基于PPMU-net的多特征高分辨率遥感道路提取  

Multi-feature High-Resolution Remote Sensing Road Extraction Based on PPMU-net

作　　者：张永宏[1] 严斌 田伟[2] 王剑庚 ZHANG Yonghong;YAN Bin;TIAN Wei;WANG Jiangeng(School of Automation,Nanjing University of Information Science&Technology,Nanjing 210044,China;School of Computer and Software,Nanjing University of Information Science&Technology,Nanjing 210044,China;School of Atmospheric Science,Nanjing University of Information Science&Technology,Nanjing 210044,China)

机构地区：[1]南京信息工程大学自动化学院,南京210044 [2]南京信息工程大学计算机与软件学院,南京210044 [3]南京信息工程大学大气科学学院,南京210044

出　　处：《计算机工程与应用》2021年第1期200-206,共7页Computer Engineering and Applications

基　　金：国家自然科学基金国际(地区)合作与交流项目(41661144039)。

摘　　要：针对复杂地形条件下道路特征选取不具代表性,分割精度低的问题,提出了一种基于卷积神经网络(PPMUnet)的高分辨率遥感道路提取的方法。将3通道的高分二号光谱信息与相应的地形信息(坡度、坡向、数字高程信息)进行多特征融合,合成6通道的遥感图像;对多特征的遥感图像进行切割并利用卷积网络(CNN)筛选出含道路的图像;将只含道路的遥感图像送进PPMU-net中训练,构建出高分辨率遥感图像道路提取模型。在与U-net神经网络、PSPnet神经网络相比时,所提的方法在对高分辨率遥感道路提取时能够达到较好的效果,提高了复杂地形条件下道路分割的精度。To solve the problem of unrepresentative selection of road features and low segmentation accuracy under complex terrain conditions,a high resolution remote sensing road extraction method based on the convolutional neural network(PPMU-net)is proposed.Firstly,the Gaofen-2 satellite spectral information of the three channels and the corresponding topographic information(slope,aspect and dem)are integrated into multi-feature to synthesize the remote sensing image of the six channels.Secondly,the multi-feature remote sensing images are cut and the images containing roads are screened by Convolutional Neural Network(CNN).Finally,the remote sensing images containing only roads are sent to PPMU-net for training,and the road extraction model of high-resolution remote sensing images is constructed.Compared with U-Net and PSPNet,the proposed method can achieve better results in the extraction of high-resolution remote sensing roads and improve the accuracy of road segmentation under complex terrain conditions.

关 键 词：高分二号 PPMU-net神经网络 多特征 复杂地形 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]