NDLI: A Novel Transductive SVM for Semisupervised Classification of Remote-Sensing Images

Content Provider	IEEE Xplore Digital Library
Author	Bruzzone, L. Mingmin Chi Marconcini, M.
Copyright Year	1980
Abstract	This paper introduces a semisupervised classification method that exploits both labeled and unlabeled samples for addressing ill-posed problems with support vector machines (SVMs). The method is based on recent developments in statistical learning theory concerning transductive inference and in particular transductive SVMs (TSVMs). TSVMs exploit specific iterative algorithms which gradually search a reliable separating hyperplane (in the kernel space) with a transductive process that incorporates both labeled and unlabeled samples in the training phase. Based on an analysis of the properties of the TSVMs presented in the literature, a novel modified TSVM classifier designed for addressing ill-posed remote-sensing problems is proposed. In particular, the proposed technique: 1) is based on a novel transductive procedure that exploits a weighting strategy for unlabeled patterns, based on a time-dependent criterion; 2) is able to mitigate the effects of suboptimal model selection (which is unavoidable in the presence of small-size training sets); and 3) can address multiclass cases. Experimental results confirm the effectiveness of the proposed method on a set of ill-posed remote-sensing classification problems representing different operative conditions
Sponsorship	IEEE Geoscience and Remote Sensing Society IEEE URSI
Starting Page	3363
Ending Page	3373
Page Count	11
File Size	417751
File Format	PDF
ISSN	01962892
Volume Number	44
Issue Number	11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-11-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Support vector machine classification Remote sensing Classification algorithms Communications technology Training data Hyperspectral sensors Statistical learning Iterative algorithms Kernel transductive inference Ill-posed problems labeled and unlabeled patterns machine learning remote sensing semisupervised classification support vector machines (SVMs)
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Electrical and Electronic Engineering

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