investigate if the model parameters are environment-dependent or not, the FGR model 's parameters were estimated in each condition against target files composed of 4 planting density ( LD,MD1,MD2,HD).

The estimated curves for four densities were shown in Fig. 2. Those curves have similar shape and a good agreement between observation and model results was obtained for MD1,MD2,HD( a RMSE of 25% and below was defined as acceptable agreement),while the RMSE for LD is a little bigger , the possible reason for which is that the date of the first fruit set should be earlier than the others, but in real measurement and calibration procedure , it is recorded the same as the other density. But anyway , we can concluded that the Beta law could be possible to model the growth rate of tomato fruits' dynamics , because it can basically track the variation of tomato fruit's kinetics. Model parameters were determined for each growing condition by statistical optimization. Table 1 presents the optimized parameter values and their respective standard deviation for each situation. Coefficient of variation (CV%) shows a higher stability of model parameters , and it indicates that increasing densities had little influence on the parameters defining the kinetics of organ fruit(beta law parameters.

Table 1. Value of parameters estimated and evaluation Fig. 3 shows the iterations process of the optimism algorithm ,i.e. Nelder-Mead applied in the non-linear least square calibration procedure. The curve indicated the good performance because the optimization algorithm converged very quickly and found the best fitted values of parameters to minimize the objective function, and it successfully found global minimum while avoiding local one. So this optimization algorithm is suitable for crop growth models. We made an average on parameters' values for four density and the determined model was validated by the fruits dynamics in the second truss and third truss. The simulation results against the observation were shown in Fig. 4. The simulation of tomato fruit's growth rate generally described the observed field data and the regression of observed data on predicted ones indicated a one to one correspondence (R2=0.91). But we still can observe from Fig. 4 that some points are over-predicted or largely under-predicted .One of the possible reasons for inaccurate prediction is that observation is the average diameter of total truss fruits , while in the calibration set, the observation is made from regular date and a single fruit, another reason is that a different nutrient solution was applied in the medium-later growth stages for the second truss and third truss from the one applied in the first truss. However, despite some inaccuracies , the model still provided a reasonable simulation of fruit expansion patterns.

CONCLUSION

Form the parameterization of the tomato growth rate model and results' analysis, it is concluded that the FGR beta-law model by using the concepts of growth cycles is able to accurately simulate the expansion rate of greenhouse tomato's fruit. The optimized parameters of model are similar for different planting densities which further strengthened the assumption that those parameters are cultivar-specific and not affected by external environment conditions. The modeling methodology described in this paper will find applications on yield and quality prediction and gene-based ideotype optimization design.

Fig. 1 .

 1 Fig.1. Method of calculating the growth cycles(GCs) according to the relationship between accumulated thermal time and number of leaves

14

 14

Fig. 2 .

 2 Fig.2. Fruit growth rate of the first truss as a function growth cycles after fruit-set: measurement verse fitted results with Beta-law FGR model.

Fig. 3 .

 3 Fig.3. Convergence paths of the Nelder-Mead optimization algorithm applied to the FGR model.

Fig. 4 .

 4 Fig.4. The validation of model against the data from the second and third truss

  1, 2,...

	iN 	=1);	( ; )
	RMSE			(	2 ) / p o n 

i fx is the output of model;  is the parameter vector to be estimated which final calibrated value will minimize this objective function. To implement this, the optimization algorithm Nelder-Mead was introduced into model calibration procedure to find stable parameters. Finally, The test of calibration results against new data set was the root mean square error (RMSE), calculated as .

[START_REF] Dong | Does the Structure-Function Model GREENLAB deal with crop phenotypic plasticity induced by plant spacing? A case study on tomato[END_REF]

ACKNOWLEDGMENTS:

This study was supported by the National Natural Science Foundation of China (#61174088 and #31200543), and Special Found for Beijing Common Construction Project. The authors would also like to thank students from College of Agronomy, China Agricultural University involved in the plant measurements.


Baofeng Su

Linya Huang

Zhen Gao

Jiao Guo
email: djiao.g@163.com

Agricultural Environmental Information Collection Device Based on Raspberry Pi

Keywords: agricultural environmental information, integration, open source, Raspberry Pi

Integrated agricultural environmental information real-time collection, transmission and management is extremely critical for precision agriculture (PA). This paper describes the basic knowledge and primary principles used in an agricultural environmental information collection device. The device is based on Raspberry Pi, combining with GPS module, some digital sensors and analog sensors to measure environmental temperature and humidity, barometric pressure, light intensity, soil moisture and other environmental information accurate collection. All collected data was real-time transmitted to a remote server specified database for management. In the experiment process, the device has been proved to have the ability to catch the distributed information and also can get different centralized data management. The results show the integration of operational information collection, transmission and management, as well as the use of open source software to make it easy to collect multiple types of data parallelism, which provides an important solution for the rapid transformation of agricultural production.

Introduction

Efficient collection and scientific management of farmland environmental information farmland for information technology has great significance [START_REF] Yuan | The Development Status, Existing Problems and Countermeasures of Agricultural Information Construction in China[END_REF]. Efficient collection of environmental information is beneficial to have timely access to agricultural information, to meet the diverse needs of farm data information, and more comprehensive and accurate grasp of environmental development. And scientific management is essential for post-processing of information. Through scientific management of information, and we can normalize all kinds of scattered and mixed messages, which is conducive to statistics and data classification. Comprehensive use of agricultural information technology will become an important means of twentyfirst century rational use of agricultural resources, improve crop yields, reduce production costs and improve competitiveness in international markets of agricultural products. However, our positioning on the use of GPS technology to collect field information technology than in other countries started late, most information acquisition devices' detection and management technology are decentralized, while the collection of data immutable, which restricts the acquisition of real-time equipment, effectiveness and flexibility. Meanwhile, most parts of China are still using manual collection of agricultural information. This method has many limitations that cannot meet the sophisticated needs and the development of agriculture, such as the poor timeliness, information collection and intelligent digital low, poor visualization, and low level of shared management of the effects of data.

For agriculture, farm environmental information collected intelligence requires integrated information collection, transmission and management in order to achieve real-time and efficient gathering information and science, improve the management of data [START_REF] Wu | Environmental issues in China: Monitoring, assessment and management[END_REF]. There have been some international research institutions and researchers with experience of successful experiments. Ricardo Godoi Vieira et al., on the Amazon River Basin for environmental information, the development of a monitoring node, and completed by WSN transmission of environmental information [START_REF] Vieira | An energy management method of sensor nodes for environmental monitoring in Amazonian Basin[END_REF]. J. Yang et al. studied the measurement and transmission test for soil moisture [START_REF] Yang | Integration of wireless sensor networks in environmental monitoring cyber infrastructure[END_REF]. Xin, Z., et al. studied on monitoring network for crop growing environment [START_REF] Xin | Study on wireless-monitoring technology for crop growing environment[END_REF]. Throughout the study of these scholars we find that their research focused on a microcontroller as the core function of simple farmland information collection device, such as GPS positioning technology combined with measurement of soil moisture, soil nutrients, planting density and other parameters. They finished just one-sided information collection work, such as a single with a measurement and use manual records or indirect transfer remote storage after collection. Most of the study only focused on access to information unilaterally, and the integration of information collection, transmission and management did not materialize. This type of data acquisition equipment is characterized by the development of a low cost, single function and the need for complex post-processing data.

Currently, with the development of open source hardware equipment and electronic technology, Raspberry Pi increasingly showing its superiority. The Raspberry Pi's appearance and behavior provides a new stage for the research platform of environment monitoring and management. Raspberry Pi with its simple operation, high processing speed and a variety of interface get a lot of attention to research staff. Slawomir introduced the Raspberry Pi as a measure to control the use of cell [START_REF] Michalak | Raspberry Pi as a Measurement System Control Unit[END_REF], Sheikh studied the environmental monitoring system based on the Raspberry Pi and Arduino [START_REF] Ferdoush | Wireless Sensor Network System Design using Raspberry Pi and Arduino for Environmental Monitoring Applications[END_REF], Vladimir and other researchers used the Raspberry Pi to achieve a smart home network node design [START_REF] Vujovic | Raspberry Pi as a Sensor Web node for home automation[END_REF]. However, on the use of agricultural information collection Raspberry Pi research are rare.

This paper proposes a farm environment information collection device, and designs an agricultural information collection device based on Raspberry Pi. Raspberry Pi equipped with a variety of sensors for many types of parallel acquisition and transmission of information, data acquired by the built-in memory card Raspberry Pi for online storage or sent via GPRS module. In practice, offline maps can be saved in the Raspberry Pi, so users can watch the current environmental information in real time, improving flexibility. The multi-class data collection procedures was controlled by python that open source software. The device can store information or online transmission, according to the needs of users, for database management information, namely, the achievement of the integrated operation of information collection, transmission and management. These features improve the timeliness, reliability, convenience and flexibility of the device, so that it can solve most of the current information collection problems encountered in the process of agricultural production.

