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Remote Sensing Image Dehazing via RGB-Space Physical Constraints
Minxian Shen1, Xucong Jiang1, Chenyang Shao1
1School of Internet of Things Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
Sensors (Basel, Switzerland)
|July 15, 2026
Summary
This study introduces a new remote sensing image dehazing method (RDPC) that overcomes limitations of existing approaches. RDPC effectively removes haze from remote sensing images using physical constraints, improving image quality without needing sky regions or paired training data.
Area of Science:
- Remote Sensing
- Image Processing
- Computer Vision
Background:
- Haze significantly degrades the quality of visible-spectrum remote sensing (RS) images, reducing contrast and distorting colors.
- Existing RS dehazing methods struggle with complex scenes lacking explicit sky regions (prior-driven) or require difficult-to-obtain paired training data (learning-based).
- These limitations hinder the real-world generalization and applicability of current RS image dehazing techniques.
Purpose of the Study:
- To propose a novel remote sensing image dehazing method, Remote Sensing Image Dehazing via RGB-Space Physical Constraints (RDPC).
- To address the limitations of prior-driven and learning-based methods by leveraging physical properties of hazy image formation.
- To enable accurate atmospheric light and transmission estimation without relying on explicit sky regions or paired datasets.
Main Methods:
- RDPC revisits the atmospheric scattering model (ASM) from an RS imaging perspective.
- It estimates atmospheric light by exploiting RGB-space line-convergence behavior in local regions with similar reflectance.
- Transmission estimation utilizes the geometric relationship between observed pixels and atmospheric light in RGB space, incorporating local perpendicularity and global compensation.
Main Results:
- RDPC refines transmission and albedo guidance by enforcing ASM consistency and variation sparsity through joint optimization.
- Experiments on synthetic and real-world RS datasets show RDPC achieves competitive performance compared to state-of-the-art methods.
- Performance is evaluated using metrics such as PSNR, SSIM, LPIPS, BRISQUE, NIMA, and processing time.
Conclusions:
- The proposed RDPC method effectively dehazes remote sensing images by applying physical constraints in RGB space.
- RDPC overcomes the reliance on explicit sky regions and paired training data, enhancing real-world applicability.
- The method demonstrates robust performance and competitive results against existing dehazing techniques.
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