MSAHiFiC: A Super-Prior Driven High-Fidelity Spectral Attention Network for Hyperspectral Image Compression

To address the limitations of existing deep learning-based hyperspectral image compression methods in accurately modeling the rate-distortion problem, we propose a coupled multi-scale attention spatial-spectral high-fidelity compression network (MSAHiFiC). MSAHiFiC employs a super-prior network to estimate bitrate and guide rate-distortion optimization, enhancing performance under constrained bitrate conditions. A multi-scale spectral attention module is introduced to capture spectral dependencies across varying inter-band distances and preserve key spectral features during downscaling. A spectral fidelity term is further incorporated into the loss function to improve reconstruction accuracy. Experiments on three benchmark hyperspectral datasets—HySpecNet-11k, XiongAn, and WHU-Hi—demonstrate that MSAHiFiC outperforms state-of-the-art methods by achieving 5% higher spectral fidelity and 6% improvement in reconstruction accuracy under a low bitrate of 0.5 bpp.