Swarm Intelligence Approach to Wavelet Design for Hyperspectral Image Classification

Abstract

Wavelets are known to be a valuable tool for analyzing hyperspectral images. In this letter, we propose to further improve their performance by means of a novel classificationdriven design scheme that aims at deriving a wavelet that best represents in terms of between-class discrimination capability the spectral signatures conveyed by a given hyperspectral image. This is achieved by adopting a polyphase representation of the wavelet filter bank and formulating the wavelet optimization problem within a particle-swarm-optimization (PSO) framework. Experimental results show that the proposed wavelet design method outperforms the popular Daubechies wavelets whatever the classifier type adopted in the classification process.

DOI: 10.1109/LGRS.2009.2026191

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Cite this paper

@article{Daamouche2009SwarmIA, title={Swarm Intelligence Approach to Wavelet Design for Hyperspectral Image Classification}, author={Abdelhamid Daamouche and Farid Melgani}, journal={IEEE Geosci. Remote Sensing Lett.}, year={2009}, volume={6}, pages={825-829} }