NDLI: Semisupervised Classification of Hyperspectral Images by SVMs Optimized in the Primal

Content Provider	IEEE Xplore Digital Library
Author	Mingmin Chi Bruzzone, L.
Copyright Year	1980
Abstract	This paper addresses classification of hyperspectral remote sensing images with kernel-based methods defined in the framework of semisupervised support vector machines (S³VMs). In particular, we analyzed the critical problem of the nonconvexity of the cost function associated with the learning phase of S³VMs by considering different (S³VMs) techniques that solve optimization directly in the primal formulation of the objective function. As the nonconvex cost function can be characterized by many local minima, different optimization techniques may lead to different classification results. Here, we present two implementations, which are based on different rationales and optimization methods. The presented techniques are compared with S³VMs implemented in the dual formulation in the context of classification of real hyperspectral remote sensing images. Experimental results point out the effectiveness of the techniques based on the optimization of the primal formulation, which provided higher accuracy and better generalization ability than the S³VMs optimized in the dual formulation
Sponsorship	IEEE Geoscience and Remote Sensing Society IEEE URSI
Starting Page	1870
Ending Page	1880
Page Count	11
File Size	277019
File Format	PDF
ISSN	01962892
Volume Number	45
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-06-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral imaging Hyperspectral sensors Remote sensing Voice mail Image analysis Covariance matrix Optimization methods Support vector machines Support vector machine classification Cost function support vector machines (SVMs) Hyperspectral images remote sensing semisupervised classification semisupervised learning
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Electrical and Electronic Engineering

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