MSFMamba: Multiscale Feature Fusion State Space Model for Multisource Remote Sensing Image Classification

In the field of multisource remote sensing image classification, remarkable progress has been made by using the convolutional neural network (CNN) and Transformer. While CNNs are constrained by their local receptive fields, Transformers mitigate this issue with their global attention mechanism. However, Transformers come with the tradeoff of higher computational complexity. Recently, Mamba-based methods built upon the state space model (SSM) have shown great potential for long-range dependence modeling with linear complexity, but they have rarely been explored for multisource remote sensing image classification tasks. To address this issue, we propose the Multi-Scale Feature Fusion Mamba (MSFMamba) network, a…

• NS
  Natural Science Foundation of Qingdao

  Award: 23-2-1-222-ZYYD-JCH
• NS
  National Science and Technology Major Project

  Award: 2022ZD0117202