ICIF-Net: Intra-Scale Cross-Interaction and Inter-Scale Feature Fusion Network for Bitemporal Remote Sensing Images Change Detection

Change detection (CD) of remote sensing (RS) images has enjoyed remarkable success by virtue of convolutional neural networks (CNNs) with promising discriminative capabilities. However, CNNs lack the capability of modeling long-range dependencies in bitemporal image pairs, resulting in inferior identifiability against the same semantic targets yet with varying features. The recently thriving Transformer, on the contrary, is warranted, for practice, with global receptive fields. To jointly harvest the local-global features and circumvent the misalignment issues caused by step-by-step downsampling operations in traditional backbone networks, we propose an intra-scale cross-interaction and inter-scale feature…

• NN
  National Natural Science Foundation of China

  Award: 61602413
• NK
  National Key Research and Development Program of China

  Award: 2018YFE0126100