Improving active learning methods using spatial information

Edoardo Pasolli*, Farid Melgani, Devis Tuia, Fabio Pacifici, William J. Emery

*Corresponding author for this work

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

8 Citations (Scopus)

Abstract

Active learning process represents an interesting solution to the problem of training sample collection for the classification of remote sensing images. In this work, we propose a criterion based on the spatial information that can be used in combination with a spectral criterion in order to improve the selection of training samples. Experimental results obtained on a very high resolution image show the effectiveness of regularization in spatial domain and open challenging perspectives for terrain campaigns planning.

Original languageEnglish
Title of host publication2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings
Pages3923-3926
Number of pages4
ISBN (Electronic)9781457710056
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

Keywords

  • Active learning
  • spatial information
  • support vector machines (SVMs)
  • very-high-resolution (VHR) images

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