Recent trends in classification of remote sensing data: active and semisupervised machine learning paradigms

Lorenzo Bruzzone, Claudio Persello

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

22 Citations (Scopus)

Abstract

This paper addresses the recent trends in machine learning methods for the automatic classification of remote sensing (RS) images. In particular, we focus on two new paradigms: semisupervised and active learning. These two paradigms allow one to address classification problems in the critical conditions where the available labeled training samples are limited. These operational conditions are very usual in RS problems, due to the high cost and time associated with the collection of labeled samples. Semisupervised and active learning techniques allow one to enrich the initial training set information and to improve classification accuracy by exploiting unlabeled samples or requiring additional labeling phases from the user, respectively. The two aforementioned strategies are theoretically and experimentally analyzed considering SVM-based techniques in order to highlight advantages and disadvantages of both strategies.
Original languageEnglish
Title of host publicationIGARSS 2010
Subtitle of host publicationProceedings of IEEE international Geoscience and Remote Sensing Symposium, 25-30 July 2010, Honolulu, HI, USA
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages3720-3723
ISBN (Electronic)978-1-4244-9566-5, 978-1-4244-9565-8
ISBN (Print)978-1-4244-9564-1
DOIs
Publication statusPublished - 2010
Externally publishedYes

Keywords

  • ADLIB-ART-4665
  • n/a OA procedure

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