Spectral similarity-preserving hyperspectral band selection

Abstract

Due to the high spectral resolution, hyperspectral images (HSI) have been widely used in land cover classification and material identification. Band selection is one of the necessary preprocessings to reduce the data volume and the redundancy therein for the subsequent analysis. Aiming at speeding up the search-based band selection process, this letter proposes a new technique from the perspective of spectral curve shape similarity. Through a newly defined measure for band subset discriminativeness (BSD), class-specific important bands (IBs) are retained which can preserve the spectral similarity of the samples from the same class and narrow down candidate band subset for the following search procedure. Then optimal search is performed in the aggregated band subset from all classes. Experiments on the Indian Pine benchmark data set have proved the efficiency and effectiveness of the proposed method.

Acknowledgements

The authors would like to thank Prof. Serpico and Dr. Moser for providing us the training/test samples of the Indian Pine data set. The authors would also like to express their gratitude to the editor and the anonymous referees for their constructive advices to improve the clarity of the letter. This work is partially supported by National Natural Science Foundation of China (NSFC) under grant nos. 61170200 and 51079040, the Key Technology R&D Program of Jiangsu Province (BE2012179).