Experiments and Methods

For the agricultural information collection device, which through digital sensors for measuring air temperature and humidity, barometric pressure, soil moisture and light intensity and other environmental information accurate collection. The information collected after treatment Raspberry Pi, the user can select the data storage as needed or sent online. Data is stored in the processor's built-in selectable storage memory or external memory card, online delivery is to the C / S model will be sent to the remote server that stores data via GPRS module. Meanwhile, the device can achieve humancomputer interaction, user-friendly command displays the input and farmland environmental information. The entire device use Raspberry Pi achieve environmental information farmland acquisition, processing, storage and delivery of integrated operations, as well as store location map. The main hardware components of the device are Raspberry Pi, information collection module, a wireless network communication module and the auxiliary module. Wherein the auxiliary module is touch input and display modules, scalable data storage module and power supply module. The device collects air temperature and humidity information through the AM2301 digital temperature and humidity sensor. Its transmission distance is up to 20 meters or more. The device collects pneumatic pressure through the GY-65 digital pressure sensor, and is connected via I2C interface to communicate with the Raspberry Pi. GY-65 sensor is a high-precision, low-power digital pressure sensor. It built-in chip is BMP085, and its measurement range of 300 ~ 1100hPa. It does not require an external clock circuit, and it built-in temperature compensation, it is possible to reduce the influence of environmental factors. In this study, we collected light intensity by GY-30 sensor, which is connected via I2C interface to communicate with the Raspberry Pi. GY-30 light intensity detection sensor uses BH1750FVI chip, and the illumination range is 0 ~ 65535 Lx. The sensor does not distinguish between ambient light, which has close to the visual sensitivity of the spectral characteristic. Soil moisture information collected using YL-69 sensors that standard single-bus interface. Soil moisture data detection module uses a D/A dual output mode. Its sensitivity is adjustable, and its comparators work stable LM393 chip. Agricultural Environmental Information collected for processing information in a central Raspberry Pi of the device, and the information in real time by GPRS module is sent to the user.

System

Fig. 1. Hardware framework

Auxiliary module of the agricultural information collection device mainly includes a touch input display module, scalable data storage module and power module. Touch input display module as a 7-inch LCD touch screen, and to support their daily work by 5V supply. Help users operate and collect information collection terminal display. Scalable data storage module for Raspberry Pi can add removable storage devices, and users can capture information stored in the internal memory or a Raspberry Pi terminals need to select the external memory. 5V voltage power supply module provides rechargeable battery or mobile power, providing power to support the normal operation of the device. The farmland hardware environment information collection device overall block diagram shown in Fig. 1.

Wireless Network Communication.

For the wireless network communication module, GPRS module can be used. GPRS is the abbreviation of General Packet Radio Service .It is a global mobile phone system (GSM) based data transmission technology. GPRS support and directly connected to the IP network, it is possible to provide SMS, MMS and other services. GPRS network short access time, transmission efficiency is higher than the GSM, theoretical bandwidth can reach 171.2 Kbit/s. GPRS network supports standard data communication protocol applications that can interconnect with IP networks , X.25 networks , point to point and point to multipoint service support. Users can freely move their distribution and network points, for IP communication desired location. This device is mainly applied to Internet-enabled GPRS module. In this device, we use SIM900A module to achieve the requirements of the Internet by APN.

Application System Developing

The central processor of the present study is the type B Raspberry Pi, built a Linux system to support python, Java, C and other programming languages. This study is based on Python programming. Python is an interactive, object-oriented, dynamic semantics and syntax beautiful scripting language. Python is an efficient development tool that supports multiple operating systems, with a free open-source, high-level language, portability, object-oriented, embeddable scalability characteristics. In this study, the device uses the python programming language, to achieve a sensor of farmland environmental information collection, collection terminals to communicate with a remote server socket based C / S mode, the operation of the database.

Information Collection Sensor System. In recent years, Dallas Semiconductor has introduced a unique single-bus technology. Single bus widely used for single-host system, which can control one or more slave devices, data exchange between them only through a signal line. For sensor collection of farmland environmental information, AM2301 temperature and humidity sensors and YL-69 soil moisture sensor to air temperature, humidity and barometric pressure information via a single bus and Raspberry Pi communication with the control easy, expansion of convenience advantages. GY-65 GY-30 pressure sensors and light intensity sensor used to collect air pressure and light intensity information. For parameters characteristic of the collection, the device uses an I2C interface to communicate with the Raspberry Pi. This bus developed by the PHILIPS two-wire serial bus is widely used in the microelectronics field communication control a bus standard. It is used to connect the microcontroller and its peripherals for the 2-wire synchronous serial communication with the interface cable less, simple control mode, a smaller device package, higher communication speed and so on. For Ubox NEO-6M GPS module, it is sent via UART communication interfaces and raspberries. A serial interface allows data to order a transfer, which is characterized by simple communication lines, and as long as the pair of transmission lines can be two-way communication, thereby reducing the cost of the equipment. Information collected scheme is shown in Fig. 2. In the application the client / server model, the client generally based embedded computer processor core units; the server is generally user-created data center. In this mode, the data center server as a user, you need to have a fixed IP address or domain name address using the fixed conversion dynamic DNS, so the client can easily obtain the IP address of the server in order to establish a TCP connection. The system to collect terminal for the client to the remote server to server-side, to achieve collection terminals communicate with a remote server using python programming socket. The type B Raspberry Pi and sim900A wiring shown in Fig. 3.

Results and Discussion

Experimental testing of the system is carried out in a wheat field, located in the neighborhood of college of Mechanical and Electronic Engineering, Northwest A & F University, Yangling District of Shaanxi Province (34°17'33.22''N, 108°04'11.8''E). Before the experiment make Yangling district maps stored into the Raspberry Pi. Using collection terminal operator in the field of environmental information collected and sent via GPRS module. The operator chose a more open testing ground for the trial subjects, and placed the sensor in the fields. After starting the collection terminal, initialize the system, manual collection touch button, select the collection of information stored or transmitted. Remote server data store as shown in Fig. 5.

Fig. 4. Data storage on remote server

Results show that agricultural environmental information collection device in this paper can effectively collect real-time environmental information farmland and the ability to select the storage or sent to the database server's designated according to user needs. The device has collected information accuracy and short cycle advantages. Each of which will perform data collection, transfer and management, to achieve the integration of agricultural information collection operation.

Conclusions

This paper designed a farm environment information collection device. The Raspberry Pi system as the core, combined with multi-sensor and GPS module for real-time acquisition of farmland environmental information. It does this by using GPRS module socket communication mechanisms to achieve the wireless transmission and storage of information. It gives Python operation of each sensor, GPRS module and PostgreSQL database program implementation. The device realizes the accuracy of the information collection, scientific management and dynamic distribution requirements. The device uses the connection Raspberry Pi and information collection, transmission and transmit modules to achieve the integration of information collection, transmission and management. It meets the accuracy of the information collection, scientific management, dynamic distribution requirements, and have real-time high, convenient and reliable, low power consumption advantages.

Farmland

  Environmental Information Collection. What the collection point of object farm setting information collection module collected mainly include latitude and longitude, altitude, barometric pressure, air temperature and humidity, soil moisture, light intensity collection points and other factors. The latitude and longitude, altitude information collection point by Ubox NEO-6M GPS module connected via UART interface; The GPS location information module serial NMEA-0183 output format uses standard communication format. The output data is in ASCII code, contains the latitude, longitude, altitude, speed, date and other information.

Fig. 2 .

 2 Fig. 2. Data collection flow

Fig. 3 .

 3 Fig. 3. The type B Raspberry Pi connection with sim900A

  60°C, testing method: load the sample into the three-quarters of sample bottle, and then place the sample bottle into the sample cell. Stabilized in constant temperature for 5min, the bottle is taken out to check if there exist bubbles. It starts to collect spectrogram if there is no bubble, and each sample averages out three times. Use NIRS random RIMP software and MATLAB7.8 to collect spectra and convert data format, use chemo metrics software Unscrambler X 10.1 to pretreatment the spectral data and analyze principal component, and use SVM pattern recognition and regression software package designed by a prof. Lin from National Taiwan University to build SVM models in MATLAB7.8 and parameters optimization.

