xLSTM Interaction Multi-level SSM-Assisted Decoding Network for Remote Sensing Image Change Detection

Remote Sensing Change Detection (RSCD) plays a crucial role in post-disaster reconstruction, environmental monitoring, and urban planning. As a result, it has become a research hotspot in the field of remote sensing image processing in recent years. With the advancements of Convolutional Neural Networks (CNNs) and Transformers in deep learning, the accuracy of RSCD has significantly improved. This improvement is largely attributed to the local feature capture capability of CNNs and the long-range dependency modeling capability of Transformers. However, change detection (CD) tasks involve both temporal and spatial dimensions. Most current deep learning models have limited ability to model the temporal relationships between bitemporal features and lack effective handling of redundant features. To address these limitations, this paper proposes an xLSTM Interaction Multi-level SSM-Assisted Decoding Network (xLMSD-Net). It utilizes Extended Long Short-Term Memory (xLSTM) to construct a Temporal-Aware Feature Interaction Module (TAFIM), which adaptively learns the spatiotemporal relationships between bitemporal images, contrasting and enhancing features from both time phases. Additionally, we combine Gating Mechanism and State Space Model to design a Multi-Channel Feature Optimization Mechanism (MCFOM) for multi-level decoding. The MCFOM suppresses redundant information level by level, enhancing feature representation during image reconstruction and improving the model's robustness and learning capacity. Extensive experiments on the BCDD, LEVIR-CD and CDD change detection datasets show that xLMSD-Net outperforms other state-of-the-art CD methods across multiple performance metrics.