DocumentCode :
3081838
Title :
Semantic rich ICM algorithm for VHR satellite images segmentation
Author :
Sublime, Jeremie ; Troya-Galvis, Andres ; Bennani, Younes ; Cornuejols, Antoine ; Gancarski, Pierre
Author_Institution :
INRA, MIA, AgroParisTech, Paris, France
fYear :
2015
fDate :
18-22 May 2015
Firstpage :
45
Lastpage :
48
Abstract :
In this article we show some applications of a MRF-based segmentation algorithm applied to real data extracted from a very high resolution image. This algorithm has specific features that enable the extraction of semantic information on the clusters in the form of affinity and geographic position properties. The results of the experiments conducted on this data set are interesting both in terms of clustering quality when using common unsupervised learning quality indexes, but also when compared to a ground-truth based on expert maps.
Keywords :
Markov processes; image resolution; image segmentation; pattern clustering; MRF-based segmentation algorithm; Markov random field; VHR satellite image segmentation; clustering quality; expert map; geographic position property; iterated conditional mode; semantic information extraction; semantic rich ICM algorithm; unsupervised learning quality index; very high resolution image; Clustering algorithms; Data mining; Image resolution; Image segmentation; Indexes; Satellites; Semantics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Vision Applications (MVA), 2015 14th IAPR International Conference on
Conference_Location :
Tokyo
Type :
conf
DOI :
10.1109/MVA.2015.7153129
Filename :
7153129
Link To Document :
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