Joint Spatial and Spectral Graph-Based Consistent Self-Representation for Unsupervised Hyperspectral Band Selection

Band selection (BS), which effectively reduces spectral dimensionality, stands out as a leading focus within hyperspectral image (HSI) analysis. Self-representation (SR) has surfaced as a favored technique in this domain due to its applicability to BS and unsupervised nature. However, the existing SR-based BS approaches only leverage either spatial or spectral relationships, with few integrating both while concentrating on the representation level rather than the selection level. In addition, employing all spatial pixels for spatial relationship utilization leads to considerable computational complexity. Therefore, this article proposes joint spatial and spectral graph-based consistent SR (JSSGCSR) to more effectively exploit spatial and spectral relationships for BS, which separately conducts SR to handle each view of spatial and spectral graphs to better consider two different structure characteristics, and ultimately integrates two SR results to achieve a unified and robust representative band set by imposing consistent sparsity pattern on their joint representation coefficients. In addition, the spatial and spectral relationships are integrated into different data spaces, that is, spectral graph SR and spatial graph SR are, respectively, conducted in the original HSI and the segmented and pooled HSI, which not only reduces the influence of superpixel segmentation on spectral relationships, but also improves the efficiency of spatial relationship utilization. Experimental results on three benchmark datasets have demonstrated the effectiveness of the proposed JSSGCSR in HSI classification tasks.