A review of remote sensing image road extraction research based on deep learning

Lin Gao; Chen Chen

doi:10.1117/12.2675173

25 May 2023 A review of remote sensing image road extraction research based on deep learning

Lin Gao, Chen Chen

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126360E (2023) https://doi.org/10.1117/12.2675173
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

Road information plays a fundamental role in many application fields. The optical remote sensing image can analyze and interpret the ground object with its high resolution, which is of great help to extract the road target. In recent years, with the support of rich sample set, the road extraction method based on deep learning has stronger applicability than the traditional road extraction method. Making full use of deep learning for accurate road extraction has become a hot and frontier issue in the field of remote sensing. In view of this, based on a large number of related literatures in recent years, this paper analyzes and evaluates the road extraction methods based on deep learning and traditional road extraction methods, and finally puts forward some suggestions and prospects for the development of road extraction from optical remote sensing images.

Citation Download Citation

Lin Gao and Chen Chen "A review of remote sensing image road extraction research based on deep learning", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126360E (25 May 2023); https://doi.org/10.1117/12.2675173

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KEYWORDS

Roads

Remote sensing

Feature extraction

Deep learning

Image segmentation

Convolution

RGB color model

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