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A discovery about the positional distribution pattern among candidate homologous pixels and its potential application in aerial multi-view image matching

Lookup NU author(s): Dr Wen Xiao

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© 2021, The Author(s).In aerial multi-view photogrammetry, whether there is a special positional distribution pattern among candidate homologous pixels of a matching pixel in the multi-view images? If so, can this positional pattern be used to precisely confirm the real homologous pixels? These problems have not been studied at present. Therefore, the study of the positional distribution pattern among candidate homologous pixels based on the adjustment theory in surveying is investigated in this paper. Firstly, the definition and computing method of pixel’s pseudo object-space coordinates are given, which can transform the problem of multi-view matching for confirming real homologous pixels into the problem of surveying adjustment for computing the pseudo object-space coordinates of the matching pixel. Secondly, according to the surveying adjustment theory, the standardized residual of each candidate homologous pixel of the matching pixel is figured out, and the positional distribution pattern among these candidate pixels is theoretically inferred utilizing the quantitative index of standardized residual. Lastly, actual aerial images acquired by different sensors are used to carry out experimental verification of the theoretical inference. Experimental results prove not only that there is a specific positional distribution pattern among candidate homologous pixels, but also that this positional distribution pattern can be used to develop a new object-side multi-view image matching method. The proposed study has an important reference value on resolving the defects of existing image-side multi-view matching methods at the mechanism level.

Publication metadata

Author(s): Zhang K, Xiao W, Sheng Y, Wang J, Zhang S, Ye L

Publication type: Article

Publication status: Published

Journal: Scientific Reports

Year: 2021

Volume: 11

Issue: 1

Online publication date: 12/05/2021

Acceptance date: 27/04/2021

Date deposited: 03/06/2021

ISSN (electronic): 2045-2322

Publisher: Nature Research

URL: https://doi.org/10.1038/s41598-021-89501-z

DOI: 10.1038/s41598-021-89501-z

PubMed id: 33980928

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