Automatic road extraction from high resolution remote sensing image by means of topological derivative and mathematical morphology

Hongyu Zhou; Xu Song; Guoying Liu

doi:10.1117/12.2282998

8 March 2018 Automatic road extraction from high resolution remote sensing image by means of topological derivative and mathematical morphology

Hongyu Zhou, Xu Song, Guoying Liu

Author Affiliations +

Proceedings Volume 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 1061104 (2018) https://doi.org/10.1117/12.2282998
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

Abstract

Automatic road extraction from High Resolution Remote Sensing Image is a challenging problem. In this paper we present a new approach for road automatically extraction which is based on topological derivative and mathematical morphology. This approach for road extraction can be divided into three main steps: using topological derivative for image segmentation, using mathematical morphology for road network identification and filtering. The experimental results show that this approach can effectively extract roads from high-resolution remote sensing image.

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Hongyu Zhou, Xu Song, and Guoying Liu "Automatic road extraction from high resolution remote sensing image by means of topological derivative and mathematical morphology", Proc. SPIE 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 1061104 (8 March 2018); https://doi.org/10.1117/12.2282998

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