Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection

Following the intuitive idea of detecting changes by directly comparing dissimilarity between a pair of images, we propose a novel Fully Convolutional siamese metric Network (CosimNet) to measure changes by customizing implicit metric. To learn more discriminative metrics, we utilize contrastive loss to reduce the distance between the unchanged feature pair and enlarge the distance between changed feature pair. Specially, to address the issue of large viewpoint difference, we propose Thresholded Contrastive Loss (TCL) with more tolerance strategy to punish this noisy change. We demonstrate the effectiveness of the proposed approach with experiments on three challenging datasets including CDnet, PCD2015, and VL-CMU-CD. Source code is available at https://github.com/gmayday1997/ChangeDet.