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HDMba: Hyperspectral Remote Sensing Imagery Dehazing with State Space Model

Hang Fu, Genyun Sun, Yinhe Li, Jinchang Ren, Aizhu Zhang, Cheng Jing, Pedram Ghamisi

TL;DR

This document provides a practical guide to using the IEEEtran LaTeX class for preparing IEEE submissions. It covers template design, distribution sources, and steps for constructing front matter, body elements, and back matter, with attention to two-column formatting and XML/HTML conversion. It also offers detailed guidance on mathematics, figures, tables, citations, and BibTeX usage to prevent common formatting errors. The result is a workflow-oriented reference that reduces formatting issues and accelerates publication-ready manuscript preparation.

Abstract

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.

HDMba: Hyperspectral Remote Sensing Imagery Dehazing with State Space Model

TL;DR

This document provides a practical guide to using the IEEEtran LaTeX class for preparing IEEE submissions. It covers template design, distribution sources, and steps for constructing front matter, body elements, and back matter, with attention to two-column formatting and XML/HTML conversion. It also offers detailed guidance on mathematics, figures, tables, citations, and BibTeX usage to prevent common formatting errors. The result is a workflow-oriented reference that reduces formatting issues and accelerates publication-ready manuscript preparation.

Abstract

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.
Paper Structure (42 sections, 17 equations, 1 figure, 1 table)

This paper contains 42 sections, 17 equations, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. The Design, Intent and Limitations of the Templates
  3. LaTeX Distributions: Where to Get Them
  4. Where to get the IEEEtran Templates
  5. Where to get LaTeX help - user groups
  6. Document Class Options in IEEEtran
  7. How to Create Common Front Matter
  8. Paper Title
  9. Author Names and Affiliations
  10. Running Heads
  11. Copyright Line
  12. Abstracts
  13. Index Terms
  14. How to Create Common Body Elements
  15. Initial Drop Cap Letter
  16. ...and 27 more sections

Figures (1)

  • Figure 1: This is the caption for one fig.