Skip to Main content Skip to Navigation
Conference papers

Semantic rich ICM algorithm for VHR satellite images segmentation

Jérémie Sublime 1 Andrès Troya-Galvis 2 Younès Bennani 3 Antoine Cornuéjols 1 Pierre Gancarski 2
1 MIA-Paris - Mathématiques et Informatique Appliquées
2 ICube - Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie
3 LIPN - Laboratoire d'Informatique de Paris-Nord
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.
Document type :
Conference papers
Complete list of metadata
https://hal-agroparistech.archives-ouvertes.fr/hal-01585544
Contributor : Eva Legras Connect in order to contact the contributor
Submitted on : Monday, September 11, 2017 - 4:08:35 PM
Last modification on : Tuesday, June 15, 2021 - 2:57:02 PM

Identifiers

Collections

AGROPARISTECH | MIA-PARIS | CNRS | INRA | LIPN | UNIV-PARIS13 | USPC | INC-CNRS | SITE-ALSACE | INSA-STRASBOURG | INSA-GROUPE | GALILE | SORBONNE-PARIS-NORD | INRAE | GS-COMPUTER-SCIENCE

Citation

Jérémie Sublime, Andrès Troya-Galvis, Younès Bennani, Antoine Cornuéjols, Pierre Gancarski. Semantic rich ICM algorithm for VHR satellite images segmentation. 14th IAPR International Conference on Machine Vision Applications (MVA), May 2015, Tokyo, Japan. pp.15292961, ⟨10.1109/MVA.2015.7153129⟩. ⟨hal-01585544⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

441