Study on Elimination Algorithms for Line Segment Mismatches-Reference-Cited by-同舟云学术

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Study on Elimination Algorithms for Line Segment Mismatches

Published:2022-04-01 Issue:3 Volume:48 Page:400-410
ISSN:0703-8992
Container-title:Canadian Journal of Remote Sensing
language:en
Short-container-title:Canadian Journal of Remote Sensing

Author:

Li Chang¹²,Jia Wenqi²,Wei Dong³

Affiliation:

1. Key Laboratory for Geographical Process Analysis & Simulation, Hubei Province, China

2. College of Urban and Environmental Science Central, China Normal University, Wuhan, Hubei Province, 430079, China

3. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei Province, 430079, China

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/07038992.2022.2052032

Reference27 articles.

1. MAGSAC++, a Fast, Reliable and Accurate Robust Estimator

2. Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses

3. Chen, M., Yan, S. H., Huang, S. Z., and Zhu, Q. 2017. “Accurate and reliable line segment matching improvements in oblique image processing.” Imaging and Geospatial Technology Forum, Baltimore, Maryland, USA, March 2017.

4. Matching with PROSAC — Progressive Sample Consensus

5. The DGPF-Test on Digital Airborne Camera Evaluation Overview and Test Design

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