dc.contributor.advisor Zare, Alina eng dc.contributor.author Du, Xiaoxiao eng dc.date.issued 2013 eng dc.date.submitted 2013 Fall eng 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.description Thesis supervisor: Dr. Alina Zare. eng dc.description Includes vita. eng dc.description.abstract Hyperspectral 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 Gauss eng dc.description.bibref Includes bibliographical references (pages 110-120). eng dc.format.extent 1 online resource (xiv, 121 pages) : color illustrations eng dc.identifier.oclc 898581743 eng dc.identifier.uri https://hdl.handle.net/10355/43049 dc.identifier.uri https://doi.org/10.32469/10355/43049 eng dc.language English eng dc.publisher University of Missouri--Columbia eng dc.relation.ispartofcommunity University of Missouri--Columbia. Graduate School. Theses and Dissertations eng dc.source Submitted by the University of Missouri--Columbia Graduate School eng dc.subject.lcsh Spectral imaging -- Measurement eng dc.subject.lcsh Remote sensing eng dc.title Accounting for spectral variability in hyperspectral unmixing using beta endmember distributions eng dc.type Thesis eng thesis.degree.discipline Electrical and computer engineering (MU) eng thesis.degree.grantor University of Missouri--Columbia eng thesis.degree.level Masters eng thesis.degree.name M.S. eng
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