A New Productive Framework for Point-based Matching of Oblique Aircraft and UAV-based Images

Bas S., OK A. Ö.

PHOTOGRAMMETRIC RECORD, vol.36, no.175, pp.252-284, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 36 Issue: 175
  • Publication Date: 2021
  • Doi Number: 10.1111/phor.12374
  • Journal Name: PHOTOGRAMMETRIC RECORD
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Compendex, Geobase, INSPEC, DIALNET, Civil Engineering Abstracts
  • Page Numbers: pp.252-284
  • Keywords: graph-cut RANSAC, oblique aircraft images, point-based feature matching, robust detection, UAV images, SAMPLE CONSENSUS, SQUARES
  • Hacettepe University Affiliated: Yes

Abstract

A novel productive framework for point-based feature matching of oblique aircraft and UAV imagery is presented. The proposed framework makes use of the powerful AKAZE descriptor for feature extraction and an iterative scheme is developed to construct as many tentative matches as possible. During the iterations, cross checks, together with Lowe's nearest-next distance ratio test, are used to filter erroneous matches. In order to extract putative matches from the tentative matches, three robust approaches, including graph-cut RANSAC, are evaluated along with the epipolar constraint enforced between the two datasets. The developed framework was validated using the ISPRS image orientation benchmark dataset and yielded successful results in terms of matching precision, even for some difficult cases. The results also outperformed the results of previously developed approaches in the same context.