2DMCG:2DMambawith Change Flow Guidance for Change Detection in Remote Sensing
This work addresses a domain-specific problem in remote sensing change detection, offering an incremental improvement by adapting Mamba for 2D spatial processing.
The paper tackles the problem of spatial information loss in 2D vision tasks using the Mamba architecture for remote sensing change detection by proposing a Vision Mamba variant with a 2D encoder and feature fusion methods, achieving superior performance on benchmark datasets like LEVIR-CD+ and WHU-CD.
Remote sensing change detection (CD) has made significant advancements with the adoption of Convolutional Neural Networks (CNNs) and Transformers. While CNNs offer powerful feature extraction, they are constrained by receptive field limitations, and Transformers suffer from quadratic complexity when processing long sequences, restricting scalability. The Mamba architecture provides an appealing alternative, offering linear complexity and high parallelism. However, its inherent 1D processing structure causes a loss of spatial information in 2D vision tasks. This paper addresses this limitation by proposing an efficient framework based on a Vision Mamba variant that enhances its ability to capture 2D spatial information while maintaining the linear complexity characteristic of Mamba. The framework employs a 2DMamba encoder to effectively learn global spatial contextual information from multi-temporal images. For feature fusion, we introduce a 2D scan-based, channel-parallel scanning strategy combined with a spatio-temporal feature fusion method, which adeptly captures both local and global change information, alleviating spatial discontinuity issues during fusion. In the decoding stage, we present a feature change flow-based decoding method that improves the mapping of feature change information from low-resolution to high-resolution feature maps, mitigating feature shift and misalignment. Extensive experiments on benchmark datasets such as LEVIR-CD+ and WHU-CD demonstrate the superior performance of our framework compared to state-of-the-art methods, showcasing the potential of Vision Mamba for efficient and accurate remote sensing change detection.