NDLI: Automated hyperspectral imagery analysis via support vector machines based multi-classifier system with non-uniform random feature selection

Content Provider	IEEE Xplore Digital Library
Author	Samiappan, S. Prasad, S. Bruce, L.M.
Copyright Year	2011
Description	Author affiliation: Mississippi State University, USA (Samiappan, S.; Prasad, S.; Bruce, L.M.)
Abstract	Ground cover classification using remotely sensed hyperspectral data is a challenging pattern recognition problem. The small (and expensive to collect) training sample sizes exacerbate the curse-of-dimensionality problem that already exists with such high dimensional feature spaces. However, Support Vector Machine (SVM) classifiers have been demonstrated to be better at handling such situations compared to other statistical classifiers. Recently, multi-classifier systems and a uniform random feature selection have proved to be very effective for hyperspectral image classification. In this paper, a support vector machines based multi-classifier system with non-uniform (spectrally-constrained) random feature selection is presented. We propose two approaches to perform such a non-uniform random-feature selection. Experimental results with the AVIRIS Indian Pines hyperspectral data demonstrate that the proposed approach outperforms regular random feature selection based on a uniform distribution.
Starting Page	3915
Ending Page	3918
File Size	211013
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6050087
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral imaging Support vector machines Accuracy Manuals Classification algorithms Multi-Classifier Hyperspectral Imaging Support Vector Machines Feature Selection
Content Type	Text
Resource Type	Article

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