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Accession Number DE2013-1015691
Title Supervised Semantic Classification for Nuclear Proliferation Monitoring.
Publication Date 2013
Media Count 10p
Personal Author A. Cheriyadat R. R. Vatsavai S. Gleason
Abstract Existing feature extraction and classification approaches are not suitable for monitoring proliferation activity using high-resolution multi-temporal remote sensing imagery. In this paper we present a supervised semantic labeling framework based on the Latent Dirichlet Allocation method. This framework is used to analyze over 120 images collected under different spatial and temporal settings over the globe representing three major semantic categories: airports, nuclear, and coal power plants. Initial experimental results show a reasonable discrimination of these three categories even though coal and nuclear images share highly common and overlapping objects. This research also identified several research challenges associated with nuclear proliferation monitoring using high resolution remote sensing images.
Keywords Classification
Images
Monitoring
Nuclear proliferation
Pattern recognition
Power plants
Remote sensing
Resolution
Semantics


 
Source Agency Technical Information Center Oak Ridge Tennessee
NTIS Subject Category 74H - Nuclear Warfare
77H - Reactor Engineering & Nuclear Power Plants
Corporate Author Oak Ridge National Lab., TN.
Document Type Technical report
Title Note N/A
NTIS Issue Number 1323
Contract Number DE-AC05-00OR22725

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