Large scale semi-supervised image segmentation with active queries

Devis Tuia*, Jordi Muñoz-Marí, Gustavo Camps-Valls

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

A semiautomatic procedure to generate classification maps of remote sensing images is proposed. Starting from a hierarchical unsupervised classification, the algorithm exploits the few available labeled pixels to assign each cluster to the most probable class. For a given amount of labeled pixels, the algorithm returns a classified segmentation map, along with confidence levels of class membership for each pixel. Active learning methods are used to select the most informative samples to increase confidence in the class membership. Experiments on a AVIRIS hyperspectral image confirm the effectiveness of the method, especially when used with active learning query functions and spatial regularization.

Original languageEnglish
Title of host publication2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings
Pages2653-2656
Number of pages4
DOIs
Publication statusPublished - 16 Nov 2011
Externally publishedYes
Event2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Vancouver, BC, Canada
Duration: 24 Jul 201129 Jul 2011

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011
CountryCanada
CityVancouver, BC
Period24/07/1129/07/11

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  • Cite this

    Tuia, D., Muñoz-Marí, J., & Camps-Valls, G. (2011). Large scale semi-supervised image segmentation with active queries. In 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings (pp. 2653-2656). [6049748] (International Geoscience and Remote Sensing Symposium (IGARSS)). https://doi.org/10.1109/IGARSS.2011.6049748