文献类型：会议论文

英文题名：LAND COVER CLASSIFICATION BY SUPPORT VECTOR MACHINES USING MULTI-TEMPORAL POLARIMETRIC SAR DATA

作者：Feng, Qi[1] Chen, Er-xue[1] Li, Zengyuan[1] Guo, Ying[1] Zhou, Wei[1] Li, Weimei[1] Xu, Guangcai[1]

第一作者：Feng, Qi

通信作者：Feng, Q[1]

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China

会议论文集：IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

会议日期：JUL 22-27, 2012

会议地点：Munich, GERMANY

语种：英文

外文关键词：POLSAR; multi-temporal; SVM; land cover

年份：2012

摘要：In order to improve the land cover classification accuracy for SAR image, Support Vector Machine (SVM), which has wide applicability is used on the land cover classification of POLSAR image in this paper. The study site is located in Tahe County, Heilongjiang Province, China, and two scenes of quad-polarization Radarsat-2 SAR images were acquired. the land cover classification of single-temporal POLSAR image by SVM, and multi-temporal POLSAR image by SVM and maximum likelihood classification (MLC) is studied separately. Then all the classification results are evaluated. Some conclusions can be got according to the analysis of all results and accuracy: Firstly, it is difficult to distinguish the different types of vegetation for the similar scattering among them in July. However, water, whose scattering characteristic is simplex, can be distinguished from others easily. Scondly, in October, the scattering characteristics among forest, shrub, grass, crop are different, therefore it is easy to distinguish vegetation because of their one from others in this period. But for water, with reduced in winter, the river width narrows, compared with it in summer, water classification accuracy is lower in this period. Thirdly, joint July and October SAR data for classification, can offset espective their own disadvantages. and improve overall accuracy. And the last one, With the characteristics that different probability density distribution, small sample, non-linear and so on, SVM shows the wide applicability.