A novel approach to hyperspectral band selection based on spectral shape similarity analysis and fast branch and bound search

With the development of hyperspectral remote sensing technology, the spectral resolution of the hyperspectral image data becomes denser, which results in large number of bands, high correlation between neighboring bands, and high data redundancy. It is necessary to reduce these bands before further analysis, such as land cover classification and target detection. Aiming at the classification task, this paper proposes an effective band selection method from the novel perspective of spectral shape similarity analysis with key points extraction and thus retains physical information of hyperspectral remote sensing images. The proposed approach takes all the bands of hyperspectral remote sensing images as time series. Firstly, spectral clustering is utilized to cluster all the training samples, which produces the prototypical spectral curves of each cluster. Then a set of initial candidate bands are obtained based on the extraction of key points from the processed hyperspectral curves, which preserve discriminative information and narrow down the candidate band subset for the following search procedure. Finally, filtering contiguous bands according to conditional mutual information and branch and bound search are further performed sequentially to gain the optimal band combination. To verify the effectiveness of the integrated band selection method put forward in this paper, classification employing the Support Vector Machine (SVM) classifier is performed on the selected spectral bands. The experimental results on two publicly available benchmark data sets demonstrate that the presented approach can select those bands with discriminative information, usually about 10 out of 200 original bands. Compared with previous studies, the newly proposed method is competitive with far fewer bands selected and a lower computational complexity, while the classification accuracy remains comparable.