High Resolution Remote Sensing Image Classification based on SVM and FCM

被引：0

作者：

Li, Qin ^{[1
]}

Bao, Wenxing ^{[1
]}

Li, Xing ^{[1
]}

Li, Bin ^{[1
]}

机构：

[1] Beifang Univ Nationalities, Dept Comp Sci & Engn, Yinchuan 750021, Peoples R China

来源：

PROCEEDINGS OF THE 2015 2ND INTERNATIONAL CONFERENCE ON ELECTRICAL, COMPUTER ENGINEERING AND ELECTRONICS (ICECEE 2015) | 2015年 / 24卷

关键词：

ALOS image; SVM; FCM; textural features;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper proposes a remote sensing image classification method based on multi-feature combination of the support vector machine (SVM) according to the classification problems of the high resolution remote sensing image. ALOS image is operated at two stages by this method. The first stage is to coarsely classify with fuzzy c-means (FCM) algorithm and k-means algorithm, and the second stage is to extract the textural features of the image with gray-level co-occurrence matrix (GLCM). The relevancy is selected to participate in the classification of the SVM. Experiments prove that the method is an effective and feasible remote sensing image classification method.

引用

页码：1271 / 1278

页数：8

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