Unbiased query-by-bagging active learning for VHR image classification

Loris Copa, Devis Tuia*, Michele Volpi, Mikhail Kanevski

*Corresponding author for this work

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

22 Citations (Scopus)

Abstract

A key factor for the success of supervised remote sensing image classification is the definition of an efficient training set. Suboptimality in the selection of the training samples can bring to low classification performance. Active learning algorithms aim at building the training set in a smart and efficient way, by finding the most relevant samples for model improvement and thus iteratively improving the classification performance. In uncertaintybased approaches, a user-defined heuristic ranks the unlabeled samples according to the classifier's uncertainty about their class membership. Finally, the user is asked to define the labels of the pixels scoring maximum uncertainty. In the present work, an unbiased uncertainty scoring function encouraging sampling diversity is investigated. A modified version of the Entropy Query by Bagging (EQB) approach is presented and tested on very high resolution imagery using both SVM and LDA classifiers. Advantages of favoring diversity in the heuristics are discussed. By the diverse sampling it enhances, the unbiased approach proposed leads to higher convergence rates in the first iterations for both the models considered.

Original languageEnglish
Title of host publicationImage and Signal Processing for Remote Sensing XVI
DOIs
Publication statusPublished - 22 Oct 2010
Externally publishedYes
EventImage and Signal Processing for Remote Sensing XVI - Toulouse, France
Duration: 20 Sep 201022 Sep 2010

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume7830
ISSN (Print)0277-786X

Conference

ConferenceImage and Signal Processing for Remote Sensing XVI
CountryFrance
CityToulouse
Period20/09/1022/09/10

Keywords

  • Active learning
  • Committees of learners
  • Diverse sampling
  • Remote Sensing
  • Very high resolution

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