Collaborative segmentation and classification for remote sensing image analysis

Andrés Troya-Galvis, Pierre Gançarski, Laure Berti-Equille

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this article we present CoSC, a generic framework for collaborative segmentation and classification. The framework is guided by both radiometric homogeneity based criteria and implicit semantic criteria to segment and extract the objects of a given thematic class. We present a proof-of-concept case-study and show that CoSC is able to reach higher confidence for object classification and results in significant improvement of the whole segmentation.

Original languageEnglish
Title of host publication2016 23rd International Conference on Pattern Recognition, ICPR 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages829-834
Number of pages6
ISBN (Electronic)9781509048472
DOIs
Publication statusPublished - 13 Apr 2017
Event23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun, Mexico
Duration: 4 Dec 20168 Dec 2016

Other

Other23rd International Conference on Pattern Recognition, ICPR 2016
CountryMexico
CityCancun
Period4/12/168/12/16

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

Troya-Galvis, A., Gançarski, P., & Berti-Equille, L. (2017). Collaborative segmentation and classification for remote sensing image analysis. In 2016 23rd International Conference on Pattern Recognition, ICPR 2016 (pp. 829-834). [7899738] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPR.2016.7899738