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dc.contributor.advisorZare, Alinaeng
dc.contributor.authorDu, Xiaoxiaoeng
dc.date.issued2013eng
dc.date.submitted2013 Falleng
dc.description"December 2013."eng
dc.description"A Thesis presented to the Faculty of the Graduate School at the University of Missouri--Columbia In Partial Fulfillment of the Requirements for the Degree Master of Science."eng
dc.descriptionThesis supervisor: Dr. Alina Zare.eng
dc.descriptionIncludes vita.eng
dc.description.abstractHyperspectral imaging is widely used in the field of remote sensing (Goetz, et al., 1985; Green, et al., 1998). In a hyperspectral imaging system, sensors collect radiance/reflectance values over an area (or a scene) across hundreds of spectral bands (Goetz, et al., 1985). The hyperspectral image yielded by such system can be represented by a three-dimensional data cube containing those radiance/reflectance values in a range of wavelengths (Landgrebe, 2002). There are two common analysis methods for hyperspectral imagery (Hu, et al., 1999): endmember estimation and hyperspectral unmixing. Endmember estimation aims at finding pure individual spectral signatures of the materials (endmembers) in the scene (Adams, et al., 1986). Hyperspectral unmixing, on the other hand, estimates the proportions of each endmember at every pixel of the image. Each pixel in the image can then be represented by endmember spectra weighted by its corresponding proportions. In order to increase the accuracy of hyperspectral unmixing, sufficient temporal and spatial spectral variability of endmembers must be taken into consideration (Roberts, et al., 1992; Roberts, et al., 1998; Bateson, et al., 2000). The most common factors contributing to spectral variability include environmental factors, such as atmospheric effects, illumination, moisture conditions, and inherent spectral variability of the material itself, such as the variations in biophysical and biochemical composition in vegetation (Song, 2005). Under such influence, the spectral signature of endmembers may vary from time to time and from pixel to pixel in the scene. In order to account for such endmember spectral variability, endmembers are regarded as either a set, or a "bundle", of individual spectra (Roberts, et al., 1998; Bateson, et al., 2000), or as a sample from a full distribution. The application of the Normal Compositional Model with Gaussian-distributed endmembers has been discussed in the literature (Eches, et al., 2010; Zare, et al., 2012). Since the domain of Gausseng
dc.description.bibrefIncludes bibliographical references (pages 110-120).eng
dc.format.extent1 online resource (xiv, 121 pages) : color illustrationseng
dc.identifier.oclc898581743eng
dc.identifier.urihttps://hdl.handle.net/10355/43049
dc.identifier.urihttps://doi.org/10.32469/10355/43049eng
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcommunityUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.sourceSubmitted by the University of Missouri--Columbia Graduate Schooleng
dc.subject.lcshSpectral imaging -- Measurementeng
dc.subject.lcshRemote sensingeng
dc.titleAccounting for spectral variability in hyperspectral unmixing using beta endmember distributionseng
dc.typeThesiseng
thesis.degree.disciplineElectrical and computer engineering (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelMasterseng
thesis.degree.nameM.S.eng


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