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Supervised Semantic Classification for Nuclear Proliferation Monitoring...

by Ranga R Vatsavai, Anil M Cheriyadat, Shaun S Gleason
Publication Type
Conference Paper
Book Title
39th IEEE Applied Imagery Pattern Recognition Workshop (AIPR)
Publication Date
Page Numbers
1 to 10
Publisher Location
New Jersey, United States of America
Conference Name
39th IEEE Applied Imagery Pattern Recognition (AIPR) Workshop
Conference Location
Washington DC, District of Columbia, United States of America
Conference Sponsor
Conference Date

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.