CDMamba: Incorporating Local Clues Into Mamba for Remote Sensing Image Binary Change Detection

Recently, the Mamba architecture based on state-space models has demonstrated remarkable performance in a series of natural language processing tasks and has been rapidly applied to remote sensing change detection (CD) tasks. However, most methods enhance the global receptive field by directly modifying the scanning mode of Mamba, neglecting the crucial role that local information plays in dense prediction tasks (e.g., binary CD). In this article, we propose a model called CDMamba, which effectively combines global and local features for handling binary CD tasks. Specifically, the scaled residual ConvMamba (SRCM) block is proposed to utilize the ability of Mamba to extract global features and convolution to…

• NN
  National Natural Science Foundation of China

  Awards: 62471014, U24B20177, 62125102
• NK
  National Key Research and Development Program of China

  Award: 2022ZD0160401
• FR
  Fundamental Research Funds for the Central Universities