Building Change Detection for Remote Sensing Images Using a Dual-Task Constrained Deep Siamese Convolutional Network Model

In recent years, building change detection methods have made great progress by introducing deep learning, but they still suffer from the problem of the extracted features not being discriminative enough, resulting in incomplete regions and irregular boundaries. To tackle this problem, we propose a dual-task constrained deep Siamese convolutional network (DTCDSCN) model, which contains three subnetworks: a change detection network and two semantic segmentation networks. DTCDSCN can accomplish both change detection and semantic segmentation at the same time, which can help to learn more discriminative object-level features and obtain a complete change detection map. Furthermore, we introduce a dual attention…

• NN
  National Natural Science Foundation of China

  Award: 61871295
• NK
  National Key Research and Development Program of China

  Award: 2016YFC0802500