A Self-Supervised Learning Framework for Road Centerline Extraction From High-Resolution Remote Sensing Images

Abstract

Road extraction from the high-resolution remote sensing image is significant for the land planning, vehicle navigation, etc. The existing road extraction methods normally need many preprocessing and subsequent optimization steps. Therefore, an automatic road centerline extraction method based on the self-supervised learning framework for high-resolution remote sensing image is proposed. This proposed method does not need to manually select training samples and other optimization steps, such as the nonroad area removing. First, the positive sample selection method combining the spectral and shape features is proposed to extract the road sample. Then, the one-class classifier framework is introduced and the random forest positive unlabeled learning classifier is constructed to get the posterior probability of the pixel belonging to road. The shape feature and the posterior probability are combined to form the final road network in the object-oriented way. Finally, the road centerline is obtained through the tensor voting algorithm. In order to verify the effectiveness of the proposed algorithm, high-resolution remote sensing images and benchmark datasets are used to do experiments. The indexes of the completeness ratio, the correctness ratio, and the detection quality are used for the quantitative accuracy evaluation. Compared with the supervised, the unsupervised, and the one-class classification road extraction algorithms, this proposed algorithm achieves high accuracy and efficiency. For the deep learning method comparison, the deep learning method performs well in most cases especially in the complex urban area. However, the deep learning method needs a large number of samples and a long training time, and our self-supervised learning framework does not need the training samples.