Road Extraction Method Combining Convolutional Neural Network and Tensor Voting

Deep learning is widely used in road extraction by training samples for feature recognition. This method is not limited to a specific type of image; however, the extracted road will be broken and noisy owing to the restriction of the number of training samples and computer hardware. Owing to the above problems, in this study, we use a VGG convolutional neural network to preliminarily extract roads and introduce a tensor voting method for optimization. First, multi-mode expansion of the samples is performed via image transformation, random cropping, and oversampling, and then, a VGG convolutional neural network model is trained. Second, the network is used to segment the road from the original image. Then, the tensor voting for the binary images of the road surface is used to obtain saliency information about the road. Finally, the threshold of significant information is set to obtain the road surface in the feature extraction process. The experimental results show that the recall rate and precision of the extracted road obtained by the proposed method arc more than 90%, and the proposed method has a higher accuracy than other traditional methods, which verifies the effectiveness of the proposed method.