LAUSR

Farmland segmentation scenario from remote sensing images plays an important role in crop growth monitoring, precision agriculture, and intelligent agriculture. To achieve high precision segmentation of farmland boundary, a Multitask Deformable UNet combined Enhanced (MDE-UNet) network is proposed for farmland boundary segmentation. The network consists of two parts: a Multitask Deformable UNet (MD-UNet) segmentation module with Deformable UNet (D-UNet) as the basic network and an enhancement module with a lightweight UNet improved by residual attention. In the MD-UNet segmentation module, three branches are used for precise segmentation of deterministic, fuzzy, and raw boundary, respectively. In the enhancement module, an improved lightweight UNet is designed, which can enhance the feature extraction ability of the MD-UNet segmentation module and further improve the segmentation accuracy. The accuracy and mIoU in the GF-2 farmland segmentation test dataset can reach 96.41% and 91.29% using the proposed model, respectively. The MDE-UNet method outperforms other representative deep learning methods such as DeepLab v3+, FCN-8 s, SegFormer, and UTNet, and has potential for practical applications of farmland boundary segmentation.