Extracting urban road area based on combination of trajectory continuity and image feature similarity

doi:10.11947/j.AGCS.2020.20190366

Abstract

Abstract: Urban road area detecting is the imperious demand in the area of management of city land use, transportation planning and so on. Trajectory extraction, remote sensing image classification and artificial collection are the traditional methods for road network detection with some limits on automation degree or extraction quality. This paper proposes a method for detecting road area in high-resolution remote sensing image based on trajectory continuity and image feature similarity, and this method utilizes the advantages of GNSS trajectory and remote sensing image. The proposed methods could be divided into three steps: firstly, using GNSS trajectory points to construct images of trajectory feature and selecting the high-confidence grids with high density value. Secondly, generating road objects based on trajectory continuity in average direction feature image. Thirdly, dividing high-resolution remote sensing image into several small areas by using road segments and extending road areas based on image feature similarity automatically to detect roads which not covered by trajectory. The experiment evidences that this method could detect road areas efficiency and accuracy in high-resolution remote sensing image and decreasing the bad effect on the different roads with different spectrums. Compared with the traditional remote sensing image classification methods, the proposed method has a higher precision and automatic degree.

Key words: high-resolution remote sensing image, taxi trajectory, trajectory continuity, similarity of spectrum feature, road area detection

CLC Number:

P237

FANG Zhixiang, ZHONG Haoyu, ZOU Xinyan. Extracting urban road area based on combination of trajectory continuity and image feature similarity[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(12): 1554-1563.

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