Multiple Feature Learning Based on Edge-Preserving Features for Hyperspectral Image Classification

Abstract

The classification of hyperspectral images is the basis and hotspot in the research of hyperspectral images. In this paper, a classification algorithm of hyperspectral image based on multiple edge-preserving features and multiple feature learning (MFL) is proposed. First, aiming to eliminate the high correlation between adjacent bands and to remove the noise in the image, a new band clustering algorithm is employed to reduce the dimensions, where the dimension-reduced image can be used as spectral information features to extract linearly separable classes. Then, spatial information features are obtained by applying the multiple edge-preserving filter on the reduced-dimensional image. This filter is used to acquire more comprehensive spatial information features of the image for extraction of nonlinearly separable classes. Following that, the locality preserving projections method is applied to retain the representative spatial information from the extracted spatial information for classification accuracy. Finally, the spectral information features and spatial information features are combined for classification using the MFL. The experiments are conducted to verify the validity of the proposed algorithm on three universally adopted hyperspectral datasets.