Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection
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The key factor of scene change detection is to learn effective feature to have higher similarity for changed parts and lower similarity for unchanged parts. However, existed researches on change detection are based on semantic segmentation, which gives rise to the lack of semantic discriminative in feature space. In this paper, we propose a novel Fully Convolutional siamese metric Network (CosimNet) and utilize various loss functions, including original contrastive loss and threshold hinge loss to address the limitation above, aiming to pull together similar pairs and push apart dissimilar pairs in the feature space. We demonstrate the effectiveness of the proposed approach with experiments on three challenging datasets including CDnet, PCD2015, and VL-CMU. Source code is available at https://github.com/gmayday1997/ChangeDet
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