RSMamba: Remote Sensing Image Classification With State Space Model

Remote sensing image classification forms the foundation of various understanding tasks, serving a crucial function in remote sensing image interpretation. The recent advancements of Convolutional Neural Networks (CNNs) and Transformers have markedly enhanced classification accuracy. Nonetheless, remote sensing scene classification remains a significant challenge, especially given the complexity and diversity of remote sensing scenarios and the variability of spatiotemporal resolutions. The capacity for whole-image understanding can provide more precise semantic cues for scene discrimination. In this paper, we introduce RSMamba, a novel architecture for remote sensing image classification. RSMamba is based on…

• NN
  National Natural Science Foundation of China

  Awards: 623B2013, 62125102
• NK
  National Key Research and Development Program of China

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