Optimal ground control points for geometric correction using genetic algorithm with global accuracy

Nguyen, Thanh 2015, Optimal ground control points for geometric correction using genetic algorithm with global accuracy, European journal of remote sensing, vol. 48, pp. 101-120, doi: 10.5721/EuJRS20154807.

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Title Optimal ground control points for geometric correction using genetic algorithm with global accuracy
Author(s) Nguyen, ThanhORCID iD for Nguyen, Thanh orcid.org/0000-0001-9709-1663
Journal name European journal of remote sensing
Volume number 48
Start page 101
End page 120
Total pages 20
Publisher Associazione Italiana Telerilevamento
Place of publication Cagliari, Italy
Publication date 2015
ISSN 2279-7254
Keyword(s) Geometric correction
georeferencing
ground control point
global accuracy
genetic algorithm
Voronoi diagram
Summary Establishing the ground control point (GCP) network is a pre-requisite for georeferencing raw image data. Given current typical digital spatial database quality, much interest among users is about the accuracy of the geometric correction model that yields the final product. This paper reports an approach to optimizing GCP assembly using a genetic/evolution algorithm. The paper also suggests an optimal criterion for accuracy assessment through appraisal of global accuracy of the transformation, which is computed at each point of the image space. Experimental results demonstrate that the proposed approach has a great potential for selection of the best GCPs, and considerable improvement to the accuracy of geometric correction models can be expected when it is implemented.
Language eng
DOI 10.5721/EuJRS20154807
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Associazione Italiana Telerilevamento
Persistent URL http://hdl.handle.net/10536/DRO/DU:30074830

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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