A transformer-based Siamese network and an open optical dataset for semantic change detection of remote sensing images

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, WHU_CD, 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.