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

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.