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Conference Paper

Semi-supervised Remote Sensing Image Classification via Maximum Entropy

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83905

Erkan,  AN
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83843

Camps-Valls,  G
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83782

Altun,  Y
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Erkan, A., Camps-Valls, G., & Altun, Y. (2010). Semi-supervised Remote Sensing Image Classification via Maximum Entropy. In 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010) (pp. 313-318). Piscataway, NJ, USA: IEEE.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-BE70-5
Abstract
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.