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Proceedings Paper

Active versus semi-supervised learning paradigm for the classification of remote sensing images
Author(s): Claudio Persello; Lorenzo Bruzzone
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Paper 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.

Paper Details

Date Published: 27 October 2011
PDF: 15 pages
Proc. SPIE 8180, Image and Signal Processing for Remote Sensing XVII, 81800C (27 October 2011); doi: 10.1117/12.898483
Show Author Affiliations
Claudio Persello, Univ. of Trento (Italy)
Lorenzo Bruzzone, Univ. of Trento (Italy)


Published in SPIE Proceedings Vol. 8180:
Image and Signal Processing for Remote Sensing XVII
Lorenzo Bruzzone, Editor(s)

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