文献类型：期刊文献

英文题名：Land cover classification by Support Vector Machines using multi-temporal polarimetric SAR data

作者：Feng, Qi[1,2] Chen, Er-Xue[1,2] Li, Zengyuan[1,2] Guo, Ying[1,2] Zhou, Wei[1,2,3] Li, Weimei[1,2] Xu, Guangcai[1,2]

第一作者：Feng, Qi

通信作者：Feng, Q.

机构：[1] Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; [2] Remote Sensing and Information Technology, State Forestry Administration Key Lab, Beijing 100091, China; [3] Geomatics College, Shandong University of Science and Technology, 579 Qianwangang Road, Qingdao 266510, China

年份：2012

起止页码：6244-6246

外文期刊名：International Geoscience and Remote Sensing Symposium (IGARSS)

收录：EI(收录号：20130615991819)

语种：英文

外文关键词：Probability density function - Synthetic aperture radar - Image classification - Radar imaging - Vegetation - Image enhancement - Remote sensing - Maximum likelihood

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. ? 2012 IEEE.