A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection

Remote sensing image change detection (CD) is done to identify desired significant changes between bitemporal images. Given two co-registered images taken at different times, the illumination variations and misregistration errors overwhelm the real object changes. Exploring the relationships among different spatial–temporal pixels may improve the performances of CD methods. In our work, we propose a novel Siamese-based spatial–temporal attention neural network. In contrast to previous methods that separately encode the bitemporal images without referring to any useful spatial–temporal dependency, we design a CD self-attention mechanism to model the spatial–temporal relationships. We integrate a new CD…

• SA
  Shanghai Association for Science and Technology

  Award: SAST2018096
• NN
  National Natural Science Foundation of China

  Award: 61671037