Spatial-Spectral Approaches to Edge Detection in Hyperspectral Remote Sensing
Date
2017
Authors
Cox, Cary M.
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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.
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Keywords
Remote sensing, Geography, Edge detection, Gradient, Hyperspectral, HySPADE, Level set, Spatial