HIGH RESOLUTION POLSAR IMAGE CLASSIFICATION BASED ON GENETIC ALGORITHM AND SUPPORT VECTOR MACHINE

被引：0
|
作者：
Li, P. X. [1 ]
Sun, W. D. [1 ]
Yang, J. [1 ]
Shi, L. [1 ]
Lang, F. K. [1 ]
Jiang, W. [1 ]
机构：
[1] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China
来源：
3RD ISPRS IWIDF 2013 | 2013年 / 40-7-W1卷
关键词：
PolSAR; Classification; Feature Selection; GA; SVM;
D O I：
暂无
中图分类号：
P9 [自然地理学];
学科分类号：
0705 ; 070501 ;
摘要：
This paper focuses on backscattering mechanisms selection and supervised classification works for CETC38-X PolSAR image. Thanks to the high radar resolution, many classes of man-made objects are visible in the images. So, land-use classification becomes a more meanful application using PolSAR image, but it involves the selection of classifiers and backscattering mechanisms. In this paper we apply SVM as the classifier and GA as the features selection method. Finally, after we find the best parameters and the suitable polarimetric information, the overall accuracy is up to 97.49%. The result shows SVM is an effective algorithm compared to Wishart and BP classifiers.
引用
收藏
页码：67 / 71
页数：5
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