A novel geometry-based feature-selection technique for hyperspectral imagery

In this letter, a geometry-based feature-selection method is proposed for efficient analysis of hyperspectral imagery. It searches the vertices that form the largest simplex iteratively in pixel space. These vertices are representative subsets of spectral bands. A distance measure is introduced in the simplex volume comparison for fast implementation of the proposed method. Fast principal component analysis and spectral band indexing are suggested for data preprocessing. This method can be implemented in supervised or unsupervised manner. It is automatic, fast, and distribution-free. Experimental results show the superiority of the proposed method in terms of quality and speed.