DehazeMamba: SAR-guided Optical Remote Sensing Image Dehazing with Adaptive State Space Model

• URL: http://arxiv.org/abs/2503.13073v1
• Date: Mon, 17 Mar 2025 11:25:05 GMT
• Title: DehazeMamba: SAR-guided Optical Remote Sensing Image Dehazing with Adaptive State Space Model
• Authors: Zhicheng Zhao, Jinquan Yan, Chenglong Li, Xiao Wang, Jin Tang,
• Abstract summary: We introduce DehazeMamba, a novel SAR-guided dehazing network built on a progressive haze decoupling fusion strategy.<n>Our approach incorporates two key innovations: a Haze Perception and Decoupling Module (HPDM) that dynamically identifies haze-affected regions through optical-SAR difference analysis, and a Progressive Fusion Module (PFM) that mitigates domain shift through a two-stage fusion process based on feature quality assessment.<n>Extensive experiments demonstrate that DehazeMamba significantly outperforms state-of-the-art methods, achieving a 0.73 dB improvement in PSNR and substantial enhancements in downstream tasks such as
• Score: 27.83437788159158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optical remote sensing image dehazing presents significant challenges due to its extensive spatial scale and highly non-uniform haze distribution, which traditional single-image dehazing methods struggle to address effectively. While Synthetic Aperture Radar (SAR) imagery offers inherently haze-free reference information for large-scale scenes, existing SAR-guided dehazing approaches face two critical limitations: the integration of SAR information often diminishes the quality of haze-free regions, and the instability of feature quality further exacerbates cross-modal domain shift. To overcome these challenges, we introduce DehazeMamba, a novel SAR-guided dehazing network built on a progressive haze decoupling fusion strategy. Our approach incorporates two key innovations: a Haze Perception and Decoupling Module (HPDM) that dynamically identifies haze-affected regions through optical-SAR difference analysis, and a Progressive Fusion Module (PFM) that mitigates domain shift through a two-stage fusion process based on feature quality assessment. To facilitate research in this domain, we present MRSHaze, a large-scale benchmark dataset comprising 8,000 pairs of temporally synchronized, precisely geo-registered SAR-optical images with high resolution and diverse haze conditions. Extensive experiments demonstrate that DehazeMamba significantly outperforms state-of-the-art methods, achieving a 0.73 dB improvement in PSNR and substantial enhancements in downstream tasks such as semantic segmentation. The dataset is available at https://github.com/mmic-lcl/Datasets-and-benchmark-code.

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