LAUSR

As the basic unit of farmland, parcel is crucial for remote sensing tasks, such as urban management. Previous studies of farmland parcels extraction are based on boundary detection and instance segmentation methods. However, these methods perform poorly in the parcels with complex shape and fuzzy boundary due to the insufficient feature extraction capability. Moreover, for the lack of multiscale features extraction and fusion, they are difficult to extract different scale farmland parcels accurately. Based on these issues, we propose a fuzzy-boundary enhanced trident network, named FBETNet, to enhance the feature of fuzzy boundary and generate multiscale parcels. First, a semantic-guided multitask strategy is introduced in order to enhance the feature of fuzzy boundary. Second, we design a multiscale trident module to further improve the performance of multiscale feature extraction. Finally, an adversarial data augmentation strategy is employed in the training phase to strengthen the robustness and stability of our proposed method. Experiments show that our proposed method improves significantly in both accuracy and visualization, especially for the parcels with fuzzy boundary and complex shape.