The extraction of the desert roads based on the U-Net network from remote sensing images

Xubing Zhang; Can Li; Shiwei Shao; Yuxin Peng

doi:10.1117/12.2625577

9 December 2021 The extraction of the desert roads based on the U-Net network from remote sensing images

Xubing Zhang, Can Li, Shiwei Shao, Yuxin Peng

Proceedings Volume 12129, International Conference on Environmental Remote Sensing and Big Data (ERSBD 2021); 121290C (2021) https://doi.org/10.1117/12.2625577
Event: 2021 International Conference on Environmental Remote Sensing and Big Data, 2021, Wuhan, China

Abstract

The automatic extraction of the roads from the remote sensing images is significant for the monitoring of the sandstorm hazards to the traffic arteries of the desert area. Since the current pixel-based road or the object-oriented extraction methods are easy to cause the noises or the adhesion phenomena, the U-Net deep learning network is applied to extract the desert roads from the Google Earth, GF-2 and JiLin-1 image datasets in this paper. Firstly, in order to improve the generalization ability of the U-Net network model, the datasets are expanded by means of rotating, mirroring, contrast stretching, and intensity dithering. Then under the constraints of the hyperparameters, the U-Net model is built and trained until the loss function value tends to be stable. Finally, the U-Net algorithm is adopted to extract the highways pass through the Takramakan desert and the Kumtg desert areas in western China. The experimental results demonstrate that the U-Net algorithm is efficient and performable.

Citation Download Citation

Xubing Zhang, Can Li, Shiwei Shao, and Yuxin Peng "The extraction of the desert roads based on the U-Net network from remote sensing images", Proc. SPIE 12129, International Conference on Environmental Remote Sensing and Big Data (ERSBD 2021), 121290C (9 December 2021); https://doi.org/10.1117/12.2625577

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KEYWORDS

Roads

Remote sensing

Data modeling

Image segmentation

Spatial resolution

Network architectures

Networks

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