What is it about?
In this paper, we propose an enhanced multi-view vertical line locus (EMVLL) matching algorithm based on positioning consistency for aerial or space images. Experimental results show that the EMVLL method successfully solves the problems associated with the TMVLL (traditional multi-view vertical line locus) method, and has greater reliability, accuracy and computing efficiency.
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Why is it important?
The algorithm considers multi-view image matching of the ground primitive with known object space plane coordinates and follows the traditional vertical line locus method. The proposed method uses synthetic object space information of the ground primitive and image space information of multi-view images, and fuses the processes of multi-view image matching and correctness validation of the matching result. These improvements aim to address the three defects of traditional VLL matching methods.
Perspectives
The TMVLL matching method has many shortcomings, including low computing efficiency, giving only one elevation and lacking an accurate means of validating the returned result. Considering the shortcomings of the TMVLL method, the paper proposes several targeted improvements, resulting in the new EMVLL method, which yields multiple elevations and includes a validation process for its results. The results show that the EMVLL method successfully resolves the problems associated with the TMVLL method. The EMVLL method obtains elevations that are closer to the actual elevation of ground objects located on the primitive and has greater reliability, accuracy, and efficiency than the TMVLL method. The proposed EMVLL algorithm represents an important advance in digital photogrammetry image matching methods.
Dr Ka Zhang
Nanjing Normal University
Read the Original
This page is a summary of: An enhanced multi-view vertical line locus matching algorithm of object space ground primitives based on positioning consistency for aerial and space images, ISPRS Journal of Photogrammetry and Remote Sensing, May 2018, Elsevier,
DOI: 10.1016/j.isprsjprs.2018.03.017.
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