Spatial-Spectral Approaches to Edge Detection in Hyperspectral Remote Sensing

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dc.contributor.advisorStefanidis, AnthonyResmini, Ronald
dc.contributor.authorCox, Cary M.
dc.creatorCox, Cary M.
dc.date.accessioned2018-10-22T01:19:48Z
dc.date.available2018-10-22T01:19:48Z
dc.date.issued2017
dc.description.abstractThis dissertation advances geoinformation science at the intersection of hyperspectral remote sensing and edge detection methods. A relatively new phenomenology among its remote sensing peers, hyperspectral imagery (HSI) comprises only about 7% of all remote sensing research – there are five times as many radar-focused peer reviewed journal articles than hyperspectral-focused peer reviewed journal articles. Similarly, edge detection studies comprise only about 8% of image processing research, most of which is dedicated to image processing techniques most closely associated with end results, such as image classification and feature extraction. Given the centrality of edge detection to mapping, that most important of geographic functions, improving the collective understanding of hyperspectral imagery edge detection methods constitutes a research objective aligned to the heart of geoinformation sciences.
dc.format.extent793 pages
dc.identifier.urihttps://hdl.handle.net/1920/11247
dc.language.isoen
dc.rightsCopyright 2017 Cary M. Cox
dc.subjectRemote sensing
dc.subjectGeography
dc.subjectEdge detection
dc.subjectGradient
dc.subjectHyperspectral
dc.subjectHySPADE
dc.subjectLevel set
dc.subjectSpatial
dc.titleSpatial-Spectral Approaches to Edge Detection in Hyperspectral Remote Sensing
dc.typeDissertation
thesis.degree.disciplineEarth Systems and Geoinformation Sciences
thesis.degree.grantorGeorge Mason University
thesis.degree.levelPh.D.

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