RISNet: Robust Ill-Posed Solver for Remote Sensing Image Change Detection

With the development of deep learning, the application of remote sensing (RS) image change detection (CD) has achieved essential breakthroughs. However, RS image CD faces challenges due to ill-posed problems, inherent uncertainty, and instability in the solution process. These challenges can be broadly categorized into two main issues: semantic redundancy and insufficient input information. Semantic redundancy in RS data introduces overlapping and repetitive features that complicate the CD process. Redundant and overlapping semantic content reduces the availability of clear information, introducing additional noise and making it harder to distinguish relevant changes from background variations. Insufficient input information exacerbates the ill-posed nature of RS image CD. This insufficiency often results from the loss of critical feature information during the extraction process. To address these problems, this article proposes RISNet, a fully supervised transformer-based CD method. RISNet consists of three main modules: temporal feature extraction (TFE), feature reconstruction encoder (FRE), and data perturbation regularization (DPR). First, the TFE module extracts rich spatio-temporal features from bitemporal RS images by leveraging contextual similarity to highlight change regions. Next, the FRE module employs an encoder–decoder system to analyze these features, assign change probabilities, and mask those with high change probabilities. Finally, during training, the DPR module introduces perturbations into the feature representations to enhance the model's adaptability to nonsemantic changes. This integrated approach effectively tackles the challenges of ill-posed problems. It improves model stability, demonstrating superior performance on the LEVIR, WHU, DSIFN, and S2Looking datasets compared to recent transformer-based methods.