NDLI: Kernel-based methods for hyperspectral image classification

Content Provider	IEEE Xplore Digital Library
Author	Camps-Valls, G. Bruzzone, L.
Copyright Year	1980
Abstract	This paper presents the framework of kernel-based methods in the context of hyperspectral image classification, illustrating from a general viewpoint the main characteristics of different kernel-based approaches and analyzing their properties in the hyperspectral domain. In particular, we assess performance of regularized radial basis function neural networks (Reg-RBFNN), standard support vector machines (SVMs), kernel Fisher discriminant (KFD) analysis, and regularized AdaBoost (Reg-AB). The novelty of this work consists in: 1) introducing Reg-RBFNN and Reg-AB for hyperspectral image classification; 2) comparing kernel-based methods by taking into account the peculiarities of hyperspectral images; and 3) clarifying their theoretical relationships. To these purposes, we focus on the accuracy of methods when working in noisy environments, high input dimension, and limited training sets. In addition, some other important issues are discussed, such as the sparsity of the solutions, the computational burden, and the capability of the methods to provide outputs that can be directly interpreted as probabilities.
Sponsorship	IEEE Geoscience and Remote Sensing Society IEEE URSI
Starting Page	1351
Ending Page	1362
Page Count	12
File Size	620800
File Format	PDF
ISSN	01962892
Volume Number	43
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-06-01
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 Image classification Kernel Hyperspectral sensors Radial basis function networks Support vector machines Support vector machine classification Image analysis Robustness Remote sensing support vector machines AdaBoost feature space hyperspectral classification kernel-based methods kernel Fisher discriminant analysis radial basis function neural networks regularization
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Electrical and Electronic Engineering

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