Efficient Graph Convolutional Self-Representation for Band Selection of Hyperspectral Image

Hyperspectral image (HSI) band selection (BS) is an important task for HSI dimensionality reduction, whose goal is to select an informative band subset containing less redundancy. However, traditional BS methods basically work in the Euclidean domain, and thus, often neglect to consider the structural information of spectral bands. In this article, to make full use of the structural information, a novel BS method termed as efficient graph convolutional self-representation (EGCSR) is proposed by incorporating graph convolution into the self-representation model. Since the proposed method is typically modeled in the non-Euclidean domain, it tends to result in a more robust self-representation coefficient matrix. We provide a closed-form solution to the EGCSR model, which leads to high-computational efficiency. We further propose two strategies to determine the informative band subset from the coefficient matrix. The first is a ranking-based strategy, which ranks every band by calculating the cumulative contribution, and the second is a clustering-based strategy, which treats BS as a band clustering task based on using subspace segmentation. Extensive experimental results on three real HSI datasets show that the proposed EGCSR model is dramatically superior to many existing BS methods, and with high-computational efficiency.