Classification and pattern recognition of high dimensional remote sensing data are distinctly different from traditional multi-channel remote sensing classification techniques. In this paper

a newly integrated feature extraction algorithm based on GA and wavelet/wavelet packet （WP） transform is proposed for high dimensional data reduction and classification. The proposed algorithm combines the advantages of GA’s global optimization and wavelet’s multiresolution and multi-scale analysis. Hyperspectral signals are firstly transformed to feature domain by using a discrete wavelet or wavelet packet decomposition strategy. Since the discrete wavelet transform （DWT） is a linear transform

the DWT coefficients at specific scales could be directly used as linear features. Followed by the decomposition phase is optimal feature subset selection

in which the optimal feature subset acquired the best divergence is obtained according to interclass/intraclass distance of the training samples. This procedure is implemented by a Genetic Algorithm

with each possible feature subset encoded as chromosome. Fitness scores in GA are calculated and evaluated based on Jeffries-Matusita distance of the selected training samples. Hyperspectral data are classified with maximum likelihood classifier （ MLC）. Experimental results show that the use of DWT/WP and GA-based feature extraction technique improves the overall classification accuracy by 1. 1%-6. 5%

as compared to the use of conventional feature extraction techniques

such as principal component analysis （ PCA）

Discriminant Analysis Feature Extraction （ DAFE） and Decision Boundary Feature Extraction （ DBFE）.