阴影是遥感影像（RSI）中普遍存在的问题，会降低视觉质量，并严重制约目标检测、语义分割等下游任务的性能。现有大多数方法将阴影检测与阴影去除视为彼此分离、级联执行的任务，导致流程繁琐且易产生误差累积。此外，许多深度学习方法依赖成对的含阴影与无阴影图像进行训练，而此类配对数据在实际应用中往往难以获取。为应对上述挑战，我们提出阴影感知与去除统一框架（SARU），一种结构紧凑的两阶段框架：首先，其双分支检测模块（DBCSF-Net）融合多色彩空间特征与语义特征，生成高保真阴影掩膜，有效区分阴影区域与暗色物体；随后，基于该掩膜，一种新颖的无需训练的物理算法（N²SGSR）利用单幅输入图像中邻近非阴影区域的光照特性，实现光照恢复。为支持严格评估并推动后续研究，我们还构建了两个新基准数据集：遥感影像阴影检测数据集（RSISD）与单图像阴影去除基准（SiSRB）。大量实验表明，SARU 在公开 AISD 数据集及我们新构建的基准上均达到当前最优性能。通过整体整合阴影检测与去除以抑制误差传播，并消除对配对训练数据的依赖，SARU 构建了一个鲁棒且实用的遥感影像分析框架。源代码与数据集已公开发布于：https://github.com/AeroVILab-AHU/SARU-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.