Supervised Band Selection Using Local Spatial Information for Hyperspectral Image

In order to alleviate the subsequent computation burden and storage requirement, band selection has been widely adopted to reduce the dimensionality of hyperspectral images, and the current methods mainly consist of the supervised and the unsupervised. Although these supervised methods have better performance, those unsupervised methods dominate the band selection field. In this letter, based on the unique properties of hyperspectral images, we propose a very simple but effective supervised band selection algorithm based on the local spatial information of the hyperspectral image and wrapper method. By using both the information of labeled and unlabeled pixels of the hyperspectral image, our proposed algorithm consistently outperforms the classical wrapper method. We use five widely used real hyperspectral data to demonstrate the effectiveness of our proposed algorithms. We also analyze the relationship between our band selection algorithm and the well-known Markov random field classifier.