Difference-Enhancement Triplet Network for Change Detection in Multispectral Images

Change detection is the process of detecting and evaluating differences from bitemporal remote sensing images. Deep-learning-based change detection methods have become the mainstream approaches due to their discriminative features and good change detection performance. However, most of the existing deep-learning-based change detection methods did not perform well in detecting subtle changes and did not fully explore the underlying information of features learned by deep neural networks. To address the above-mentioned problems, we propose an end-to-end deep neural network for multispectral change detection, named difference-enhancement triplet network (DETNet). DETNet mainly includes two modules: the triplet feature extraction module and the difference feature learning module. First, the triplet feature extraction module uses the triple CNN as the backbone to extract representative spatial–spectral features. Second, the difference feature learning module mines the underlying information of difference representations of learned spatial–spectral features to detect subtle changes. Finally, the model uses a compound loss function, which includes triplet loss, contrastive loss, and cross-entropy loss, to guide DETNet toward learning more discriminative features. Extensive experimental results of the proposed DETNet and other state-of-the-art methods on four datasets demonstrate its superiority.