基于改进U-Net网络的高分遥感影像水体提取  

Water Body Extraction of High Resolution Remote Sensing Image based on Improved U-Net Network

作　　者：何红术 黄晓霞 李红旮 倪凌佳 王新歌 陈崇 柳泽[3] HE Hongshu;HUANG Xiaoxia;LI Hongga;NI Lingjia;WANG Xinge;CHEN Chong;LIU Ze(Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China;University of Chinese Academy of Sciences,Beijing 100049,China;Urban and Rural Planning Management Center of the Ministry of Housing and Urban-Rural Development,Beijing 100835,China)

机构地区：[1]中国科学院空天信息创新研究院,北京100094 [2]中国科学院大学,北京100049 [3]住房和城乡建设部城乡规划管理中心,北京100835

出　　处：《地球信息科学学报》2020年第10期2010-2022,共13页Journal of Geo-information Science

基　　金：国家重点研发计划项目(2017YFB0503905);国家自然科学基金项目(41971363)。

摘　　要：传统基于光谱信息的水体提取未能考虑水体形状、纹理、大小、相邻关系等问题,且存在同物异谱、异物同谱现象,导致水体提取精度较低。而传统基于分类提取水体方法设计特征过程较为繁琐,且不能挖掘深度信息特征。因此,本文提出改进的U-Net网络语义分割方法,借鉴经典U-Net网络的解编码结构对网络进行改进:①将VGG网络用于收缩路径以提取特征;②在扩张路径中对低维特征信息进行加强,将收缩特征金字塔上一层的特征图与下一层对应扩张路径上的特征图进行融合,以提高提取结果分割精度;③在分类后处理中引入条件随机场,以将分割结果精细化。在保持相同训练集、验证集和测试集的情况下,分别用SegNet、经典U-Net网络和改进的U-Net网络做对照试验。试验结果表明,改进的U-Net网络结构在IoU、精准率和Kappa系数指标上均高于SegNet和经典U-Net网络,与SegNet相比,3项指标分别提升了10.5%、12.3%和0.14,与经典U-Net网络结果相比,各个指标分别提升了5.8%、4.4%和0.05。改进的网络水体提取结果较为完整,对小目标水体能够准确提取。改进的U-Net网络能够有效地实现水体提取任务。There are two main methods of traditional water body extraction:a method based on spectral information and a method based on classification.Traditional water body extraction methods based on spectral information fail to take into account features such as water body shape,internal texture,water body size,and adjacent relations of water body.Also,there is a common phenomenon of“same object with different spectra and same spectrum with different objects”,which could result in low accuracy of water body extraction.Thus,the traditional methods that design features based on classification to extract water body is complex and impossible to capture the deep information of water body features.This paper proposed an improved U-Net network semantic segmentation method,which uses the de-encoding structure of the classic U-Net network to improve the network:①Use the VGG network to shrink the path and increase the depth of the network to extract deep features of the water;②Strengthen the low-dimensional feature information in the expansion path,fuse the feature map on the next layer of the shrinking feature pyramid with the feature map on the corresponding expansion path in the next layer,and enhance the model's low-dimensional feature information to improve the classification accuracy of the model;and③The Conditional Random Feld(CRF)was introduced in the post-classification process to refine the segmentation results and improve the segmentation accuracy.In the study of Qingdao area,SegNet,classic U-Net network,and improved U-Net network were selected as controlled experiments while maintaining the same training set,validation set,and test set.The test results show that the improved U-Net network structure performed better than SegNet and classic U-Net networks in terms of IoU,accuracy rate and Kappa coefficient.Compared with SegNet,the three indicators increased by 10.5%,12.3%,and 0.14,respectively.Compared with the results of the classic U-Net network,each indicator increased by 5.8%,4.4%and 0.05,respectively.The r

关 键 词：U-Net 水体提取 高分遥感影像 条件随机场 图像分割 VGG16 青岛 西宁 

分 类 号：P332[天文地球—水文科学] TP751[水利工程—水文学及水资源]