基于PPMU-net的多特征高分辨率遥感道路提取

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（PPMU-net）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。

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

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.