Change Detection on Remote Sensing Images Using Dual-Branch Multilevel Intertemporal Network

Change detection (CD) of remote sensing (RS) images is mushrooming up accompanied by the on-going innovation of convolutional neural networks (CNNs). Yet with the high-speed technology upgrade, the obstacle that identifies unbalanced variations in foreground–background categories still lies on the table, especially in cases with limited samples and massive interference such as seasonal turnover, illumination intensity, and building reformation. Moreover, to date, neither of the off-the-shelf methods probes the feasibility of direct interaction between bitemporal images before accessing difference features. In this article, we propose a dual-branch multilevel intertemporal network (DMINet) to efficiently and…

• NN
  National Natural Science Foundation of China

  Award: 62276232
• ZU
  Zhejiang University of Science and Technology

  Award: 2022JCY10
• NK
  National Key Research and Development Program of China

  Award: 2018YFE0126100