OPTIMIZING WAVELETS FOR HYPERSPECTRAL IMAGE CLASSIFICATION

被引：2

作者：

Daamouche, Abdelhamid ^{[1
]}

Melgani, Farid ^{[1
]}

Hamami, Latifa ^{[2
]}

机构：

[1] Univ Trent, Dept Informat Engn & Comp Sci, Via Sommarive 14, I-38050 Trento, Italy

[2] Ecole Natl Polytech, Algiers 16200, Algeria

来源：

2009 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-5 | 2009年

关键词：

hyperspectral images; image classification; particle swarm optimization (PSO); support vector machine (SVM); wavelet filters;

D O I：

10.1109/IGARSS.2009.5418070

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

This work presents a procedure to optimize a wavelet filter in terms of discrimination capability between the classes characterizing a given hyperspectral remote sensing image To this end, this procedure estimates the coefficients of the wavelet filter bank by means of a particle swarm optimization (PSO) so that to maximize the average Bhattacharyya distance. The obtained experimental results show that PSO-based optimized wavelets can significantly outperform conventional wavelets.

引用

页码：553 / +

页数：2

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