Remote sensing image water body extraction based on U-Net,U-Net++and Attention-U-Net networks
Currently,the application of deep learning in the extraction of water bodies from high-resolution remote sensing images has become a research hotspot in the remote sensing field. Among them,algorithms based on the U-Net network have demonstrated good performance in water body extraction. However,there is scarce research that provides in-depth and detailed comparisons of the performance differences of different U-Net network algorithms in water body extraction tasks. Therefore,this article selects three convolutional neural networks,named U-Net,U-Net++,and Attention-U-Net,and based on the GID dataset,draws conclusions through experiments and quantitative analysis. The results indicate that:U-Net++achieves the highest training accuracy,followed by U-Net and Attention-U-Net,with accuracies of 0.912,0.907,and 0.899 respectively. U-Net++exhibits superior edge extraction capability compared to the other two networks. In segmenting different types of water bodies and distinguishing non-water areas similar to water bodies in remote sensing images,U-Net++shows significantly better extraction results,while U-Net and Attention-U-Net are prone to omission errors and exhibit suboptimal performance.
water body extractionhigh-resolution remote sensing imageryU-Net