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dc.contributor.advisorKruse, Fred A.
dc.contributor.authorCone, Shelli R.
dc.dateSep-14
dc.date.accessioned2014-12-05T20:10:07Z
dc.date.available2014-12-05T20:10:07Z
dc.date.issued2014-09
dc.identifier.urihttp://hdl.handle.net/10945/43893
dc.description.abstractVisible to near-, shortwave-, and longwave-infrared (VNIR, SWIR, LWIR) remote sensing data are typically analyzed in their individual wavelength regions, even though theory suggests combined use would emphasize complementary features. This research explored the potential for improvements in material classification using integrated datasets. Hyperspectral (HSI) VNIR and SWIR data from the MaRSuper Sensor System (MSS-1) were analyzed with HSI LWIR data from the Spatially Enhanced Broadband Array Spectrograph System (SEBASS) to determine differences between individual (baseline) and combined analyses. The first integration approach applied separate minimum noise fraction (MNF) transforms to the three regions and combined only non-noise transformed bands from the individual regions during analysis. The second approach integrated over 470 hyperspectral bands covering the VNIR, SWIR, and LWIR wavelengths before using MNF analysis to isolate linear band combinations containing high signal to noise. Spectral endmembers isolated from data were unmixed using partial unmixing. The feasible and high abundance pixels were spatially mapped using a consistent feasibility ratio threshold. Both integration methods enabled straight-forward and effective identification, characterization, and mapping of the scene because higher variability existed between endmembers and background. Results were compared to the baseline analysis. Material identification was more conclusive when analyzing across the full spectrum.en_US
dc.description.urihttp://archive.org/details/explorationofint1094543893
dc.publisherMonterey, California: Naval Postgraduate Schoolen_US
dc.rightsCopyright is reserved by the copyright owner.en_US
dc.titleExploration of integrated visible to near-, shortwave-, and longwave-infrared (full-range) spectral analysisen_US
dc.typeThesisen_US
dc.contributor.secondreaderMcDowell, Meryl L.
dc.contributor.departmentInformation Sciences (IS)
dc.subject.authorHyperspectral Imagingen_US
dc.subject.authorFull-Range Spectral Analysisen_US
dc.subject.authorVisible to NearInfrareden_US
dc.subject.authorShortwave Infrareden_US
dc.subject.authorMSS-1en_US
dc.subject.authorLongwave Infrareden_US
dc.subject.authorSEABASSen_US
dc.description.recognitionOutstanding Thesisen_US
dc.description.serviceContractor, Scitor Corporationen_US
etd.thesisdegree.nameMaster of Science in Remote Sensing intelligenceen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineRemote Sensing Intelligenceen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.


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