Partially supervised classification – based on weighted unlabeled samples support vector machine

Authors:
Zhigang Liu;Wenzhong Shi;Deren Li;Qianqing Qin
Affiliations:
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing Applications, CAS;Advanced Research Centre for Spatial Information Technology, Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong;State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University;State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University
Venue:
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Year:
2005

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Abstract

This paper addresses a new classification technique: partially supervised classification (PSC), which is used to identify a specific land-cover class of interest from a remotely sensed image by using unique training samples belong to a specifically selected class. This paper also presents and discusses a novel Support Vector Machine (SVM) algorithm for PSC. Its training set includes labeled samples belong to the class of interest and unlabeled samples of all classes randomly selected from a remotely sensed image. Moreover, all unlabeled samples are assumed to be training samples of other classes and each of them is assigned a weighting factor indicating the likelihood of this assumption; hence, the algorithm is so-called ‘Weighted Unlabeled Sample SVM' (WUS-SVM). Experimental results with both simulated and real data sets indicate that the proposed PSC method is more robust than 1-SVM and has comparable accuracy to a standard SVM.