SEMANTIC SEGMENTATION AND CHANGE DETECTION BY MULTI-TASK U-NET

Change detection involves extracting the changed regions from images taken of the same place at different times. Potential applications are automatically updating of HD maps or identifying damages caused by natural disasters. However, conventional change detection methods merely detect changed regions without classifying them. In this paper, we propose a change detection method that can estimate the object class of a changed region. Our method extends a U-Net as a multi-task learning framework and estimates changed regions and semantic segmentation simultaneously. We propose using the pixel-wise classification probabilities of semantic segmentation for detecting changed regions rather than the conventional L2 norm-based difference of feature maps. In our experiments, we show that our method can improve change detection performance and estimate the classes of corresponding changed objects.