Improved support vector machine and classification for remotely sensed data

ZHANG Rui1; MA Jian-wen1 1.Institute of Remote Sensing Applications; Chinese Academy of Sciences; Beijing 100101; China.Graduate University; Chinese Academy of Sciences; Beijing 100049; China

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Improved support vector machine and classification for remotely sensed data
Improved support vector machine and classification for remotely sensed data
2009年13卷第3期页码：437-444
纸质出版日期： 2009 ，
DOI：

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[1].Improved support vector machine and classification for remotely sensed data[J].遥感学报,2009,13(03):437-444.

Improved support vector machine and classification for remotely sensed data[J]. Journal of Remote Sensing, 2009,13(3):437-444.
[1].Improved support vector machine and classification for remotely sensed data[J].遥感学报,2009,13(03):437-444. DOI：

Improved support vector machine and classification for remotely sensed data[J]. Journal of Remote Sensing, 2009,13(3):437-444. DOI：

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In this paper

the P-SVM algorithm was introduced into multi-spectral/high-spatial resolution remotely sensed data classification and it is applied to classification of ASTER satellite data and ADS40 aerial digital data.The experiments indicate that the P-SVM is at least competitive with the standard SVM algorithm in classification accuracy of remotely sensed data and the time needed is less.

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Improved support vector machine and classification for remotely sensed data

Improved support vector machine and classification for remotely sensed data

DOI：

摘要

Abstract

关键词

Keywords

references