ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model

Convolutional neural networks (CNNs) and Transformers have made impressive progress in the field of remote sensing change detection (CD). However, both architectures have inherent shortcomings: CNN is constrained by a limited receptive field that may hinder their ability to capture broader spatial contexts, while Transformers are computationally intensive, making them costly to train and deploy on large datasets. Recently, the Mamba architecture, based on state space models (SSMs), has shown remarkable performance in a series of natural language processing tasks, which can effectively compensate for the shortcomings of the above two architectures. In this article, we explore for the first time the potential of…

• MR
  Microsoft Research
• UO
  University of Tokyo
• CF
  Council for Science, Technology and Innovation
• MR
  Microsoft Research Asia
• JS
  Japan Society for the Promotion of Science

  Award: 22H03609
• JS
  Japan Science and Technology Agency

  Award: JPMJFR206S
• NR
  National Research Institute for Earth Science and Disaster Prevention