Hardware Design Raspberry Pi. "Raspberry Pi Foundation" developed the Raspberry Pi and which is a registered charity in the United Kingdom. Raspberry Pi is a card-type computer, and its size is only 85.6 x 53.98 x 17mm

[START_REF] Mcmanus | Introducing the Raspberry Pi[END_REF]

. As of April 2015, Raspberry Pi Foundation has released a total of four Raspberry Pi, respectively, Raspberry Pi Model A, Model B, Model B + and 2 Model B. The type B Raspberry Pi is the choice of this study, the core module of this system is produced Broadcom's BCM2835, and its memory is 512MB. SD card can be used as the storage medium. It integrated GPIO, I2C, UART and SPI interfaces, and it can connect a variety of sensors to collect environmental information farmland

[START_REF] Banerjee | Secure Sensor Node with Raspberry Pi[END_REF]

. The type B Raspberry Pi can run on many Adopt SupNIR-5700 NIRS (Focused Photonics (Hangzhou), Inc.) to collect NIR spectra of all samples. Spectral measurement of samples uses random RIMP software and its testing method is: transmission, measurement range: 1000~1800nm, scanning speed:10 times/sec, spectral resolution: 6nm, temperature of sample cell:

Acknowledgment

The authors would like to thank the anonymous reviewers for their critical and constructive review of the manuscript. This study was supported by the National Natural Science Foundation of China (No. 41401391), the Fundamental Research Funds for the Central Universities of China (No. 2014YB071, No. Z109021423), and the Exclusive Talent Funds of Northwest A&F University of China (No. 2013BSJJ017, No. Z109021108).


Lei Xi

Guang Zheng

Yanna Ren

Xinming Ma

An Algorithm for the Interactive Calculating of Wheat Plant Surface Point Coordinates Based on Point Cloud Model

Keywords: wheat plant, point cloud model, point coordinates, interactive

Employing the 3-dpoint cloud model of the wheat plant as the research object, through the establishment of mapping algorithm from the screen pixel to the coordinate point of the wheat plant body surface, this paper realized the calculating for the point coordinates on the wheat plant surface utilizing the mouse operation. This algorithm was constituted of solving a user view equation of given screen pixels, screening the point cloud near the line of sight, extracting the efficient point sets near the line of sight, surface fitting efficient point set and solving the intersection of the line of sight and the fitting surface five processing flow on the basis of scattered point cloud data preprocessing and rendering. Adopting the mark function of the FastSCAN 3-d digital scanner accessory instrumental software, the comparison validation of the wheat plant body surface coordinates points' selection was conducted. The validation results show that the error less than 2.1 mm and had good precision. Considering the influence of the users' perspective to the calculation of the plant surface points coordinates, the comparison validation was conducted with the10º , 20º and 0º perspective respectively. The results show that the error is less than 1.8 mm and had good accuracy. The mapping algorithm between the screen pixel and the object surface coordinate point established in the study could map the action of the mouse on the screen window to the operation of the object surface. Moreover, it also provided the technical reference for the establishment of the geometry measurement of the interactive plants based on point cloud data.

INTRODUCTION

Virtual plant was always the research hot spot of the interdisciplines such as the digital agriculture and virtual reality. The 3-d geometry measurement of plants was a basic and preliminary work of the virtual-plants modeling. Whether the building of the geometric modeling and the morphogenesis model of plant organs, or the building of the quantitative topology model of the plant, the accurate, complete geometric information was necessary for support. Due to the complex characteristics of the internal morphological structure of the plants, the measurements to the plant geometrical morphology became a timeconsuming complex work. With the development of the 3-d information acquisition technology, rapid access to the highprecision point cloud data of the plants became possible. It also provided a new method for obtaining the geometrical morphology data of the plants. The methods of measuring the geometrical morphology data of the plants could be divided into two categories generally: the geometrical morphology measurement to the surface model formed by the reconstruction of point cloud [1-4] and the geometrical morphology measurement using the point cloud data directly [5-6] . Due to the reconstruction of topological structure between body surface points and establish object surface model in the surface model formed by the reconstruction of point cloud, the design of the measurement algorithm was relatively simple. Moreover, the existing CAD software could be used in the measurement method. However, the shortcomings such as time-consuming and big error (produce redundant triangles) could easily arise during the reconstructing the point cloud data of the whole plant because of the complexity of the plant structure. Therefore, the current study of plant point clouds reconstruction mostly concentrated in the point clouds reconstruction of plant organs [7] . The research of the measurement to the plant geometrical morphology using the point cloud data directly was concentrated in certain fixed shape feature extraction [8] utilizing the point cloud feature extraction technology [9-11] . Although the measurement to the plant geometrical morphology using point cloud feature extraction technology possessed the advantages of the high degree of automation, the interactive measurement to the geometrical morphology of plants such as leaf edge curve, veins curve and space angle with the help of the users' selection to the surface coordinates points had the characteristics of high maneuverability and flexible. Therefore, using the threedimensional point cloud data of the wheat plant as the object of study, the research in the paper realized the calculation of the plant surface point coordinates using the mouse operation and provided the technical reference for the interactive plant geometrical morphology measurement based on point cloud data.

MATERIAL AND METHOD

The calculating of the interactive object surface point coordinates aimed to build the mapping between the screen pixels selected by the user mouse operation to the object surface coordinate points. Using mathematical language, it could be described as the calculating the intersection point of the first time intersection between the object's surface and the sight line of users along the line of sight direction. The intersection point was the mapped coordinate point of the screen pixel on the object body surface. Due to the lack of the concept of the "surface" of the object represented by the point cloud data, the intersecting probability of the user's line of sight with any point in the point cloud data all was 0 in theory. Therefore, in this study, the intersection between the line of sight and the object surface was defined as the intersection between the line of sight and the local surface fitted by the point cloud data near the line of sight. Thus, the mapping algorithm from the screen pixel point to the object surface coordinates point on the basis of the preprocessing and rendering to the point cloud data in this paper was constituted by the solving the user sight equation passing the given screen pixel point, screening the point cloud near the line of sight, extracting the efficient point set near the line of sight, surface fitting efficient point set and calculating the intersection between the line of sight and the fitted curve five processing steps and shown in Figure 1.

Solving the user sight equation passing the given screen pixel point

Accounting the coordinate system (object coordinate system) describing the point cloud data was the 𝑂 system. The coordinate system with the increased depth values describing the window coordinates was named the 𝑂 ′ system. The transition from the 𝑂 system to the 𝑂 ′ system could be expressed by the transformational matrix generated by the point cloud rendering, as shown in the formula [START_REF] Boissionnat | Geometric structures for three-dimensional shape representation[END_REF].

𝑀 = ∏ 𝑀 𝑖 1 𝑖=𝑛 (1)
In the formula, the M was the transformational matrix from the 𝑂 system to the 𝑂 ′ system. The𝑀 𝑖 was the transformational matrixgenerated by the point cloud rendering following the sequence. Suppose that the window coordinate of the depth value of the pixel point passed by the line of sight was the 𝑃 𝑤 ′ (𝑤 𝑥 , 𝑤 𝑦 , 𝑤 𝑧 ), the corresponding point of the pixel point 𝑃 𝑤 in the coordinate system 𝑂 could be expressed by the formula [START_REF] Dey | Tight cocone: a watertight surface reconstructor[END_REF].

𝑃 𝑤 = 𝑀 • 𝑃 𝑤 ′ (2)
The 𝑤 𝑥 , 𝑤 𝑦 were expressed by the window coordinate of the mouse during the approach implementation. The 𝑤 𝑧 could be determined by the projection matrix in the point cloud data rendering and commonly was 0. Suppose that the location of the user's view was 𝑃 𝑒 , the mathematical equation of the line of the sight could be expressed by the formula [START_REF] Dey | Provable surface reconstruction from noisy samples[END_REF].

𝑃(𝑡) = 𝑃 𝑤 + 𝑣 𝑒 • 𝑡 (3)
The 𝑣 𝑒 was the direction vector of the line of sight and given by the formula (4). The 𝑃 𝑒 was set during rendering the point cloud data. In the point cloud data near the line of sight, using the clustering based on density and the WaveCluster method [12-15] both could realize the corresponding requirements of identifying effective point set. However, the clustering based on density generally required the users to set the corresponding parameters. Moreover, both the two clustering methods had the shortage that the calculation speed was slow. Considering the requirement to the procedure response speed for the "interactive" design, this research conducted the dimension reduction process to the point cloud data near the line of sight firstly. After that, a linear algorithm was designed to conduct the method for extracting the effective point set.

In the design of the dimension reduction process for the point cloud data near the line of sight, the point cloud located in the different local surface could be found. The projection of the distance from these point cloud to the point of view in the line of sight direction would have bigger difference, as shown in Figure 3. Hence, J was set as the result for the dimension reduction process to the point cloud data near the line of sight. It could be defined as the formula [START_REF] Paulus | Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping[END_REF].

