Hyperspectral band selection, which aims to select a small number of bands to reduce data redundancy and noisy bands, has attracted widespread attention in recent years. Many effective clustering-based band selection methods have been proposed to accomplish the band selection task and have achieved satisfying performance. However, most of the previous methods reshape the original hyperspectral images (HSIs) into a set of stretched band vectors, which ignore the spatial information of HSIs and the difference between diverse regions. To address these issues, a graph regularized spatial-spectral subspace clustering method for hyperspectral band selection is proposed in this paper, referred to as GRSC. Specifically, the proposed method adopts superpixel segmentation to preserve the spatial information of HSIs by segmenting their first principal component into diverse homogeneous regions. Then the discriminative latent features are generated from each segmented region to represent the whole band, which can mitigate the effect of noise on the band selection. Finally, a self-representation subspace clustering model and an l2,1-norm regularization are utilized to explore the spectral correlation among all bands. In addition, a similarity graph between region-aware latent features is adaptively learned to preserve the spatial structure of HSIs in the latent representation space. Extensive classification experimental results on three public datasets verify the effectiveness of GRSC over several state-of-the-art methods. The demo code of this work is publicly available at https://github.com/WangJun2023/GRSC.
Keywords: Clustering; Feature learning; Hyperspectral band selection; Similarity graph learning.
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