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Active Versus Semi-supervised Learning Paradigm for the Classification of Remote Sensing Images


Conference Paper


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.

Author(s): Persello, C. and Bruzzone, L.
Pages: 1-15
Year: 2011
Month: September
Day: 0
Editors: Bruzzone, L.
Publisher: SPIE

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1117/12.898483
Event Name: Image and Signal Processing for Remote Sensing XVII
Event Place: Praha, Czech Republic

Address: Bellingham, WA, USA
Digital: 0

Links: Web


  title = {Active Versus Semi-supervised Learning Paradigm for the Classification of Remote Sensing Images},
  author = {Persello, C. and Bruzzone, L.},
  pages = {1-15},
  editors = {Bruzzone, L.},
  publisher = {SPIE},
  address = {Bellingham, WA, USA},
  month = sep,
  year = {2011},
  month_numeric = {9}