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Content Provider | IEEE Xplore Digital Library |
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Author | Bruzzone, L. Mingmin Chi Marconcini, M. |
Copyright Year | 2005 |
Description | Author affiliation: Dept. of Inf. & Commun. Technol., Trento Univ., Italy (Bruzzone, L.; Mingmin Chi; Marconcini, M.) |
Abstract | This paper presents transductive support vector machines (TSVMs) for the semisupervised classification of hyperspectral remote sensing images. On the basis of the analysis of TSVMs recently introduced in the machine learning literature and of the properties of hyperspectral classification problems, a specific TSVM algorithm is proposed to alleviate the Hughes phenomenon in a nonparametric and kernel-based classification framework. The extension of the proposed technique to multiclass cases is also discussed. Experimental results obtained on a real hyperspectral image point out that when small-size training data are available, the proposed TSVMs outperform standard inductive support vector machines (ISVMs). |
File Size | 142480 |
File Format | |
ISBN | 0780390504 |
DOI | 10.1109/IGARSS.2005.1526130 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2005-07-29 |
Publisher Place | South Korea |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Support vector machines Support vector machine classification Hyperspectral imaging Hyperspectral sensors Remote sensing Image sensors Parameter estimation Kernel Algorithm design and analysis Machine learning |
Content Type | Text |
Resource Type | Article |
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