𝐽 𝑖 = |𝑃 𝑒 𝑃 𝑖 • 𝑣 𝑒 | (6)
In the formula, the 𝑃 𝑖 was the point 𝑖 in the point cloud data near the line of sight. Analysis the point cloud near the line of sight, 𝐽 𝑖 would appear the abnormal increasing from a local surface to another local surface along the line of sight direction usually because the threshold value r was small relatively. Therefore, the efficient point set could be detected through the analysis of the increased abnormal values of adjacent points 𝐽 𝑖 . The specific algorithm was designed as follows:

The linear table 𝐿 𝑝 was constructed and saved in the point cloud near the line of sight. After that, it was sort following the 𝐽 𝑖 of each point. The increment of the 𝐽 𝑖 of the two neighboring points was calculated and saved in the new linear table 𝐿 𝑗 . The process could be expressed as the 𝐿 𝑗 [𝑖] = 𝐿 𝑝 [𝑖 + 1]. 𝐽 -𝐿 𝑝 [𝑖]. 𝐽. After that, the abnormal value in the 𝐿 𝑗 was detected to judge whether the point could reach the next part surface. Considering the interactive response speed, the algorithm adopted the simple abnormal value detection method and determined whether the element in the 𝐿 𝑗 was the abnormal value by detecting whether the deviation of each element in the 𝐿 𝑗 was greater than a certain ratio standard deviation.

The two multiple parameters𝑛 1 , 𝑛 2 were set to be the threshold value for the abnormal value detection in the algorithm. The 𝑛 1 was set as the threshold value for the abnormal value-to-be and expressed that the next step detection should to be continue to conduct the abnormal value judgment. The 𝑛 2 was the threshold value for the abnormal value and expressed that the abnormal value was detected currently. The algorithm implementation process was shown as follows: [START_REF] Boissionnat | Geometric structures for three-dimensional shape representation[END_REF]. A set 𝑆 𝑝 was established as a valid point set and initialized to the first three elements of the 𝐿 𝑝 .

(2). The linear table 𝐼 𝑗 was established to hold the increment of the 𝐽 𝑖 in computer and initialized to the first two elements of the 𝐿 𝑗 . The loop variable was defined 𝑖 as 4.

2.4Surface fitting efficient point set

According to the interpretation for the intersection of the line of sight and the point cloud, the intersection of the line of sight and the object was actually the intersection of the line of sight and the curved surface expressed by the efficient point set near the line of sight. The setting of the thresholdr was generally smaller during the extracting the point cloud near to the line of sight. At the same time, according to the demand of the rapid response in the user interaction, the least square plain fitting to the efficient point set was adopted to the express the local surface of object in the method. The method was as follows. The equations of plane was set as 𝑧 = -𝐴𝑥 -𝐵𝑦 + 𝐷. Substituted all the points in the efficient point set into the equation, the least square error formula could be obtained and shown as the formula [START_REF] Lu Shenglian | Research on Techniques for Accurate Modeling and Rendering3D Plant Leaf[END_REF].

𝐸 = ∑(-𝐴𝑥 𝑖 -𝐵𝑦 𝑖 + 𝐷 -𝑧 𝑖 ) (7)
Due to the 𝛻𝐸 = 0, the least square solution of the 𝐴, 𝐵, 𝐷 could be obtained and shown as the formula [START_REF] Yuandi | Midvein Extraction for 3-D Corn Leaf Model Based on Parameterization[END_REF].

( 𝛴𝑥 𝑖 2 𝛴𝑥 𝑖 𝑦 𝑖 -𝛴𝑥 𝑖 𝛴𝑥 𝑖 𝑦 𝑖 𝛴𝑦 𝑖 2 -𝛴𝑦 𝑖 -𝛴𝑥 𝑖 -𝛴𝑦 𝑖 𝑛 ) ( 𝐴 𝐵 𝐷 ) = ( -𝛴𝑥 𝑖 𝑧 𝑖 -𝛴𝑦 𝑖 𝑧 𝑖 𝛴𝑧 𝑖 ) (8)
In the formula, n expressed the number of the point sets. 𝑥 𝑖 , 𝑦 𝑖 , 𝑧 𝑖 was the coordinate value of the point 𝑖. The least square estimation of the local surface could be obtained through the solving of the equation set. For the convenience of the calculating of the intersection of the line of sight and the fitting plane, the vector V was defined as 𝑣 = (𝐴, 𝐵, 1),the vector P was defined as 𝑃 = (𝑥, 𝑦, 𝑧). Thus, the plane equation could be expressed as the vector expression and shown in the formula [START_REF] Satio | Comperhensible Rendering of 3-D Shapes[END_REF]. 𝑣 • 𝑃 = 𝐷 (9) 2.5 Calculating the intersection between the line of sight and the object surface Contact the formula ( 9) and the formula (3), the parametert of the point of intersection between the line of sight of users and the fitting local surface of the object could be determined and shown as the formula [START_REF] Dong | Algorithm for Extracting Sharp Features from Point Cloud Models[END_REF]. After that, as shown in figure 5, the intersection point could then be found.

𝑡 =

𝐷 -𝑣 • 𝑃 𝑤 𝑣 • 𝑣 𝑒 [START_REF] Dong | Algorithm for Extracting Sharp Features from Point Cloud Models[END_REF] Fig. 5 The intersection point of the line of sight and the object

RESULT AND ANALYSIS

Using the point cloud data of the wheat plant collected in Henan agricultural university science park from 2012 to 2013 as the experimental material, adopting the C++ language, with the help of the software library such as OpenGL, PCL [16] and so on, the interactive wheat plant surface pastry coordinates calculating algorithm was realized at the Ubuntu12.04 system environment in the research and shown in Figure 6. As shown in table 1, using the mark function of the supporting tools software of the FastSCAN 3D digital scanner, the mapping from the screen pixels to the object surface coordinates was verified. The results showed that the error of the proposed mapping method in this paper was less than 2.1 mm compared with the FastSCAN and had good accuracy. Because there was no direct intersection between the line of sight and the point cloud in this study, the error of the interactive selection of the points was mainly produced by different user perspective. As shown in table 2 and table 3, this study conducted the comparison validation with the 10 o , 20 o and the 0 o viewing angle respectively. The results showed that the error of the method was less than 1.8 mm in the normal range of angle and had good accuracy.

CONCLUSION

Using the scattered three-dimensional point cloud data of the wheat plant as the research object, through the procedure including "Solving the user sight equation passing the given screen pixel point--Screening the point cloud near the line of sight--Extracting efficient point set in the point cloud near the line of sight--Surface fitting efficient point set--Calculating the intersection between the line of sight and the object surface", the mapping from the screen pixel point to the object surface coordinate point was formed. It could map the window coordinate of the user's mouse to the object surface which expressed by the point cloud and realize the calculation for the wheat plant surface point coordinate. T This method had certain universality and could to provide technical reference for the construction of the interactive plant to geometry measurement based on point cloud data.

Fig. 1 .

 1 Fig.1. The algorithm flow of the mapping from the screen pixel point to the object surface coordinate point

2 .Fig. 2 . 2 ( 5 ) 2 . 3

 222523 2Screening the point cloud near the line of sight As shown in Figure 2, all the points which the distance to the sight less than the threshold 𝑟 were regarded as the points near the line of sight and were defined as the point cloud near the line of sight. In the Figure 2b, the full line was used to express the line of sight of the users. The 𝑆 1 , 𝑆 2 , 𝑆 3 , 𝑆 4 was used to the point cloud near the line of sight. Moreover, it also was used to stand for the 4 local surface of the object. a b The point cloud near the line of sight Based on the data equation of the line of sight, for any point in the point cloud, the distance to the line of sight could be given by the formula (5). Any point in the point cloud could be conducted the screening and extracting following the formula (5). 𝑑 = √(𝑃 𝑤 𝑃 𝑖 • 𝑣 𝑒 ) 2 + 𝑃 𝑤 𝑃 𝑖 Extracting the efficient point set near the line of sight In general, the point cloud near the line of sight often represented one or more local surfaces. According to the definition of the mapping from the screen pixels to the surface coordinates, only the local surface closest to the viewpoint was used to calculate the surface of the mapping point. That also meant that only the point set which represented the surface was effective. For example, in figure 2 b, the point cloud near the line of sight expressed four local surfaces. However, only the point set 𝑆 1 was valid.

