DTHNet: Dual-Stream Network Based on Transformer and High-Resolution Representation for Shadow Extraction from Remote Sensing Imagery
Shuang Zhang, Yungang Cao, Baikai Sui · IEEE Geoscience and Remote Sensing Letters · 2023
Shadow extraction from remote sensing images is critical work. However, the inter-class similarity of shadows with dark water, trees, and roads and the dependence on other objects make accurate shadow extraction still challenging. This letter proposes a dual-stream network based on the transformer and high-resolution representation (DTHNet) for multi-scale shadow extraction. The DTHNet utilizes two streams: the high-resolution representation stream extracts deep features while maintaining detail information, and the clustering representation stream provides clustering feature constraints to enhance the ability to distinguish between foreground and background. Additionally, the designed multi-scale auxiliary predictor and hybrid loss aid in extracting multi-scale shadows. We evaluated our proposed method on the AISD dataset and compared it against six state-of-the-art generic semantic segmentation models and shadow extraction methods. The experimental results demonstrate that the DTHNet outperforms the existing methods.