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Statistical models for physically derived target sub-spaces

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dc.contributor.author Ientilucci, Emmett
dc.contributor.author Bajorski, Peter
dc.date.accessioned 2009-04-17T18:13:31Z
dc.date.available 2009-04-17T18:13:31Z
dc.date.issued 2006-07
dc.identifier.citation Imaging Spectrometry XI. Edited by Shen, Sylvia S.; Lewis, Paul E.. Proceedings of the SPIE, Volume 6302, pp. 63020A (2006). en_US
dc.identifier.uri http://hdl.handle.net/1850/9101
dc.description RIT community members may access full-text via RIT Libraries licensed databases: http://library.rit.edu/databases/
dc.description.abstract Traditional approaches to hyperspectral target detection involve the application of detection algorithms to atmo- spherically compensated imagery. Rather than compensate the imagery, a more recent approach uses physical models to generate target sub-spaces. These radiance sub-spaces can then be used in an appropriate detection scheme to identify potential targets. The generation of these sub-spaces involves some a priori knowledge of data acquisition parameters, scene and atmospheric conditions, and possible calibration errors. Variation is allowed in the model since some parameters are di±cult to know accurately. Each vector in the subspace is the result of a MODTRAN simulation coupled with a physical model. Generation of large target spaces can be computationally burdensome. This paper explores the use of statistical methods to describe such target spaces. The statistically modeled spaces can then be used to generate arbitrary radiance vectors to form a sub-space. Statistically modeled target sub-spaces, using limited training samples, were found to accurately resemble MODTRAN derived radiance vectors. en_US
dc.language.iso en_US en_US
dc.publisher Society of Photo-Optical Instrumentation Engineers en_US
dc.relation.ispartofseries DOI: 10.1117/12.679525 en_US
dc.subject Hyperspectral en_US
dc.subject Invariant Subspace en_US
dc.subject Physics Based Modeling en_US
dc.subject Statistical Models en_US
dc.subject Subpixel Target Detection en_US
dc.subject Target Sub-Spaces en_US
dc.title Statistical models for physically derived target sub-spaces en_US
dc.description.college Kate Gleason College of Engineering en_US
dc.description.department Center for Quality and Applied Statistics en_US
dc.identifier.url http://dx.doi.org/10.1117/12.679525

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