Multiscale Diff-Changed Feature Fusion Network for Hyperspectral Image Change Detection

For hyperspectral image (HSI) change detection (CD), multiscale features are usually used to construct the detection models. However, the existing studies only consider the multiscale features containing changed and unchanged components, which is difficult to represent the subtle changes between bitemporal HSIs in each scale. To address this problem, we propose a multiscale diff-changed feature fusion network (MSDFFN) for HSI CD, which improves the ability of feature representation by learning the refined change components between bitemporal HSIs under different scales. In this network, a temporal feature encoder–decoder subnetwork, which combines a reduced inception (RI) module and a cross-layer attention…

• NN
  National Natural Science Foundation of China

  Awards: 62071340, 62201109
• NS
  Natural Science Foundation of Chongqing

  Award: CSTB2022NSCQ-MSX0452