Partially Supervised Classification: Based on Weighted Unlabeled Samples Support Vector Machine

Zhigang Liu, Wenzhong Shi, Deren Li and Qianqing Qin
Additional contact information
Zhigang Liu: State Key Laboratory of Remote Sensing Science, China
Wenzhong Shi: Beijing Normal University, China
Deren Li: Wuhan University, China
Qianqing Qin: The Hong Kong Polytechnic University, Hong Kong

International Journal of Data Warehousing and Mining (IJDWM), 2006, vol. 2, issue 3, 42-56

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 using unique training samples that belongs to a specified class. This paper also presents and discusses a newly proposed novel Support Vector Machine (SVM) algorithm for PSC. Accordingly, its training set includes labeled samples that 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 weight factor indicating the likelihood of this assumption; hence, the algorithm is called ‘Weighted Unlabeled Sample SVM’ (WUS-SVM). Based on the WUS-SVM, a PSC method is proposed. Experimental results with both simulated and real datasets indicate that the proposed PSC method can achieve encouraging accuracy and is more robust than the 1-SVM and the Spectral Angle Mapping (SAM) method.

Date: 2006
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 4018/jdwm.2006070104 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jdwm00:v:2:y:2006:i:3:p:42-56

Access Statistics for this article

International Journal of Data Warehousing and Mining (IJDWM) is currently edited by Eric Pardede

More articles in International Journal of Data Warehousing and Mining (IJDWM) from IGI Global
Bibliographic data for series maintained by Journal Editor ().