Title :
Optimizing wavelets for hyperspectral image classification
Author :
Daamouche, Abdelhamid ; Melgani, Farid ; Hamami, Latifa
Author_Institution :
Dept. of Inf. Eng. & Comput. Sci., Univ. of Trento, Trento, Italy
Abstract :
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.
Keywords :
geophysical image processing; image classification; particle swarm optimisation; remote sensing; wavelet transforms; average Bhattacharyya distance; hyperspectral image classification; hyperspectral remote sensing image; particle swarm optimization; wavelet filter bank; wavelet optimization; Discrete wavelet transforms; Filter bank; Finite impulse response filter; Hyperspectral imaging; Hyperspectral sensors; Image classification; Low pass filters; Particle swarm optimization; Remote sensing; Support vector machines; hyperspectral images; image classification; particle swarm optimization (PSO); support vector machine (SVM); wavelet filters;
Conference_Titel :
Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009
Conference_Location :
Cape Town
Print_ISBN :
978-1-4244-3394-0
Electronic_ISBN :
978-1-4244-3395-7
DOI :
10.1109/IGARSS.2009.5418070
