Remote Sensing Image Dehazing: A Systematic Review of Progress, Challenges, and Prospects
🤖 AI Summary
Remote sensing imagery is frequently degraded by haze and thin clouds, leading to distorted surface reflectance information. This work presents the first systematic survey framework for remote sensing dehazing, categorizing existing methods into three paradigms: handcrafted prior-based, data-driven deep restoration, and physics-intelligent hybrid generative approaches, with over thirty representative techniques uniformly evaluated. The study introduces a novel physically radiometric-consistent evaluation metric and advocates for building Trustworthy, Controllable, and Efficient (TCE) dehazing systems. Experimental results demonstrate that integrating Transformers with diffusion models improves SSIM by 12%–18% and reduces perceptual error by 20%–35%. Furthermore, incorporating physical constraints such as transmission maps and atmospheric light estimates mitigates color distortion by up to 27%.
Application Category
📝 Abstract
Remote sensing images (RSIs) are frequently degraded by haze, fog, and thin clouds, which obscure surface reflectance and hinder downstream applications. This study presents the first systematic and unified survey of RSIs dehazing, integrating methodological evolution, benchmark assessment, and physical consistency analysis. We categorize existing approaches into a three-stage progression: from handcrafted physical priors, to data-driven deep restoration, and finally to hybrid physical-intelligent generation, and summarize more than 30 representative methods across CNNs, GANs, Transformers, and diffusion models. To provide a reliable empirical reference, we conduct large-scale quantitative experiments on five public datasets using 12 metrics, including PSNR, SSIM, CIEDE, LPIPS, FID, SAM, ERGAS, UIQI, QNR, NIQE, and HIST. Cross-domain comparison reveals that recent Transformer- and diffusion-based models improve SSIM by 12%~18% and reduce perceptual errors by 20%~35% on average, while hybrid physics-guided designs achieve higher radiometric stability. A dedicated physical radiometric consistency experiment further demonstrates that models with explicit transmission or airlight constraints reduce color bias by up to 27%. Based on these findings, we summarize open challenges: dynamic atmospheric modeling, multimodal fusion, lightweight deployment, data scarcity, and joint degradations, and outline promising research directions for future development of trustworthy, controllable, and efficient (TCE) dehazing systems. All reviewed resources, including source code, benchmark datasets, evaluation metrics, and reproduction configurations are publicly available at https://github.com/VisionVerse/RemoteSensing-Restoration-Survey.
Problem
Research questions and friction points this paper is trying to address.
Remote sensing image dehazing
haze removal
atmospheric degradation
image restoration
radiometric consistency
Innovation
Methods, ideas, or system contributions that make the work stand out.
remote sensing dehazing
physical-intelligent hybrid modeling
radiometric consistency
systematic benchmarking
diffusion-based restoration
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