统一检测与去除遥感图像阴影，无需成对训练数据。
SARU: A Shadow-Aware and Removal Unified Framework for Remote Sensing Images with New Benchmarks
- 双分支网络融合多色空间与语义特征，精准生成阴影掩码。
- 无训练物理算法单图修复，处理速度超1.3秒/图，性能接近先进方法。
- 提出新基准数据集，推动遥感阴影研究标准化与可比性。
阴影是遥感影像中普遍存在的问题，降低视觉质量并严重制约目标检测与语义分割等下游任务表现。现有方法多将阴影检测与去除视为独立步骤，导致流程繁琐且误差累积。此外，多数深度学习方法依赖成对的有影与无影图像进行训练，而现实中此类数据常不可得。为此，本文提出统一的SARU框架，分两阶段处理：首先，通过双分支检测模块（DBCSF-Net）融合多色空间与语义特征，生成高保真阴影掩码，有效区分阴影与暗物体；随后，基于该掩码，设计一种无需训练的物理恢复算法（N²SGSR），仅凭单张输入图像，利用邻近非阴影区域属性实现光照复原。为促进严格评估与未来研究，本文还引入两个新基准数据集：遥感阴影检测数据集（RSISD）与单图阴影去除基准（SiSRB）。在AISD与RSISD数据集上的大量实验表明，SARU在阴影检测上达到当前最优（SOTA）性能；在去除方面，其无训练算法平均处理时间约1.3秒，快于当前最优方法MAOSD超过10倍，且在AISD与SiSRB上均保持接近0.9的SRI值，媲美先进方法RS-GSSR。通过整合检测与去除以减少误差传播，并摆脱对成对数据的依赖，SARU构建了一个鲁棒、实用的遥感图像分析框架。代码与数据集已开源：https://github.com/AeroVILab-AHU/SARU
原文摘要 · Abstract (English)
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 on the AISD and RSISD datasets demonstrate that SARU achieves SOTA shadow detection performance. For shadow removal, our training-free N$^2$SGSR algorithm attains an average processing speed of approximately $1.3$s, which is over $10$ times faster than the SOTA MAOSD while maintains an SRI value close to 0.9 on both the AISD and SiSRB datasets, a level comparable to the advanced RS-GSSR method. 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 code and datasets are publicly available at: https://github.com/AeroVILab-AHU/SARU
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