Hyperspectral Band Selection based on Improved Affinity Propagation

Abstract

Dimensionality reduction is a common method to reduce the computational complexity of hyperspectral images and improve the classification performance. Band selection is one of the most commonly used methods for dimensionality reduction. Affinity propagation (AP) is a clustering algorithm that has better performance than traditional clustering methods. This paper proposes an improved AP algorithm (IAP), which divides each intrinsic cluster into several subsets, and combines the information entropy to change the initial availability matrix to obtain a suitable number of clustering results with arbitrary shapes. The experimental results on the public hyperspectral data set show that the band combination selected by IAP has a better classification accuracy compared with all bands data set and band subset by traditional AP algorithm.