Active versus semi-supervised learning paradigm for the classification of remote sensing images

Claudio Persello, Lorenzo Bruzzone

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

Abstract

This paper presents a comparative study in order to analyze active learning (AL) and semi-supervised learning (SSL) for the classification of remote sensing (RS) images. The two learning paradigms are analyzed both from the theoretical and experimental point of view. The aim of this work is to identify the advantages and disadvantages of AL and SSL methods, and to point out the boundary conditions on the applicability of these methods with respect to both the number of available labeled samples and the reliability of classification results. In our experimental analysis, AL and SSL techniques have been applied to the classification of both synthetic and real RS data, defining different classification problems starting from different initial training sets and considering different distributions of the classes. This analysis allowed us to derive important conclusion about the use of these classification approaches and to obtain insight about which one of the two approaches is more appropriate according to the specific classification problem, the available initial training set and the available budget for the acquisition of new labeled samples.
Original languageEnglish
Title of host publicationImage and signal processing for remote sensing XVII, 19-21 September 2011, Prague, Czech Republic
EditorsL. Bruzzone
Place of PublicationBellingham, USA
PublisherSPIE
Number of pages15
ISBN (Print)978-0-819-4880-77
DOIs
Publication statusPublished - 2011
Externally publishedYes
EventImage and Signal Processing for Remote Sensing XVII 2011 - Prague, Czech Republic
Duration: 19 Sept 201122 Sept 2011

Publication series

NameProceedings of SPIE
PublisherSPIE
Volume8180

Conference

ConferenceImage and Signal Processing for Remote Sensing XVII 2011
Country/TerritoryCzech Republic
CityPrague
Period19/09/1122/09/11

Keywords

  • ADLIB-ART-4712
  • n/a OA procedure

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