ScaleMix: Intra- And Inter-Layer Multiscale Feature Combination for Change Detection

Rui Huang, Qingyi Zhao, Ruofei Wang, Caihua Liu, Sihua Gao, Yuxiang Zhang, Wei Fan

Abstract: Change detection (CD) aims at finding change objects from bi-temporal images, which has wide applications in different vision tasks. Previous CD methods focus more on fusing inter-layer multiscale features while ignoring the intra-layer multiscale characteristics, which hurts the integrity of change objects with different sizes. In this paper, we propose to mix intra- and inter-layer multiscale features to generate more complete change regions. To realize intra-layer multi-scale, we propose inception difference module (IDM), which employs convolutional filters with different sizes, absolute differences, and residual connections to capture intra-layer multiscale characteristics. To capture inter-layer multiscale, we propose a residual network refinement module (RNR) to fuse the features from the highest layer to the lowest layer and generate finely detailed change predictions. Our method can capture complete changes of different sizes by considering the multiscale characteristics of intra- and inter-layer simultaneously. Experiments on two benchmark datasets reveal that our method outperforms six state-of-the-art change detectors.