Fig. 3

 3 Fig.3 The projection of the distance from the point to the point of view in the line of sight direction

  [START_REF] Dey | Provable surface reconstruction from noisy samples[END_REF]. At the time of the 𝑖 > 𝑛, the algorithm was stopped. n was the length of the 𝐿 𝑝 .[START_REF] Yinxi | Amended Delaunay Algorithm for Single Tree Factor Extraction Using 3-D Crown Modeling[END_REF]. Calculating the mean 𝐼 𝑎𝑣𝑔 and the standard deviation 𝐼 𝑠𝑡𝑑 of the elements in the 𝐼 𝑗 . (5). Executing 𝑡𝑚𝑝 = 𝐿 𝑗 [𝑖 -1] -𝐿 𝑗 [𝑖 -2], if 𝑡𝑚𝑝 -𝐼 𝑎𝑣𝑔 > 𝑛 1 • 𝐼 𝑠𝑡𝑑 , go to (8). (6). Putting the 𝐿 𝑝 [𝑖] in the set 𝑆 𝑝 , putting 𝐿 𝑥 [𝑖 -1] in the set 𝐼 𝑥 , 𝑖 = 𝑖 + 1, go to (3). (7). If 𝑡𝑚𝑝 -𝐼 𝑎𝑣𝑔 > 𝑛 2 • 𝐼 𝑠𝑡𝑑 , that meant the next surface had been reached, exiting program. (8). While 𝑖 + 1 > 𝑛, the algorithm was stopped. (9). Executing 𝑡𝑚𝑝 = 𝐿 𝑗 [𝑖] -𝐿 𝑗 [𝑖 -2], if 𝑡𝑚𝑝 -𝐼 𝑎𝑣𝑔 > 𝑛 2 • 𝐼 𝑠𝑡𝑑 , exiting program. (10). Putting 𝐿 𝑝 [𝑖 -1], 𝐿 𝑝 [𝑖] into the set 𝑆 𝑝 , putting the 𝐿 𝑥 [𝑖 -1], 𝐿 𝑥 [𝑖] into 𝐼 𝑥 , executing 𝑖 = 𝑖 + 2, go to (3). The set 𝑆 𝑝 was the efficient point set near the line of sight at the end of the algorithm. The evaluation of the 𝑛 1 , 𝑛 2 were 4,5 commonly. For the peculiar point cloud data, some small adjustment also could be done. In the Figure 4, the red point set expressed the result of the efficient point set extraction near the line of sight.

Fig. 4

 4 Fig.4 The result of the efficient point set extraction near the line of sight

Fig. 6

 6 Fig.6 Interactive measurement of point

Table I

 I Experimental verification of screen pixel coordinates point-to-surface point map

	3	-52.98	18.29	136.58	-52.4159	19.09847	137.0976	1.113435
	4	-49.19	12.52	116.09	-48.1335	13.99432	116.9974	2.028112
	5	-37.67	7.37	101.52	-37.2543	7.99218	101.8187	0.805715
	6	-24.03	5.63	81.85	-23.5856	6.558495	82.29905	1.123049
	7	-33.52	3.68	83.44	-32.4752	4.144433	84.60859	1.634903
	8	-13.76	1.7	45.37	-12.7095	1.78187	45.72393	1.111563
	9	-0.53	1.58	8.44	0.342988	2.501845	9.016809	1.394495
	10	-16.4	-1.71	44.72	-16.0489	-0.42301	45.43838	1.515156
			Fastscan		Mapping method in this paper
	Serial number	x(mm)	y(mm)	z(mm)	x(mm)	y(mm)	z(mm)	Error(mm)
	1	-93.03	41.06	208.84	-92.9382	42.48706	209.3463	1.51698
	2	-72.27	27.55	168.54	-72.2284	27.91212	169.2688	0.814823

Table II

 II The verification of interactive point selection with angle of slight is 0 degrees and 20 degrees TableIIIThe verification of interactive point selection with angle of slight is 0 degrees and 20 degrees

	Serial		0 o				10 o
	number	x(mm)	y(mm)	z(mm)	x(mm)	y(mm)	z(mm)	Error(mm)
	1	-92.938	42.487	209.346	-92.083	42.793	209.082	0.948
	2	-72.228	27.912	169.269	-72.062	28.466	169.009	0.634
	3	-52.416	19.099	137.098	-52.542	18.906	137.786	0.725
	4	-48.134	13.994	116.997	-47.982	13.460	116.648	0.656
	5	-37.254	7.992	101.819	-36.953	7.862	101.332	0.587
	6	-23.586	6.558	82.299	-23.459	6.893	83.078	0.857
	7	-32.475	4.144	84.600	-31.813	4.848	83.938	1.176
	8	-12.7095	1.782	45.724	-12.049	2.732	44.968	1.382
	9	0.343	2.5018	9.0168	-0.240	2.286	8.540	0.784
	10	-16.049	-0.423	45.438	-15.319	-0.668	45.156	0.820
				0 o				20 o
	Serial number
		x(mm)	y(mm)	z(mm)	x(mm)	y(mm)	z(mm)	Error(mm)
	1	-92.9382	42.48706 209.3463 -93.1475	42.12267 210.6366 1.356964
	2	-72.2284	27.91212 169.2688 -72.3269	28.22446 169.8735 0.687665
	3	-52.4159	19.09847 137.0976 -51.0175	20.17961 136.6038 1.835253
	4	-48.1335	13.99432 116.9974 -48.4386	13.57301 117.768	0.929734
	5	-37.2543	7.99218	101.8187 -36.2038	8.447684 100.6524 1.634433
	6	-23.5856	6.558495 82.29905 -23.504	6.636894 82.53445 0.261196
	7	-32.4752	4.144433 84.60859 -32.5588	3.288491 83.80317 1.178269
	8	-12.7095	1.78187	45.72393 -13.1347	2.014106 44.55533 1.26504
	9	0.342988 2.501845 9.016809 0.962779 2.533851 9.861952 1.048539
	10	-16.0489	-0.42301	45.43838 -15.8339	-0.59462	45.10651 0.43108

Acknowledgements: This study has been funded by China National 863 Plans Projects (Contract Number: 2008AA10Z220) and key scientific and technological project of Henan Province (Contract Number: 082102140004).


Hang Zhang

Xi Qiao

Zhenbo Li

Daoliang Li

Effective wavelengths selection of hyperspectral images of plastic films in cotton

Keywords: Hyperspectral imaging, difference spectra, principal component analysis, wavelengths selection

This research was conducted to investigate the application of detecting plastic films in cotton using visible and near-infrared hyperspectral imaging. A line-scan hyperspectral imaging system (326-1100 nm) was used to detect plastic films mixed with cotton which was an important quality issue. Hyperspectral reflectance images were acquired and difference spectra of cotton and plastic films were extracted and analyzed to determine the dominant wavelengths. Also, as one of the most commonly used methods for dimensionality reduction, principal component analysis (PCA) was chosen to process the hyperspectral images. Afterwards, effective wavelengths were selected by analyzing the first three principal components (PCs) and six single-band images at 473.24 nm, 497.29 nm, 530.6 nm, 670.81 nm, 674.71 nm, and 955.68 nm were extracted respectively. Finally, the selected wavelengths were validated to prove the effectiveness. The results indicated that the selected wavelengths could be able to detect plastic films in cotton instead of the whole wavelengths.

Introduction

The quality of cotton products is easily and seriously affected by foreign matter mixed into cotton during spinning, weaving, and dyeing. As one kind of foreign matter, plastic films are widely used to preserve soil temperature and moisture in China when growing cotton and make it a problem that a great many plastic films might be mixed with cotton when harvesting. They are easily to be broken into lots of very small pieces during spinning [START_REF] Fang | A hybrid approach for efficient detection of plastic mulching films in cotton[END_REF] , which make removal of them difficult and make breakability of cotton yarn increase. Therefore, it is crucial for us to detect and remove plastic films in cotton rapidly and accurately.

For the past two decades, computer vision technique has been applied to identify bark in cotton and determine the gravimetric bark content in cotton [START_REF] Lieberman | Determining gravimetric bark content in cotton with machine vision[END_REF] , conduct automated visual inspection of cotton quality [START_REF] Tantaswadi | Machine vision for automated visual inspection of cotton quality in textile industries using color isodiscrimination contour[END_REF] , measure interlace of intermingled and false-twist textured yarns [START_REF] Millman | Computer vision for textured yarn interlace (nip) measurements at high speeds[END_REF] , detect structural defects in textiles [START_REF] Abouelela | Automated vision system for localizing structural defects in textile fabrics[END_REF] , inspect and classify different types of foreign fibers [START_REF] Yang | A new approach for image processing in foreign fiber detection[END_REF][START_REF] Li | Classification of foreign fibers in cotton lint using machine vision and multi-class support vector machine[END_REF] , etc. Furthermore, research has also been conducted on plastic films. Fang et al. [START_REF] Fang | A hybrid approach for efficient detection of plastic mulching films in cotton[END_REF] used an online visual detection machine to acquire images of plastic films and proposed a new method to identify plastic films. The result showed that the prosed method improved the detection rate of plastic films and reduced the negative identification accuracy. Nevertheless, the average identification accuracy was still not very high (43.33%). In order to detect white foreign fibers (such as white plastic bags, white cotton cloth, transparent plastic films, etc.), Hua et al. [START_REF] Hua | White Foreign Fibers Detection in Cotton Using Line Laser[END_REF] constructed a linear laser imaging system and carried out a series of experiments. The results indicated that it's still difficult to detect transparent plastic films although the contrast between object and background was increased effectively. Liu et al. [START_REF] Liu | A laser imaging method for machine vision detection of white contaminants in cotton[END_REF] used linear laser cross-sectional imaging to distinguish typical white contaminants from cotton. 12 types of white contaminants including semitransparent plastic mulch and white plastic film were used in their experiment. The result indicated the detection rate was 97% for white plastic films and 95% for semitransparent plastic mulch.

