Spectral Feature Selection with Particle Swarm Optimization for Hyperspectral Classification

被引：3

作者：

Li, Jun ^{[1
]}

Ding, Sheng ^{[1
]}

机构：

[1] Wuhan Univ Sci & Technol, Coll Comp Sci & Technol, Wuhan, Peoples R China

来源：

2012 INTERNATIONAL CONFERENCE ON INDUSTRIAL CONTROL AND ELECTRONICS ENGINEERING (ICICEE) | 2012年

关键词：

support vector machine(SVM); Particle Swarm Optimization(PSO); Optimization; Feature Selection;

D O I：

10.1109/ICICEE.2012.116

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：

Spectral band selection is a fundamental problem in hyperspectral classification. This paper addresses the problem of band selection for hyperspectral remote sensing image and SVM parameter optimization. We propose an evolutionary classification system based on particle swarm optimization (PSO) to improve the generalization performance of the SVM classifier. The proposed PSO-SVM algorithm is performed to select the best discriminant features and appropriate SVM parameters for hyperspectral remote sensing imagery simultaneously.

引用

页码：414 / 418

页数：5

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