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This paper introduces SARU, a two-stage framework for shadow detection and removal in remote sensing imagery that unifies these tasks to avoid error propagation. SARU uses a dual-branch detection module (DBCSF-Net) to generate shadow masks by fusing multi-color space and semantic features, followed by a training-free physical algorithm (N$^2$SGSR) for illumination restoration using information from adjacent non-shadow regions. The authors also introduce two new benchmark datasets, RSISD and SiSRB, and demonstrate state-of-the-art performance on both public and newly introduced datasets.
Achieve state-of-the-art shadow removal in remote sensing images without paired training data by unifying shadow detection and removal into a single framework.
Shadows are a prevalent problem in remote sensing imagery (RSI), degrading visual quality and severely limiting the performance of downstream tasks like object detection and semantic segmentation. Most prior works treat shadow detection and removal as separate, cascaded tasks, which can lead to cumbersome process and error accumulation. Furthermore, many deep learning methods rely on paired shadow and non-shadow images for training, which are often unavailable in practice. To address these challenges, we propose Shadow-Aware and Removal Unified (SARU) Framework , a cohesive two-stage framework. First, its dual-branch detection module (DBCSF-Net) fuses multi-color space and semantic features to generate high-fidelity shadow masks, effectively distinguishing shadows from dark objects. Then, leveraging these masks, a novel, training-free physical algorithm (N$^2$SGSR) restores illumination by transferring properties from adjacent non-shadow regions within the single input image. To facilitate rigorous evaluation and foster future work, we also introduce two new benchmark datasets: the RSI Shadow Detection (RSISD) dataset and the Single-image Shadow Removal Benchmark (SiSRB). Extensive experiments demonstrate that SARU achieves state-of-the-art performance on both the public AISD dataset and our newly introduced benchmarks. By holistically integrating shadow detection and removal to mitigate error propagation and eliminating the dependency on paired training data, SARU establishes a robust, practical framework for real-world RSI analysis. The source code and datasets are publicly available at: https://github.com/AeroVILab-AHU/SARU-Framework.
