NDLI: Multiple-Detector Fusion for Anomaly Detection In Multispectral Imagery Based on Maximum Entropy and Nonparametric Estimation

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Multiple-Detector Fusion for Anomaly Detection In Multispectral Imagery Based on Maximum Entropy and Nonparametric Estimation

Content Provider	IEEE Xplore Digital Library
Author	Wei Di Quan Pan Yong-qiang Zhao Lin He
Copyright Year	2006
Description	Author affiliation: Coll. of Autom., Northwestern Polytech Univ., Xi'an (Wei Di; Quan Pan; Yong-qiang Zhao; Lin He)
Abstract	With the development of sensors capable of high spatial and spectral resolution, anomaly detection in multispectral imagery has gained more attention recently. However, using single detector meets great limitation due to that many of the conditions and parameters that govern performance are unknown or poorly characterized in an operational setting. Unlike the conventional fusion approaches simply using logical operators (e.g. AND, OR), which lead to produce highly variable performance results from one case and thus difficult to specify the "best" fusion logic in advance, a multiple-detector fusion (MDF) algorithm is proposed in this paper. Three successive procedures are included as follows: first, we use series anomaly detectors including well-known RX and its varieties to get the pilot detection results; and second, in order to estimate the pdf statistics of each individual detector's output more accurately, a nonparametric method called kernel density estimation (KDE) with bandwidth adjusted adaptively is used. The obtained probabilistic information are then fused using a modeled joint distribution by the principle of maximum entropy. Finally, the MDF approach is applied to real multispectral imagery. Experimental results and theoretical analysis demonstrate the effectiveness of proposed algorithm
File Size	180464
File Format	PDF
ISBN	0780397363
DOI	10.1109/ICOSP.2006.345778
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-11-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multispectral imaging Entropy Detectors Image sensors Sensor phenomena and characterization Spatial resolution Image resolution Logic Statistical distributions Kernel
Content Type	Text
Resource Type	Article

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