RMMamba: Randomized Mamba for Remote Sensing Shadow Removal

Remote sensing shadow removal task aims to effectively restore key regions of an image obscured by shadows. However, the spatial non-uniformity of shadow distribution presents significant challenges to this task. To address this issue, we propose RMMamba, a shadow removal network based on the SS2D architecture. The core concept of RMMamba involves balancing the spatial non-uniformity of shadow distribution, thereby optimizing the utilization efficiency of non-shadow pixel information across different windows. Specifically, RMMamba employs a random pixel shuffling operation to evenly disperse pixels from shadow regions with pronounced spatial non-uniformity into non-shadow areas, ensuring a more balanced spatial distribution of shadow and non-shadow pixels within each window. Subsequently, a shared weight local State Space Model (SS2D) is employed to integrate non-shadow pixel features uniformly distributed around shadow pixels, consequently effectively relighting shadow pixels. Reverse shuffling operations are then applied to restore the processed image to its original pixel order. Coupled with CP-FFN, a lightweight feedforward network incorporating color priors, RMMamba effectively restores color in shadow regions. More importantly, given the difficulty of acquiring remote sensing shadow samples with corresponding ground truth and shadow masks, we leverage the game GTA to control its shadow renderer and create SRGTA, a synthetic fully supervised dataset, hence providing a new benchmark for the performance evaluation of remote sensing shadow removal algorithms. Extensive experiments conducted on SRGTA and UAV-SC have demonstrated the outstanding performance of RMMamba. The code and SRGTA dataset are publicly available at https://github.com/xgd-cj/RMMamba.