Urban road extraction with fusion of high-resolution images and LiDAR data

doi:10.13474/j.cnki.11-2246.2021.0166

Abstract

Abstract: In order to avoid the problem of inaccurate road information extraction from remote sensing images due to the complexity of urban roads and the shadow occlusion of trees and buildings. The method of fusion of high-resolution images and LiDAR data is used to achieve the extraction of urban roads, and a post-processing improvement method based on the minimum area boundary rectangle (MABR) is proposed to improve the extraction. First, we realize the data registration in the area. Then, we apply the FNEA algorithm for image segmentation and use the random forest classification method for classification, apply image fusion and object shape index and other related operators to the road extraction. Finally we extract complete road information by removing the vegetation and buildings and improving the road filling. The results show that the road integrity of Toronto and Tai’an Study area is 95.41% and 90.84%, and the accuracy is 83.07% and 85.63%. This method can effectively remove false road information, improve the completeness of road extraction, and achieve better road information extraction.

Key words: road extraction, image segmentation, random forest classification, LiDAR, MABR

CLC Number:

P237

LIU Maohua, LI Manwen. Urban road extraction with fusion of high-resolution images and LiDAR data[J]. Bulletin of Surveying and Mapping, 2021, 0(6): 1-5,60.

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