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dc.contributor.authorSchott, Johnen_US
dc.contributor.authorSalvaggio, Carlen_US
dc.contributor.authorBrown, Scotten_US
dc.contributor.authorRose, Roberten_US
dc.date.accessioned2007-07-05T14:07:58Zen_US
dc.date.available2007-07-05T14:07:58Zen_US
dc.date.issued1995-06en_US
dc.identifier.citationTargets and Backgrounds: Characterization and Representation 2469 (1995) 189-196en_US
dc.identifier.issn0277-786Xen_US
dc.identifier.urihttp://hdl.handle.net/1850/4230en_US
dc.descriptionRIT community members may access full-text via RIT Libraries licensed databases: http://library.rit.edu/databases/
dc.description.abstractThe Digital Imaging and Remote Sensing Synthetic Image Generation (DIRSIG) model emphasizes quantitative prediction of the radiance reaching sensors with bandpass values between 0.28 and 20.0 jim. The model embodies a rigorous end-to-end speciral modeling of radiation propagation, absorption and scattering, target temperatures based on meteorological history, extensive directional target-background interactions and detector responsivities.1 This paper describes texture quantification, the spectral-spatial correlation of textures, texture collection and generation methods. Finally, we will describe how DIRSIG generates texture on a pixel by pixel basis and maintains the spectral correlation of targets between bands.en_US
dc.description.sponsorshipn/aen_US
dc.language.isoen_USen_US
dc.publisherThe International Society for Optical Engineering (SPIE)en_US
dc.relation.ispartofseriesvol. 2469en_US
dc.titleIncorporation of texture in multispectral synthetic image generation toolsen_US
dc.typeArticleen_US
dc.subject.keywordRemote sensingen_US
dc.subject.keywordSynthetic image generationen_US
dc.subject.keywordTextureen_US
dc.identifier.urlhttp://dx.doi.org/10.1117/12.210590


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