As a promising technique, hyperspectral imaging has been used for detection of bruises on apples [START_REF] Lu | DETECTION OF BRUISES ON APPLES USING NEAR -INFRARED HYPERSPECTRAL IMAGING[END_REF][START_REF] Xing | Detecting Bruises on 'Golden Delicious' Apples using Hyperspectral Imaging with Multiple Wavebands[END_REF] , canker lesions on citruses [START_REF] Qin | Citrus canker detection using hyperspectral reflectance imaging and PCA-based image classification method[END_REF] , sprout damage and Fusarium head blight in wheat [START_REF] Xing | Detection of sprout damage in Canada Western Red Spring wheat with multiple wavebands using visible/near-infrared hyperspectral imaging[END_REF][START_REF] Barbedo | Detecting Fusarium head blight in wheat kernels using hyperspectral imaging[END_REF] , common defects on oranges [START_REF] Li | Detection of common defects on oranges using hyperspectral reflectance imaging[END_REF] , etc. However, very few researches have been reported for the application of this technique to evaluate cotton quality. To our knowledge, Guo et al. [START_REF] Guo | Detection of foreign materials on the surface of ginned cotton by hyper-spectral imaging[END_REF] firstly developed a hyperspectral imaging system in reflectance mode (422 nm-982 nm) to detect different trash on the surface of cotton including fine, colorless, light color, and white foreign matter. Nevertheless, the transparent polyethylene mulching films' recognition rates of training sets and test sets were only 41.7% and 53.8% respectively.

Furthermore, there are still some disadvantages for hyperspectral imaging technique to consider, such as higher cost and lower speed compared to conventional machine vision systems. Therefore, it is still important to investigate hyperspectral imaging for detecting plastic films in cotton and determine the effective wavelengths that can best characterize the features of plastic films.

Materials and methods

Samples

Cotton samples and plastic films with different sizes and shapes were collected from a local cotton processing plant. The cotton samples were made to flat layers and the length and width of the cotton layer were 255 mm and 175 mm, respectively. Some plastic films were put on the surface of the cotton layer randomly and the others were put into the cotton layer at the depth of 1-6 mm. The image of the plastic films was shown in Figure 1.

System setup

The assembled hyperspectral imaging system (Figure 2) consisted of a prism-grating-prism imaging spectrograph (ImSpector V10E), a standard 23 mm C-mount zoom lens, an EMCCD camera, two 150-watt halogen lamps, an electric displacement platform, an image capture software, and a computer with a frame grabber. The scanning speed was adjusted at 0.8 mm/s. The object distance was set to 45 cm throughout the test. The exposure time was 20 ms with a frame rate of 16.47 fps to build hyperspectral images (also called hypercube). The hyperspectral images were acquired at 1000 wavelength channels in the wavelengths from 326.7 nm to 1098 nm with an increment of 0.78 nm line by line. The image acquisition parameters were set by the Spectral Image and raw hyperspectral images with 1004×1420 pixles were obtained.

Image correction

In order to correct the raw images, a dark image and a white reference image were acquired seperately. The equation below was used to correct the raw image:

I C = I R -I D / I W -I D (1)
In the equation, I R is the raw image, I W is the white reference image, I D is the dark image and I C is the corrected image. After image correction, the images at the first 170 wavelengths and last 80 wavelengths were cropped for their high level of noise and the size of hyperspectral images was then reduced to 600×400 pixels at 750 wavelength channels. A sample data cube of the corrected hyperspectral images was shown in Figure 3.

Wavelength selection

The regions of interest (ROIs) of hyperspectral images were used to extract spectra of cotton and plastic films and mean spectra that used for following processing were computed from the selected ROIs [START_REF] Qin | Multispectral Detection of Citrus Canker Using Hyperspectral Band Selection[END_REF] . Then, difference spectra were calculated by subtracting the cotton spectra from plastic films spectra. The difference between the spectra of cotton and plastic films was compared and the wavelengths with local maxima were selected. Multivariate analysis methods were also usually used for the reduction of redundant information of hyperspectral data. As one of the mostly used statistical tools, principal component analysis (PCA) was adopted for dimensionality reduction by transforming original data (usually high correlated) into a new set of uncorrelated images that were called principal components (PCs) images. PCA loadings or eigenvectors were different among PCs images and the local maxima and/or local minimum corresponding wavelengths could be chosen as the determinative wavelengths. Most of the information could be carried by the images with selected effective wavelengths and might be more efficient for the following data processing [START_REF] Wold | Atlantic Salmon Average Fat Content Estimated by Near-Infrared Transmittance Spectroscopy[END_REF] .

Results and discussion

Difference spectra between cotton and plastic films

8 ROIs chosen from different locations on cotton and plastic films of every hyperspectral images in the 450-1035 nm region were selected to extract the spectral characteristics. The elliptic ROI areas with different sizes (about 80 to 200 pixels) were selected as representative areas of spectral reflectance. The extracted spectra of cotton and plastic films were averaged respectively and the average reflectance intensities from images in cotton were higher than those in plastic films. Then, the mean spectra of cotton were subtracted from the mean spectra of plastic films to obtain the difference spectra. As shown in Figure 4, there were two local maxima at 497.29 nm and 670.81 nm indicating that the reflectance difference values of cotton and plastic films in these two wavelengths had large intensities and the two wavelengths could be selected as the dominant wavelengths. Thus, the images obtained from the two wavelengths could be used for further analysis.

PCA on the whole wavelengths

The hyperspectral images after correction were subjected to PCA using the wavelengths in the spectral ranges (450-1035 nm). The representative PC1 to PC3 obtained from PCA accounted for over 96 percent of the total variance of all bands for the images of plastic films, thus the first three PC images could be an alternative to substitute the raw images for image processing and data analysis. Also, PCA could be used to conduct dimensionality reduction of hyperspectral images.

As shown in Figure 5, the resultant PC images indicated that the major features of plastic films became more evident, especially PC1 images. PC1 images represented the gray value information of the sample and provided more features of plastic films to make them be clearly identified. The plastic films in the PC2 images were brighter than those in the PC1 images. PC3 images were much noisy than the PC1 and PC2 images. PC2 images and PC3 images gave some other useful features for detection as well. The rest PCs depicted other features. However, starting from PC4 image, the transformed images did not give more meaningful information to detect plastic films.

Selection of effective wavelengths

Based on the analysis results of PCA, several wavelengths of the hyperspectral images could be selected as the effective wavelength and the corresponding images could be used to represent the whole hyperspectral images.

The average weighing coefficients (eigenvectors) for the first three PCs of the samples were obtained and plotted in Figure 6. A total of five effective wavelengths that depicted as local minimum and maximum were selected, which were centered at 473.24 nm, 530.6 nm, 670.81 nm, 674.71 nm, and 955.68 nm, respectively. As aforementioned, 497.29 nm and 670.81 nm were the other two effective wavelengths that were selected in the difference spectra analysis. It could be found that the wavelength of 670.81nm was selected in the two different methods. Therefore, six wavelengths were chosen for analysis. It's quite obvious that five of the six selected wavelengths were in the visible spectral region suggesting that the visible region of spectrum (mainly the wavelengths in red and green regions) was critical for the identification. On the other hand, one near-infrared wavelength (955.68 nm) was also selected as the effective wavelength indicating that the near-infrared spectral region might have the possibility to detect plastic films in cotton. Further research could be conducted in the whole near-infrared spectral region to verify the feasibility.

Validation of selected wavelengths

PCA was then conducted on the six effective wavelengths (473.24 nm, 497.29 nm, 530.6 nm, 670.81 nm, 674.71 nm, and 955.68 nm). Figure 7 showed the first three PC images. As illustrated in Figure 7, PC1 and PC2 images were similar to those PC images previously obtained based on analysis of the full wavelength range although PC3 image was not quite the same to the corresponding one shown in Figure 5. Also, the first two PCs explained almost 98% (PC1: 92.09% and PC2: 5.92%) of the variance indicating that the selected wavelengths could be efficiently used for plastic films detection in cotton. And the selected wavelengths could also be used to construct a multispectral imaging system to detect plastic films in cotton in real time. Hyperspectral reflectance images was acquired for the detection of plastic films in cotton and a new wavelength selection method was proposed by combining difference spectra analysis with principal component analysis.

