A Real-Time Unsupervised Hyperspectral Band Selection via Spatial-Spectral Information Fusion-Based Downscaled Region

Information fusion plays a vital role in hyperspectral band selection as it enables the exploration of the spatial-spectral structure relationship present in bands of hyperspectral images (HSIs). However, the focus of most algorithms primarily lies in the processing of single-band vectors, leaving only a few algorithms to handle spatial-spectral features of HSIs in a complex and inefficient manner. To overcome these limitations, a real-time unsupervised hyperspectral band selection method via spatial-spectral information fusion-based downscaled region (SIFDR) is proposed in this study. In particular, this approach incorporates an energy constraint method for assigning band weights to each detected pixel and estimates band spectral information through average fusion. Furthermore, a band weak redundancy sorting method is introduced, which is based on spectral information peaks, thereby achieving complementary spectral information. By performing regional downscaling of the HSI, spatial-spectral information is effectively fused, resulting in a real-time entire process. To evaluate the effectiveness of the proposed algorithm, experiments were conducted on four hyperspectral datasets, including an ultrahigh-dimensional medical HSIs, which distinguishes itself from previous methods that are typically evaluated exclusively on remote sensing datasets. Comparative results with several state-of-the-art (SOTA) algorithms demonstrate that the proposed algorithm excellently accomplishes hyperspectral band selection tasks in real time. The code of SIFDR has been shared on
https://github.com/zhangchenglong1116/SIFDR
.