Dimensionality Reduction of Hyperspectral Imagery Using Sparse Graph Learning

Abstract

Combining with sparse representation, the sparse graph can adaptively capture the intrinsic structural information of the specified data. In this paper, an unsupervised sparse-graph-learning-based dimensionality reduction (SGL-DR) method is proposed for hyperspectral image. In SGL-DR, the sparse graph construction and projection learning are combined together in a unified framework and influence each other. During sparse graph learning, projected features are utilized to enhance the discriminant information in sparse graph. Likewise, in projection learning, the enhanced sparse graph could make projected features have high discriminant capacity. Besides, the spatial–spectral information in the original space combined with the structure information in the projected space is also exploited to learn the imprecise discriminant information. With the imprecise discriminant information, the projected space that is spanned by the projection matrix of the constructed sparse graph would contain abundant discriminant information, which is beneficial for hyperspectral image classification. Experimental results over two hyperspectral image datasets demonstrate that the proposed approach outperforms the other state-of-the-art unsupervised approaches with a 10% improvement of the classification accuracy. Furthermore, it also outperforms those graph-based supervised methods with acceptable computational cost.