NDLI: Background Density Nonparametric Estimation With Data-Adaptive Bandwidths for the Detection of Anomalies in Multi-Hyperspectral Imagery

Content Provider	IEEE Xplore Digital Library
Author	Matteoli, S. Veracini, T. Diani, M. Corsini, G.
Copyright Year	2004
Abstract	This letter presents a scheme for detecting global anomalies, in which a likelihood ratio test based decision rule is applied in conjunction with an automated data-driven estimation of the background probability density function (PDF). The latter is reliably estimated with a nonparametric variable-band width kernel density estimator (VKDE), without making any distributional assumption. With respect to conventional fixed bandwidth KDE (FKDE), which lacks adaptivity due to the use of a bandwidth that is fixed across the entire feature space, VKDE lets the bandwidths adaptively vary pixel by pixel, tailoring the amount of smoothing to the local data density. Two multispectral images are employed to explore the potential of VKDE background PDF estimation for detecting anomalies in a scene with respect to conventional nonadaptive FKDE.
Sponsorship	IEEE Geoscience and Remote Sensing Society
Starting Page	163
Ending Page	167
Page Count	5
File Size	423841
File Format	PDF
ISSN	1545598X
Volume Number	11
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	variable bandwidth kernel density estimation Anomaly detection multi-hyperspectral images
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Geotechnical Engineering and Engineering Geology

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