基于U-Net优化的SAR遥感图像语义分割  

Semantic Segmentation of SAR Remote Sensing Image Based on U-Net Optimization

作　　者：王鑫 张昊宇 凌诚[1] WANG Xin;ZHANG Hao-yu;LING Cheng(School of Information Science and Technology,Beijing University of Chemical Technology,Beijing 100000,China;School of Information Engineering,Zhejiang Ocean University,Zhoushan,Zhejiang 316000,China)

机构地区：[1]北京化工大学信息科学与技术学院,北京100000 [2]浙江海洋大学信息工程学院,浙江舟山316000

出　　处：《计算机科学》2021年第S02期376-381,共6页Computer Science

摘　　要：多光谱图像的分割是遥感图像解译的重要基础环节,SAR遥感图像中包含着复杂的地物目标信息,传统的分割方法存在耗时长、效率低等问题,导致传统图像分割方法的应用受限。近年来,深度学习算法在计算机视觉方向的应用取得了较好的成果,针对多光谱遥感影像语义分割问题,使用深度学习的语义分割方法来实现遥感影像的高性能分割,在U-Net网络结构上添加激活层、Dropout层、卷积层,提出一种基于U-Net优化的深度卷积神经网络,在少量数据集的基础上实现了对以SAR图像合成的多光谱影像中耕地、建筑、河流的快速检测,整体分割准确率达94.6%。与U-Net,SegNet的对照实验结果表明,所提方法的分割准确率相比U-Net,SegNet整体较优,相比U-Net和SegNet分别提升了2.5%与5.8%。Multi-spectral image segmentation is an important basic link in remote sensing image interpretation.SAR remote sensing images contain complex object information.Traditional segmentation methods have problems such as time-consuming and low efficiency,which limits the application of traditional image segmentation methods.In recent years,the application of deep learning algorithms in the direction of computer vision has achieved good results.Aiming at the problem of semantic segmentation of multi-spectral remote sensing images,deep learning semantic segmentation methods are used to achieve high-performance segmentation of remote sensing images.In the U-Net network structure Above,add activation layer,dropout layer,convolutional la-yer,and propose a deep convolutional neural network optimized based on U-Net.On the basis of a small amount of data set,it realizes for rapid detection of buildings and rivers,the overall segmentation accuracy rate reaches 94.6%.The comparison test results with U-Net and SegNet show that the segmentation accuracy of the method used in this paper is better than that of U-Net and SegNet.Compared with U-Net and SegNet,it has increased by a minimum of 2.5%and 5.8%,respectively.

关 键 词：U-Net 多光谱 深度学习 SAR 遥感影像 语义分割 

分 类 号：TN959[电子电信—信号与信息处理]