Haze contamination in hyperspectral remote sensing images (HSI) can lead to
spatial visibility degradation and spectral distortion. Haze in HSI exhibits
spatial irregularity and inhomogeneous spectral distribution, with few dehazing
networks available. Current CNN and Transformer-based dehazing methods fail to
balance global scene recovery, local detail retention, and computational
efficiency. Inspired by the ability of Mamba to model long-range dependencies
with linear complexity, we explore its potential for HSI dehazing and propose
the first HSI Dehazing Mamba (HDMba) network. Specifically, we design a novel
window selective scan module (WSSM) that captures local dependencies within
windows and global correlations between windows by partitioning them. This
approach improves the ability of conventional Mamba in local feature
extraction. By modeling the local and global spectral-spatial information flow,
we achieve a comprehensive analysis of hazy regions. The DehazeMamba layer
(DML), constructed by WSSM, and residual DehazeMamba (RDM) blocks, composed of
DMLs, are the core components of the HDMba framework. These components
effectively characterize the complex distribution of haze in HSIs, aiding in
scene reconstruction and dehazing. Experimental results on the Gaofen-5 HSI
dataset demonstrate that HDMba outperforms other state-of-the-art methods in
dehazing performance. The code will be available at
this https URL

基于长距离依赖模型 Mamba，本研究提出了第一个用于高光谱图像去霾的 HSI Dehazing Mamba (HDMba) 网络，通过设计了一个新颖的窗口选择性扫描模块 (WSSM)，以及由 WSSM 和残差 DehazeMamba (RDM) 块构成的 DehazeMamba layer (DML)，实现了对复杂的雾霾分布的准确建模，从而在场景重建和去霾方面取得了优于其他最先进方法的效果。