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A heuristic approach to learning new graph structures for remote sensing images


Salmon, BP and Kleynhans, W and Olivier, JC and Schwegmann, CP, A heuristic approach to learning new graph structures for remote sensing images, Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 10-15 July 2016, Beijing, China, pp. 3051-3054. ISBN 978-1-5090-3332-4 (2016) [Refereed Conference Paper]

Copyright Statement

Copyright 2016 IEEE

DOI: doi:10.1109/IGARSS.2016.7729789


A probability graph model can effectively model spectral and spatial dependencies within remote sensing images for land cover classification. The most common structure used to unify this probabilistic information is a second order Markov network that encapsulate unary and pairwise potentials. In this paper we explore various heuristics to discover new graph structures that will assist with classifying land cover. Experiments were conducted to compare classification accuracies in two study areas; one homogeneous and one heterogeneous located in the Kwazulu-Natal province, South Africa.

Item Details

Item Type:Refereed Conference Paper
Keywords:context awareness, graph theory, image classification, Markov random fields and remote sensing
Research Division:Engineering
Research Group:Geomatic engineering
Research Field:Photogrammetry and remote sensing
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in engineering
UTAS Author:Salmon, BP (Dr Brian Salmon)
UTAS Author:Olivier, JC (Professor JC Olivier)
ID Code:114643
Year Published:2016
Deposited By:Engineering
Deposited On:2017-02-22
Last Modified:2017-07-11
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