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Recent change detection (CD) methods focus on the extraction of deep change semantic features. However, existing methods overlook the fine-grained features and have the poor ability to capture long-range space–time information, which leads to the micro changes missing and the edges of change types smoothing. In this paper, a potential transformer-based semantic change detection (SCD) model, Pyramid-SCDFormer is proposed, which precisely recognizes the small changes and fine edges details of the changes. The SCD model selectively merges different semantic tokens in multi-head self-attention block to obtain multiscale features, which is crucial for extraction information of remote sensing images (RSIs) with multiple changes from different scales. Moreover, we create a well-annotated SCD dataset, Landsat-SCD with unprecedented time series and change types in complex scenarios. Comparing with three Convolutional Neural Network-based, one attention-based, and two transformer-based networks, experimental results demonstrate that the Pyramid-SCDFormer stably outperforms the existing state-of-the-art CD models and obtains an improvement in MIoU/F1 of 1. 11/0. 76%, 0. 57/0. 50%, and 8. 75/8. 59% on the LEVIR-CD, WHUCD, and Landsat-SCD dataset respectively. For change classes proportion less than 1%, the proposed model improves the MIoU by 7. 17–19. 53% on Landsat-SCD dataset. The recognition performance for small-scale and fine edges of change types has greatly improved.
Yuan et al. (Mon,) studied this question.