A CNN-Transformer Network With Multiscale Context Aggregation for Fine-Grained Cropland Change Detection

Non-agriculturalization incidents are serious threats to local agricultural ecosystem and global food security. Remote sensing change detection (CD) can provide an effective approach for in-time detection and prevention of such incidents. However, existing CD methods are difficult to deal with the large intra-class differences of cropland changes in high-resolution images (HRIs). In addition, traditional CNN based models are plagued by the loss of long-range context information, and the high computational complexity brought by deep layers. Therefore, we propose a CNN-transformer network with multi-scale context aggregation (MSCANet), which combines the merits of CNN and transformer to fulfill efficient and…

• NN
  National Natural Science Foundation of China

  Award: 61976234
• SY
  Sun Yat-sen University

  Award: 22qntd2001
• FR
  Fundamental Research Funds for the Central Universities