# HDMba: Hyperspectral Remote Sensing Imagery Dehazing with State Space Model

arXiv: https://arxiv.org/abs/2406.05700 · HTML: https://mlsanity.com/paper/2406.05700/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Hyperspectral image analysis with Mamba, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 9 Jun 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Hyperspectral image analysis with Mamba](https://mlsanity.com/ssm/hyperspectral-image-analysis-with-mamba/)

## Builds on (most cited first, 11 of 11)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [FFA-Net: Feature Fusion Attention Network for Single Image Dehazing](https://mlsanity.com/paper/1911.07559/) (2019)
- [Vision Transformers for Single Image Dehazing](https://mlsanity.com/paper/2204.03883/) (2022)
- [LocalMamba: Visual State Space Model with Windowed Selective Scan](https://mlsanity.com/paper/2403.09338/) (2024)
- [Visual Mamba: A Survey and New Outlooks](https://mlsanity.com/paper/2404.18861/) (2024)
- [U-shaped Vision Mamba for Single Image Dehazing](https://mlsanity.com/paper/2402.04139/) (2024)
- [Spatial-Spectral Transformer for Hyperspectral Image Denoising](https://mlsanity.com/paper/2211.14090/) (2022)
- [Image Restoration for Remote Sensing: Overview and Toolbox](https://mlsanity.com/paper/2107.00557/) (2021)
- [RSDehamba: Lightweight Vision Mamba for Remote Sensing Satellite Image Dehazing](https://mlsanity.com/paper/2405.10030/) (2024)
- [LKD-Net: Large Kernel Convolution Network for Single Image Dehazing](https://mlsanity.com/paper/2209.01788/) (2022)

## Built on by (most cited first, 5 of 5)

- [Mamba in Vision: A Comprehensive Survey of Techniques and Applications](https://mlsanity.com/paper/2410.03105/) (2024)
- [Real-World Remote Sensing Image Dehazing: Benchmark and Baseline](https://mlsanity.com/paper/2503.17966/) (2025)
- [Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook](https://mlsanity.com/paper/2505.00630/) (2025)
- [DehazeMamba: SAR-guided Optical Remote Sensing Image Dehazing with Adaptive State Space Model](https://mlsanity.com/paper/2503.13073/) (2025)
- [State Space Models Meet Remote Sensing: A Survey](https://mlsanity.com/paper/2606.25329/) (2026)

## Abstract (arXiv)

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 https://github.com/RsAI-lab/HDMba.