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Semi-Supervised Learning for Ill-Posed Polarimetric SAR Classification

Department of Signal Processing, Tampere University of Technology, 33720 Tampere, Finland
Author to whom correspondence should be addressed.
Remote Sens. 2014, 6(6), 4801-4830;
Received: 2 December 2013 / Revised: 28 April 2014 / Accepted: 29 April 2014 / Published: 27 May 2014
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
In recent years, the interest in semi-supervised learning has increased, combining supervised and unsupervised learning approaches. This is especially valid for classification applications in remote sensing, while the data acquisition rate in current systems has become fairly large considering high- and very-high resolution data; yet on the other hand, the process of obtaining the ground truth data may be cumbersome for such large repositories. In this paper, we investigate the application of semi-supervised learning approaches and particularly focus on the small sample size problem. To that extend, we consider two basic unsupervised approaches by enlarging the initial labeled training set as well as an ensemble-based self-training method. We propose different strategies within self-training on how to select more reliable candidates from the pool of unlabeled samples to speed-up the learning process and to improve the classification performance of the underlying classifier ensemble. We evaluate the effectiveness of the proposed semi-supervised learning approach over polarimetric SAR data. Results show that the proposed self-training approach using an ensemble-based classifier that is initially trained over a small training set can achieve a similar performance level of a fully supervised learning approach where the training is performed over significantly larger labeled data. Considering the difficulties of the manual data labeling in such massive volumes of SAR repositories, this is indeed a promising accomplishment for semi-supervised SAR classification. View Full-Text
Keywords: semi-supervised; machine learning; ensemble; SAR; superpixel semi-supervised; machine learning; ensemble; SAR; superpixel
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MDPI and ACS Style

Uhlmann, S.; Kiranyaz, S.; Gabbouj, M. Semi-Supervised Learning for Ill-Posed Polarimetric SAR Classification. Remote Sens. 2014, 6, 4801-4830.

AMA Style

Uhlmann S, Kiranyaz S, Gabbouj M. Semi-Supervised Learning for Ill-Posed Polarimetric SAR Classification. Remote Sensing. 2014; 6(6):4801-4830.

Chicago/Turabian Style

Uhlmann, Stefan, Serkan Kiranyaz, and Moncef Gabbouj. 2014. "Semi-Supervised Learning for Ill-Posed Polarimetric SAR Classification" Remote Sensing 6, no. 6: 4801-4830.

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