RS-Mamba for Large Remote Sensing Image Dense Prediction

Context modeling is critical for remote sensing image dense prediction tasks. Nowadays, the growing size of very-high-resolution (VHR) remote sensing images poses challenges in effectively modeling context. While transformer-based models possess global modeling capabilities, they encounter computational challenges when applied to large VHR images due to their quadratic complexity. The conventional practice of cropping large images into smaller patches results in a notable loss of contextual information. To address these issues, we propose the remote sensing Mamba (RSM) for dense prediction tasks in large VHR remote sensing images. RSM is specifically designed to capture the global context of remote sensing…

• NN
  National Natural Science Foundation of China

  Award: 42071297
• FR
  Fundamental Research Funds for the Central Universities

  Award: 020914380119