Spatiotemporal Enhancement and Interlevel Fusion Network for Remote Sensing Images Change Detection

Remote sensing (RS) images change detection (CD) plays a crucial role in monitoring surface dynamic. However, current deep learning (DL)-based CD methods still suffer from pseudo changes and scale variations due to inadequate exploration of temporal differences and under-utilization of multiscale features. Based on the aforementioned considerations, a spatiotemporal enhancement and interlevel fusion network (SEIFNet) is proposed to improve the ability of feature representation for changing objects. Firstly, the multilevel feature maps are acquired from Siamese hierarchical backbone. To highlight the disparity in the same location at different times, the spatiotemporal difference enhancement modules (ST-DEM)…

• NN
  National Natural Science Foundation of China

  Award: 42171302
• FR
  Fundamental Research Funds for the Central Universities

  Award: 2042017kf0034