关键词: 高分二号, PPMU-net神经网络, 多特征, 复杂地形

Abstract:

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.

Key words: Gaofen-2 satellite, PPMU-net Neural Network, multi-features fusion, complex terrain