Remote sensing image segmentation by active queries

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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

39 Citations (Scopus)

Abstract

Active learning deals with developing methods that select examples that may express data characteristics in a compact way. For remote sensing image segmentation, the selected samples are the most informative pixels in the image so that classifiers trained with reduced active datasets become faster and more robust. Strategies for intelligent sampling have been proposed with model-based heuristics aiming at the search of the most informative pixels to optimize models performance. Unlike standard methods that concentrate on model optimization, here we propose a method inspired in the cluster assumption that holds in most of the remote sensing data. Starting from a complete hierarchical description of the data, the proposed strategy aims at sampling and labeling pixels in order to discover the data partitioning that best matches with the users expected classes. Thus, the method combines active supervised and unsupervised clustering with a smart prune-and-label strategy. The proposed method is successfully evaluated in two challenging remote sensing scenarios: hyperspectral and very high spatial resolution (VHR) multispectral images segmentation.

Original languageEnglish
Pages (from-to)2180-2192
Number of pages13
JournalPattern Recognition
Volume45
Issue number6
DOIs
Publication statusPublished - Jun 2012
Externally publishedYes

Keywords

  • Active learning
  • Clustering
  • Hyperspectral imagery
  • Linkage
  • Multiscale image segmentation
  • Multispectral imagery
  • Remote sensing

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