Hyperspectral images were firstly acquired and corrected. The ROIs were then selected and subjected to difference spectra analysis and two local maxima at 497.29 nm and 670.81 nm were chosen because the reflectance difference values of cotton and plastic films in these two wavelengths had large intensities. Then, the hyperspectral images were subjected to PCA and the effective wavelengths were obtained by analyzing the first three PCs. Afterwards, six single-band images at 473.24 nm, 497.29 nm, 530.6 nm, 670.81 nm, 674.71 nm, and 955.68 nm were extracted respectively. Finally, the selected wavelengths were validated by using PCA and the effectiveness was proved. Furthermore, multispectral imaging systems could be developed using the selected wavelengths to lower the cost and enhance the speed.

Fig. 1 .

 1 Fig. 1. Image of the plastic films.

Fig. 2 .

 2 Fig. 2. Schematic of the hyperspectral imaging system.

Fig. 3 .

 3 Fig. 3. Data cube of a hyperspectral image of plastic films in cotton.

Fig. 4 .

 4 Fig. 4. Difference spectra of cotton and plastic films and hyperspectral reflectance images at 497.29 nm and 670.81 nm.

Fig. 5 .

 5 Fig. 5. The first three PC images obtained using the whole wavelengths

Fig. 6 .

 6 Fig. 6. Weighing coefficients (eigenvectors) of first three PCs obtained from principal component analysis.

Fig. 7 .

 7 Fig. 7. The first three PC images obtained using the six effective wavelengths 4 Conclusion

Acknowledgments

This work was supported by the National Natural Science Foundation of China (31228016).


Lei Guo

Dongling Zhao
email: bzhaodongling@cau.edu.cn

Rui Zhang

Meng Du

Zhixiao Li
email: elizhixiao91@cau.edu.cn

Xiang Wang
email: fwangx@eq-he.an.cn

Yaru Wang

Study on Methods of Extracting New Construction Land Information Based on SPOT6

Keywords: SPOT6, image fusion, SVM, multi-window texture, new construction land

SPOT6 is a new remote sensing satellite launched in 2012, with high spatial resolution and strong data acquisition ability. However, a complete data preprocessing technology for the regulation of land resources has not yet been formed. According to the characteristics of SPOT6 satellite images, four different image fusion methods --Gram-Schmidt, HPF, PanSharpand PanSharpening were selected to conduct the comparison experiment by using the software platforms of ENVI, ERDAS and PCI. We evaluate the results' performances from 3 different aspects. First, evaluating the image quality of experiment results qualitatively, then assessed quantitatively by establishing evaluation indexes including mean, standard deviation, information entropy, average gradient and correlation coefficient. Finally, evaluating the applicative effect of fused images based on the classification accuracy. The analysis results shows that the method of PanSharp is best to extract construction land information. Based on the PanSharp fusion image, in order to obtain the texture information under different scales, the authors screened the texture features according to Shannon entropy, and then used distance-based approach J-M to calculate the separation for choosing the optimal texture window. Once got the texture information, combining it with the original image to participate in the multi-scale image classification. The research result showed that multi-window texture participation in classification can improve separation of objects. Finally we extract construction land information with the method of SVM .This study may provide the technical support for application of SPOT6 image in the land resources management.

Introduction

Construction land refers to basic premises of all social and economic activities people engaged in, including residential land, roads, land use, public service area (excluding Greenland and water) [START_REF] Bouziani | Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge [J][END_REF] . With China's rapid development of industrialization and urbanization construction land most prone to change, especially in the last 30 years, with the rapid expansion and he sprawl phenomenon of construction land, causing decreased utilization of land resources, reduction of arable land resources, environmental pollution and ecological destruction and other issues. Therefore, it has important significance for the rational development of urban planning programs, socio-economic and resource use and sustainable development of ecological environment to obtain new construction information timely and accurately.

For high-resolution image information extraction, it can't rely solely on spectral characteristics, but also greater use of spatial information of remote sensing images. In recent years, the use of auxiliary image texture information of spectral classification in order to enhance the classification accuracy is becoming a hot topic of remote sensing information extraction areas [START_REF] Huang Yan | A study of the optimal scale texture analysis for remote sensing image classification[END_REF] . Based on the research of suitable fusion methods of extracting construction land information, this paper tries to use the object-oriented SVM classification combined with multi-window texture and spectral information to extract new construction land information.

Materials and Methods

Study Area and Data

The study area was located in spa town, Haidian District, Beijing of China, with the controlled area of about 33.32km The data sources of study area included SPOT 6 satellite data obtained on January 25th, 2013 and a topographic map with the size 1/2000 obtained on 2008. The vegetation coverage of image is low, some places have snow cover.

The Principle of Support Vector Machine

SVM method is based on the VC dimension theory and structural risk minimization principle of statistical learning theory. Based on the limited sample information, finding the best compromise between complexity of the model and learning ability, in order to obtain the best generalization ability [START_REF] Huang | Classification of high spatial resolution remotely sensed imagery based upon fusion of multi-scale features and SVM [J][END_REF][START_REF] Lei | Semi-supervised Collaborative Classification for Hyperspectral Remote Sensing Image with Combination of Cluster Feature and SVM[J][END_REF] .

By learning algorithm, SVM can automatically find the support vector that have a greater ability to distinguish, then construct a classifier that can maximize the interval between classes, so there is a good promotion and a higher classification accuracy.

For simple binary classification problem, SVM is to find an optimal split plane that can be adjusted as correctly as possible to separate two kinds of data with wider distance. The basic idea is illustrated by the following figure. According to the maximum interval method, we require not only the classification lines separate the two classes of samples correctly, but also should make the interval of classification is max; extended to high-dimensional space, the optimal classification line can be extended to the optimal hyperplane.

Test flow

The test procedure of this research mainly includes pre-processing of images (orthorectification, image fusion and image clipping), extracting of texture feature, combination of multi-source information, classification of images based on SVM (multi-scale segmentation ,parameter setting of the value of C and γ, etc.) and accuracy of evaluation. The test flow chart is expressed as Fig. 2. In this paper, data preprocessing process is mainly focused on the choice of fusion methods.

Image fusion can compensate for the lack of information in a single image, take advantage of a variety of images, get multi-faceted feature information, is a crucial step of information extraction [START_REF] Huang He | Research on fusion of mapping satellite-1 image and its evaluation[END_REF] . According to the characteristics of SPOT6 satellite images, four different image fusion methods of Gram-Schmidt, HPF, PanSharp and PanSharpening were selected to conduct the experiment of comparison. For evaluating the results' performances, we compare them from 3 aspects. The image quality of experiment results was evaluated qualitatively, and also assessed quantitatively by establishing evaluation indexes including mean, standard deviation, information entropy, average gradient and correlation coefficient. The applicative effect of fused images was also evaluated based on the evaluation of the classification accuracy, the results are shown in figure 3. The analysis results show that it is better to choose the image fusion methods of PanSharp to extract construction land information.

Fig. 3. Image classification overall accuracy and Kappa coefficient 2.5 Texture Feature Selection

Accuracy and precision of analysis will improve coupled with texture information on the basis of spectral information on the original image in the remote sensing thematic information extraction. GLCM can describe spatial distribution and structural characteristics of grayscale of each pixel in the image. There were advantages in improving the effect of ground objects classification in images by using texture of images. Then the texture feature of the first principal component can be extracted according to gray-level co-occurrence matrix (GLCM) which is proposed by Haralick, et al. [START_REF] Mariz | Fusion of multi-spectral SPOT-5 images and very high resolution texture information extracted from digital orthophotos for automatic classification of complex Alpine areas [J][END_REF] . These texture statistics can be classified combined with multi-spectral remote sensing image. And each texture statistics can be involved in the calculation along with gray value of each band, etc, as a basis for classification [START_REF] Meng-Meng | SVM land Cover classification based on spectral and textural ferture using stratified samples[END_REF] . There are 8 GLCM texture statistics which were often used in the 14 kinds , including mean, standard deviation, homogeneity, contrast, dissimilarity, entropy, angle second moment, correlation.

By using principal component analysis (PCA) while extracting the texture feature of SPOT 6 image, the result shows that the cumulative contribution of the first principal component is 95.73%,which represents the high and low frequency part of 4 bands of these images. The outcome not only has the effect on descending dimension and descending dimension, but also can incarnate the texture feature of land objects and basic tonality feature [START_REF] Jin-Lian | Extraction of Texture Feature and Identification Method of Landuse Informati on from SPOT5 Image [J][END_REF] . Then the 8 GLCM texture statistics of the first principal component can be extracted. Based on Shannon entropy and visual judgment, we compare and analysis eight kinds of texture features. In this paper, we calculate the information entropy of texture features in 3 × 3 window as example, the results are shown in Table 1. As can be seen from Table 1, the mean of the highest entropy, provide the most abundant information; homogeneity degree and entropy followed, can also provide a wealth of information. From visual judgment, although the mean entropy is high, but the edge of the texture image is not clear, there is no prominent feature of the border (figure 4), therefore excluded. So we chosen homogeneity and entropy involved in classification.

