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Accession Number ADA586631
Title Online Cluster Analysis Supporting Real Time Anomaly Detection in Hyperspectral Imagery.
Publication Date Jun 2013
Media Count 172p
Personal Author J. E. Waddell
Abstract Ongoing work in anomaly detection in hyperspectral images has shown that cluster analysis performed in appropriate principal component subspaces can enhance the performance of detectors such as the Reed-Xiaoli detector and its derivatives. Numerous operational considerations motivate the development of an online or incremental clustering algorithm, which can perform clustering as pixels of the image are collected in real time rather than waiting until the full image is complete. Such an algorithm is developed by combining key elements of existing clustering algorithms from related domains. The parameters of the algorithm are tuned and performance of the algorithm is assessed using a set of actual hyperspectral images by exploiting key attributes of an appropriate principal component sub-space. A byproduct of the clustering algorithm is a rudimentary anomaly detector which demonstrates the feasibility of cluster based outlier detection in hyperspectral imagery.
Keywords Algorithms
Factor analysis
Hyperspectral imagery
Online systems
Real time

Source Agency Non Paid ADAS
NTIS Subject Category 72B - Algebra, Analysis, Geometry, & Mathematical Logic
72E - Operations Research
99F - Physical & Theoretical Chemistry
Corporate Author Air Force Inst. of Tech., Wright-Patterson AFB, OH. Graduate School of Engineering and Management.
Document Type Technical report
Title Note Graduate research paper.
NTIS Issue Number 1405
Contract Number N/A

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