Study of remote sensing detection method for road obstacle and accessibility evaluation

doi:10.6046/gtzyyg.2020.02.13

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Abstract

In view of the defects of the existing road obstacle detection methods, such as requirement for high registration accuracy, influence by imaging conditions , low-level automation and the need for professional operation, this paper proposes a road obstacle detection method based on reverse feature matching and accessibility evaluation method. On the basis of SIFT feature extraction algorithm, this method detects obstacles by acquiring the set of feature points that are not matched in the road buffer area of the disaster image, then obtains the distribution range and shape of obstacles by using a variety of sub-point region growing algorithms, and finally evaluates the road accessibility by overlapping analysis with road vector data. The experimental results show that this method can effectively extract the position information and shape information of obstacles.

Keywords high-resolution remote sensing image obstacle detection SIFT region growth accessibility

TP79

Corresponding Authors: Haoping QI E-mail: qhp@seu.edu.cn

Issue Date: 18 June 2020

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	Jinjie KANG
	Haoping QI
	Qinghua YANG
	Hua CHEN

Cite this article:

Jinjie KANG,Haoping QI,Qinghua YANG, et al. Study of remote sensing detection method for road obstacle and accessibility evaluation[J]. Remote Sensing for Land & Resources, 2020, 32(2): 94-102.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.02.13 OR https://www.gtzyyg.com/EN/Y2020/V32/I2/94

Fig.1 Influence of Image contrast on the feature points detection

Fig.2 Obstacle feature point screening

Fig.3 Obstacle feature point grouping

Fig.4 Recognition and merging of feature points on different side of the same obstacle

Fig.5 Obtain standard road samples

Fig.6 Calibrate properties of candidate points

Tab.1 Properties of candidates points

Tab.2 Accessibility analysis

Fig.7 Post-disaster image and obstacle distribution

Fig.8 Feature points in road ROI

Fig.9 Examples of obstacle feature points

Fig.10 Examples of obstacle candidate points

Tab.3 Calibration results

Fig.11 Obstacle detection results

Tab.4 Obstacle extraction result

Fig.12 Overlay analysis

Fig.13 Feature points in road ROI

Fig.14 Screening and grouping of feature points

Fig.15 Obstacle detection results

Tab.5 Obstacle extraction result

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