基于Radarsat-2的水稻种植面积提取  

Paddy rice planting area extraction based on Radarsat-2 data

作　　者：单捷[1] 邱琳[1] 孙玲[1] 王志明[1] 

机构地区：[1]江苏省农业科学院农业经济与信息研究所,江苏南京210014

出　　处：《江苏农业学报》2017年第3期561-567,共7页Jiangsu Journal of Agricultural Sciences

基　　金：国家科技重大专项课题(09-Y30B03-9001-13/15-4);江苏省农业科学院基本科研业务专项课题(ZX-15-3003);江苏省农业科学院基金项目(6111651;6111650);农业部遥感应用中心技术创新课题(2911660)

摘　　要：选用2013年7月23日-10月27日期间5期分辨率为5.2 m×7.6 m的Radarsat-2影像为数据,采用支持向量机法(SVM)和最大似然法(MLC)分别对各时相水稻种植面积进行提取,并以地面实测GPS水稻样方进行精度验证。结果表明SVM和MLC方法的水稻面积提取精度均在9月9日达到最高,所以选择在9月9日的水稻面积提取结果上研究耕地地块优化和碎小图斑去除对精度的影响。通过耕地地块优化和碎小图斑去除处理,水稻面积提取精度显著提高,SVM法由原先的72.876%提高到95.482%,MLC法由74.224%提高到91.792%。The 5 scenes of Radarsat-2 satellite image with spatial resolution of 5.2 m×7. 6 m collected from July 23rd 2013 to October 27th 2013 were used to extract the paddy rice planting area of every scene using support vector machine （ SVM） and maximum likelihood classification （ MLC） . The accuracy was verified by on-site GPS measurement quadrat ar-eas. Since the extration accuracies of both SVM and MLC were the highest on September 9th, the scene extracted on Sep-tember 9th was chosen to study the effect of farmland parcel optimization and pattern spot removal on the accuracy. The ac-curacy of SVM was improved from 72. 876% to 95. 482%, and the accuracy of MLC was improved from 74. 224% to 91. 792%.

关 键 词：遥感 支持向量机 最大似然法 水稻种植面积提取 

分 类 号：S127[农业科学—农业基础科学]