基于U-Net、U-Net++和Attention-U-Net网络的遥感影像水体提取

Remote sensing image water body extraction based on U-Net,U-Net++and Attention-U-Net networks

目前,深度学习在高分辨率遥感影像水体提取方面的应用已成为遥感领域的研究热点。其中基于U-Net网络的算法在水体提取中表现出较好的性能,但鲜有研究对不同U-Net网络算法在水体提取任务中的性能差异进行深入比较。因此,本文选择U-Net、U-Net++和Attention-U-Net 3种卷积神经网络,基于GID数据集,进行试验与定量分析。结果表明:U-Net++的训练精度最高,其次为U-Net、Attention-U-Net,三者分别为0.912、0.907、0.899;U-Net++的边缘提取能力优于其他两种网络;在分割不同类型水体和区分遥感影像中与水体区域相似的非水体区域上,U-Net++的提取效果显著,U-Net和Attention-U-Net易出现漏提现象,效果欠佳。

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.