Fine classification of rice with multi-temporal compact polarimetric SAR based on SVM+SFS strategy
Xianyu GUO1, Kun LI2(), Zhiyong WANG1, Hongyu LI3, Zhi YANG4
1. College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China 2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China 3. School of Earth Science and Resources, China University of Geosciences(Beijing), Beijing 100083, China 4. Institute of Transmission and Transformation Engineering, China Electric Power Research Institute, Beijing 100055, China
Different types and planting methods can result in the discrepancy of rice growth and yield. It is of great importance to provide accurate growth vigor information for rice growth monitoring and estimation using fine distinction of different rice varieties and planting methods. As a new type of SAR sensor, compact polarimetry synthetic aperture Radar (CP-SAR) provides the possibility for the fine mapping of paddy land with abundant polarimetric information and large width. In this study, the authors firstly used RADARSAT-2 fully polarimetric SAR data to simulate CP-SAR data and extracted 22 types of feature parameters. In addition, on the basis of the multi-dimensional feature information CP - SAR data, the support vector machine and sequential forward selection (SVM + SFS) strategy were performed for feature selection, and the optimal feature subset of paddy land fine classification was obtained. Moreover, the decision tree and SVM method were used for paddy land fine classification based on feature subset. The results show that paddy land fine classification method based on decision tree can achieve better classification results. The overall classification precision and Kappa coefficient of optimal feature subset respectively are 92.57% and 0.896, which are higher than those of the set of all feature parameters by improving 1.2% in overall classification precision and 0.016 in Kappa coefficient.
国贤玉, 李坤, 王志勇, 李宏宇, 杨知. 基于SVM+SFS策略的多时相紧致极化SAR水稻精细分类[J]. 国土资源遥感, 2018, 30(4): 20-27.
Xianyu GUO, Kun LI, Zhiyong WANG, Hongyu LI, Zhi YANG. Fine classification of rice with multi-temporal compact polarimetric SAR based on SVM+SFS strategy. Remote Sensing for Land & Resources, 2018, 30(4): 20-27.
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2018, Vol. 30 
), 王志勇1, 李宏宇3, 杨知4
