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Spatial-Spectral Approaches to Edge Detection in Hyperspectral Remote Sensing

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dc.contributor.advisor Stefanidis, AnthonyResmini, Ronald Cox, Cary M.
dc.creator Cox, Cary M. 2018-10-22T01:19:48Z 2018-10-22T01:19:48Z 2017
dc.description.abstract This 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.extent 793 pages
dc.language.iso en
dc.rights Copyright 2017 Cary M. Cox
dc.subject Remote sensing en_US
dc.subject Geography en_US
dc.subject edge detection en_US
dc.subject gradient en_US
dc.subject hyperspectral en_US
dc.subject HySPADE en_US
dc.subject level set en_US
dc.subject spatial en_US
dc.title Spatial-Spectral Approaches to Edge Detection in Hyperspectral Remote Sensing
dc.type Dissertation Ph.D. Earth Systems and Geoinformation Sciences George Mason University

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