DSAMNet: A Deeply Supervised Attention Metric Based Network for Change Detection of High-Resolution Images
In view of the insufficient of current change detection, we propose a deeply-supervised attention metric-based network (DSAMNet) for bi-temporal image change detection. The DSAMNet contains a CBAM integrated change decision module to learn a change map directly from features from feature extractor, and an auxiliary deep supervision module to generate intermediate change results to help the training of hidden layers. We also provide a new benchmark-SYSU-CD-with totally 20000 image pairs for the training and testing of deep learning based CD methods. Comparative experiments on the SYSU-CD dataset have proved the effectiveness of the proposed method.
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