Automated railroad reconstruction from remote sensing image based on texture filter

Jie Xiao; Kaixia Lu

doi:10.1117/12.2285197

8 March 2018 Automated railroad reconstruction from remote sensing image based on texture filter

Jie Xiao, Kaixia Lu

Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 1060910 (2018) https://doi.org/10.1117/12.2285197
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

Abstract

Techniques of remote sensing have been improved incredibly in recent years and very accurate results and high resolution images can be acquired. There exist possible ways to use such data to reconstruct railroads. In this paper, an automated railroad reconstruction method from remote sensing images based on Gabor filter was proposed. The method is divided in three steps. Firstly, the edge-oriented railroad characteristics (such as line features) in a remote sensing image are detected using Gabor filter. Secondly, two response images with the filtering orientations perpendicular to each other are fused to suppress the noise and acquire a long stripe smooth region of railroads. Thirdly, a set of smooth regions can be extracted by firstly computing global threshold for the previous result image using Otsu's method and then converting it to a binary image based on the previous threshold. This workflow is tested on a set of remote sensing images and was found to deliver very accurate results in a quickly and highly automated manner.

Citation Download Citation

Jie Xiao and Kaixia Lu "Automated railroad reconstruction from remote sensing image based on texture filter", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 1060910 (8 March 2018); https://doi.org/10.1117/12.2285197

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