The Determination of Texture Window

Research shows that, when the sliding window is larger than 21 x 21, it will be difficult to reflect object properties using the texture features we extracted, so the window size of 21 x 21 is chosen as the largest sliding window [START_REF] Xi | Classification of High Spatial Resolution Remotely Sensed Imagery Based Upon Fusi on of Multiscale Features and SVM [J][END_REF] . The moving window size is set to 3 x 3, 5 x 5, 7 x 7, 9 x 9, 11 x 11, 13 x 13, 15 x 15, 17 x 17, 19 x 19, 21 x 21 in succession, in order to compare and analyze texture feature at different scales of windows.

As different land objects feature extraction has different optimal texture scale, therefore need to filter the most suitable texture window [START_REF] Feng | Object-oriented Building Extraction of Multisource Remote Sensing Imagery Based on SVM[END_REF] . In this paper, we choose the index J-M distance to calculate the separability. This paper, we are mainly for extract newly-added construction land, the types of land use in the study area is: vegetation covered area (including vegetation cover cultivated land, woodland), dark place (including water, shadow), construction land (including buildings, roads), bare land and snow all 5 categories. Figure 5, figure 6 shows the curve of class separability (J-M distance) with different texture windows. 6 show that for the texture features include entropy and homogeneous of SPOT6 images, all classes were not presented in increasing or decreasing trend. For the homogeneous, vegetation, bare land, snow, construction land can be divided good in the 15 × 15 window, the separability of dark place in 9 × 9 window reached the highest. For the texture features entropy, the separability of all classes in 15 × 15 window is highest, and reached the minimum in the window 21 × 21. So for the SPOT6 image, selected texture feature homogeneous in 9 × 9 and 15 × 15 window and entropy in 15 × 15 window take part in the classification. Multiwindow texture take part in classification can improve the separability of different types of objects effectively.

The Determination of SVM Kernel Function and Parameters

This study selected the RBF kernel function as SVM kernel function, because the RBF kernel can map samples into a higher dimensional space, can deal with case when the relationship between class label and feature is nonlinear ; and is applied for low dimension, high dimension, small sample, large sample. It has wide convergence domain, is kind of ideal classification function [START_REF] Wu | Classification of quickbird image with maximal mutual information feature selection and support vector machine[END_REF] . The kernel parameter and penalty factor C are two necessary parameter of RBF kernel function. This paper we use cross validation algorithm to determine the two parameters, it means the selected training samples will be divided into an equal number of N subset, and make the N -1 groups as a model of training samples, the rest of the group as the test sample, make use of the test sample to test and verify the classification results accuracy of n-1 part data, by changing C and γ, we looking for higher sample classification accuracy [START_REF] Ling-Min | Study on Multi-source Remote Sensing Images Classification with SVM [J][END_REF] . This paper we choose the parameters selection model Grid. py which provided by the software libsvm 2.83 to search C and γ. The final determination of C=32, γ =0.0001.

3

Results and Analysis

The classification results

Combining the optimal texture characteristics and index characteristics Image recognition ability for dark place and vegetation are better than that of bare land and the construction land, construction land and bare land have a certain degree of mixing. SVM classification capability of roads and other linear features is good, even smaller rural road can also be identify effectively.

The extraction results of new construction land

Before extracting new construction land, we need deal with classification results. There were some shadows of building existing in class of dark place. Using the neighborhood characteristics of eCognition, set the threshold, those can be classified into construction land; and merge adjacent segmentation map spot. According to the minimum on map spot, those witch area less than 666.67 square meters will be deleted. Finally, make differential operation to classification results which were treated and base land use map. The extraction results are shown below.

Fig.8. new construction extraction results

Statistical results of new construction land are shown in Table3. The accurate rate of extraction is more than 80%, achieved superior extraction effect, but there is a certain degree of error and omission phenomenon. Among those new construction land which extracted , the bare land divided into construction land had high misclassification rate, which may be related to the image itself , cause the SPOT6 image as the winter image, vegetation coverage is low, may result in the impact on the classification results.

Conclusion and Discussion

(1) Image fusion can compensate for the lack of information in a single image, take advantage of a variety of images, get multi-faceted feature information, is a crucial step of information extraction. The most suitable fusion method for the extraction of construction land was selected by quality evaluation system of fusion image. For SPOT6 image, select the fusion method of PanSharp can improve the extraction accuracy of construction land.

(2) Multi-window texture features take part in image classification can improve the class separability and classification accuracy effectively. SPOT6 image, choose the texture features homogeneity of 9×9 window and 15×15 window, choose the texture features entropy of 15×15 window.

(3) The classification method of SVM can solve the small sample, nonlinear and high dimensional problems, obtain global optimal solution, it shown the superiority in the application of road and building.

(4) The SPOT6 image in this paper was winter data, vegetation coverage rate was low, and there are snow covered, this may cause certain effect on the extraction of construction land information. We should select more data to verify the reliability of this method.

Fig. 1 .

 1 Fig.1. optimal hyperplane Solid point and hollow point of figure s represent two categories of training samples; H is classified line; the distance between H1 and H2 are classified intervals.According to the maximum interval method, we require not only the classification lines separate the two classes of samples correctly, but also should make the interval of classification is max; extended to high-dimensional space, the optimal classification line can be extended to the optimal hyperplane.

Fig. 2 .

 2 Figure of classification

Fig. 4 .

 4 Fig. 4. Texture features of SPOT6 image (partial)

Fig. 5 .Figure 5

 55 Figure5and Figure6show that for the texture features include entropy and homogeneous of SPOT6 images, all classes were not presented in increasing or decreasing trend. For the homogeneous, vegetation, bare land, snow, construction land can be divided good in the 15 × 15 window, the separability of dark place in 9 × 9 window reached the highest. For the texture features entropy, the separability of all classes in 15 × 15 window is highest, and reached the minimum in the window 21 × 21. So for the SPOT6 image, selected texture feature homogeneous in 9 × 9 and 15 × 15 window and entropy in 15 × 15 window take part in the classification. Multiwindow texture take part in classification can improve the separability of different types of objects effectively.

  (normalized differential vegetation index, NDVI, normalized differential water index and normalized construction index) as the band and then merge with multi spectral image, participate in image classification. After repeated experiments, determining the parameters of multi-scale segmentation are: segmentation scale 100, shape parameter 0.4 compactness parameter 0.6, the segmentation results can basically ensure most object boundary extraction correctly. Using visual judgment and GPS select site of typical sample. Finally we collected 305 samples altogether, 175 of which were used as training samples, the other 130 as the test sample. According to the training samples, select the SVM RBF kernel function make object-oriented classification, the classification results are shown in figure 7.

Fig. 7 .

 7 Fig.7. SVM classification results of SPOT6 imagesTo evaluate the classification results accuracy effectively, classification accuracy as shown in the table2:

Table 1 .Information entropy eight texture features

 1

	Mean	Standard deviation	Homogeneity	Contrast	Dissimilarity	Entropy	Moment Angular Second	Correlation
	2.21	0.25	2.18	0.23	1.77	1.99	1.88	0.47

Table 2 .The form of SVM classification accuracy evaluation

 2

		Dark	Vegeta-	Bare	Constru-			Customer
		place	tion	land	tion land	Snow	Total	accuracy
								(%)
	Dark place	21	0	1	1	0	22	95.45
	Vegetation	0	17	4	0	0	21	80.95
	Bare land	0	2	17	6	1	26	65.38
	Construction	4	1	6	45	5	61	73.77
	land
	Snow	0	0	0	0	2	2	100.00
	Total	25	20	24	51	8
	Producer	84.00	85.00	70.83	88.24	25.00	84.00
	accuracy
	(%)
				Overall accuracy:79.70%
				Kappa coefficient:0.717

Table 3 . Statistics of new construction

 3

	Types	The number of	Are of polygons(km 2 )	Percentage of
		polygons		area(%)
	Construction land	38	117.62	88.61
	Bare land	12	10.35	7.80
	Dark place	5	4.17	3.14
	Snow	2	0.60	0.45
	Total	57	132.74
	Misclassification			11.39
	rate
	Leakage rate			3.77

Acknowledgment

This research was founded by the Beijing Municipal Science and Technology Projects (Z141100000614001), and the projects of Hebei Provincial Seismological Bureau (D Z20150428102, DZ2015017012).