Enhanced classification of remotely sensed hyperspectral images through efficient band selection using autoencoders and genetic algorithm

Abstract

Hyperspectral images (HSIs) contain significant number of contiguous dense spectral bands which often have large redundancy and high correlation that subsequently results into “curse of dimensionality” in HSI analysis. Therefore, efficient band selection techniques are crucial for dimensionality reduction of HSIs without any significant loss of spectral information contained in it. In this paper, deep learning autoencoders and genetic algorithm (GA) are used for efficient selection of the most revealing bands from a remotely sensed HSI. The proposed method formulates the HSI band selection process as a GA-based evolutionary optimization that minimizes the reconstruction error of an autoencoder which uses a few informative bands for HSI reconstruction. The proposed approach starts with spectral segmentation of the bands in an HSI into a number of spectral regions, and then, different autoencoders are trained on each segment with the original input band vectors contained in the segmented region. Finally, GA-based search heuristics is applied on each region in order to find out sparse sub-combination of spectral bands in such a way that the trained autoencoders would reconstruct the original segmented spectral vectors from the resulting band sub-combinations with least reconstruction errors. The final band selection is carried out by aggregating all the band sub-combinations returned from the segmented regions. Finally, the effectiveness of the proposed method is verified through selected bands validation by a support vector machine classifier. Experimental results on three publicly available HSI datasets depict the consistently superior effectiveness of the proposed band selection method over other state-of-the-art methods in land cover classification of remotely sensed HSIs. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.