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Semi-supervised Remote Sensing Image Classification via Maximum Entropy

2010

Conference Paper

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Remote sensing image segmentation requires multi-category classification typically with limited number of labeled training samples. While semi-supervised learning (SSL) has emerged as a sub-field of machine learning to tackle the scarcity of labeled samples, most SSL algorithms to date have had trade-offs in terms of scalability and/or applicability to multi-categorical data. In this paper, we evaluate semi-supervised logistic regression (SLR), a recent information theoretic semi-supervised algorithm, for remote sensing image classification problems. SLR is a probabilistic discriminative classifier and a specific instance of the generalized maximum entropy framework with a convex loss function. Moreover, the method is inherently multi-class and easy to implement. These characteristics make SLR a strong alternative to the widely used semi-supervised variants of SVM for the segmentation of remote sensing images. We demonstrate the competitiveness of SLR in multispectral, hyperspectral and radar image classifica tion.

Author(s): Erkan, AN. and Camps-Valls, G. and Altun, Y.
Journal: Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010)
Pages: 313-318
Year: 2010
Month: September
Day: 0
Publisher: IEEE

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

DOI: 10.1109/MLSP.2010.5589199
Event Name: 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010)
Event Place: Kittilä, Finland

Address: Piscataway, NJ, USA
Digital: 0
Institution: Institute of Electrical and Electronics Engineers
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{6619,
  title = {Semi-supervised Remote Sensing Image Classification via Maximum Entropy},
  author = {Erkan, AN. and Camps-Valls, G. and Altun, Y.},
  journal = {Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010)},
  pages = {313-318},
  publisher = {IEEE},
  organization = {Max-Planck-Gesellschaft},
  institution = {Institute of Electrical and Electronics Engineers},
  school = {Biologische Kybernetik},
  address = {Piscataway, NJ, USA},
  month = sep,
  year = {2010},
  month_numeric = {9}
}