基于Radarsat-2的水稻种植面积提取
发布时间：2018-11-07 09:11
【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%。
[Abstract]:From July 23 to October 27, 2013, five Radarsat-2 images with a resolution of 5.2 m 脳 7.6 m were selected as data. The rice planting area was extracted by using support vector machine (SVM) and maximum likelihood method (MLC), respectively. The accuracy was verified by ground measured GPS rice square. The results showed that the precision of rice area extraction by SVM and MLC methods reached the highest on September 9. Therefore, the effect of farmland optimization and small patch removal on the precision was studied based on the results of rice area extraction on September 9th. The precision of rice area extraction was improved significantly through the optimization of cultivated land and small patch removal, and the SVM method increased from 72.876% to 95.482MLC from 74.224% to 91.792%.
【作者单位】： 江苏省农业科学院农业经济与信息研究所;
【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)
【分类号】：S127;S511
本文编号：2315879
[Abstract]:From July 23 to October 27, 2013, five Radarsat-2 images with a resolution of 5.2 m 脳 7.6 m were selected as data. The rice planting area was extracted by using support vector machine (SVM) and maximum likelihood method (MLC), respectively. The accuracy was verified by ground measured GPS rice square. The results showed that the precision of rice area extraction by SVM and MLC methods reached the highest on September 9. Therefore, the effect of farmland optimization and small patch removal on the precision was studied based on the results of rice area extraction on September 9th. The precision of rice area extraction was improved significantly through the optimization of cultivated land and small patch removal, and the SVM method increased from 72.876% to 95.482MLC from 74.224% to 91.792%.
【作者单位】： 江苏省农业科学院农业经济与信息研究所;
【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)
【分类号】：S127;S511
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