A Semi-Supervised Pyramid Cross-Temporal Attention Transformer for Change Detection in High-Resolution Remote Sensing Images

The vision transformer (ViT) model has the advantage of being able to model the long-range dependencies in the imagery and has been studied for the task of remote sensing image change detection (CD). However, the performance of the existing transformer-based CD methods is not satisfactory in the case of limited labeled data. The original self-attention mechanism cannot effectively extract the change information, and the large number of parameters in the ViT model makes the model difficult to train. To solve the above-mentioned problems, a semi-supervised pyramid cross-temporal attention transformer for change detection (CT ² RCDSS) is proposed in this letter. The CT ² RCDSS method follows an encoder-decoder structure. The encoder utilizes a dual-branch structure, containing the combination of the proposed cross-temporal attention (PCTA) and pyramid self-attention (PSA) mechanisms, which is designed to consider the interaction of the features from different time phases and enhance the changes at different scales. In the decoder, a series of deconvolutional layers with skip connections are utilized, and a Softmax layer follows to acquire the final binary change map. In addition, a semi-supervised training strategy, which reduces the errors in the pseudo-labels generated from the models initialized with different parameters, is used to improve the model stability while using unlabeled data. The experiments showed that the proposed method can achieve a superior F1-score and intersection over union (IoU), which indicates the potential of the proposed method.