2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IP-2.3
Paper Title SEMANTIC SEGMENTATION AND CHANGE DETECTION BY MULTI-TASK U-NET
Authors Shungo Tsutsui, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi, Chubu University, Japan
SessionMLR-APPL-IP-2: Machine learning for image processing 2
LocationArea E
Session Time:Monday, 20 September, 15:30 - 17:00
Presentation Time:Monday, 20 September, 15:30 - 17:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract 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.