Multicluster Spatial-Spectral Unsupervised Feature Selection for Hyperspectral Image Classification

Haichang Li, Shiming Xiang, Zisha Zhong, Kun Ding, Chunhong Pan

Abstract: A new unsupervised spatial-spectral feature selection method for hyperspectral images has been proposed in this letter. The key idea is to select the features that better preserve the multicluster structure of the multiple spatial-spectral features. Specifically, the multicluster structure information is obtained through spectral clustering utilizing a weighted combination of the multiple features. Then, such information is preserved in a group-sparsity-based robust linear regression model. The features that contribute more in preserving the multicluster structure information are selected. Comparative experiments on two popular real hyperspectral images validate the effectiveness of the proposed method, showing higher classification accuracy.