Active learning: Any value for classification of remotely sensed data?

Melba M. Crawford*, Devis Tuia, Hsiuhan Lexie Yang

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

119 Citations (Scopus)


Active learning, which has a strong impact on processing data prior to the classification phase, is an active research area within the machine learning community, and is now being extended for remote sensing applications. To be effective, classification must rely on the most informative pixels, while the training set should be as compact as possible. Active learning heuristics provide capability to select unlabeled data that are the 'most informative' and to obtain the respective labels, contributing to both goals. Characteristics of remotely sensed image data provide both challenges and opportunities to exploit the potential advantages of active learning. We present an overview of active learning methods, then review the latest techniques proposed to cope with the problem of interactive sampling of training pixels for classification of remotely sensed data with support vector machines (SVMs). We discuss remote sensing specific approaches dealing with multisource and spatially and time-varying data, and provide examples for high-dimensional hyperspectral imagery.

Original languageEnglish
Article number6425391
Pages (from-to)593-608
Number of pages16
JournalProceedings of the IEEE
Issue number3
Publication statusPublished - 1 Feb 2013
Externally publishedYes


  • Active learning
  • adaptation
  • classification
  • high-resolution multispectral
  • hyperspectral
  • multiview
  • spatial learning
  • support vector machines (SVMs)

Fingerprint Dive into the research topics of 'Active learning: Any value for classification of remotely sensed data?'. Together they form a unique fingerprint.

Cite this