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NEW METHODS OF SPECTRAL-DENSITY BASED GRAPH CONSTRUCTION AND THEIR APPLICATION TO HYPERSPECTRAL IMAGE ANALYSIS

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dc.contributor.advisor Qu, John J
dc.contributor.author Stevens, Jeff
dc.creator Stevens, Jeff
dc.date.accessioned 2018-10-22T01:19:53Z
dc.date.available 2018-10-22T01:19:53Z
dc.date.issued 2017
dc.identifier.uri https://hdl.handle.net/1920/11274
dc.description.abstract The past decade has seen the emergence of many hyperspectral image (HSI) analysis algorithms based on graph theory and derived manifold-coordinates. Yet, despite the growing number of algorithms, there has been limited study of the graphs constructed from spectral data themselves. Which graphs are appropriate for various HSI analyses—and why? This research aims to begin addressing these questions as the performance of graph-based techniques is inextricably tied to the graphical model constructed from the spectral data. We begin with a literature review providing a survey of spectral graph construction techniques currently used by the hyperspectral community, starting with simple constructs demonstrating basic concepts and then incrementally adding components to derive more complex approaches. Throughout this development, we discuss algorithm advantages and disadvantages for different types of hyperspectral analysis. A focus is provided on techniques influenced by spectral density through which the concept of community structure arises. Through the use of simulated and real HSI data, we demonstrate density-based edge allocation produces more uniform nearest neighbor lists than non-density based techniques through increasing the number of intracluster edges, facilitating higher k-nearest neighbor (k-NN) classification performance. Imposing the common mutuality constraint to symmetrify adjacency matrices is demonstrated to be beneficial in most circumstances, especially in rural (less cluttered) scenes. Many complex adaptive edge-reweighting techniques are shown to slightly degrade nearest-neighbor list characteristics. Analysis suggests this condition is possibly attributable to the validity of characterizing spectral density by a single variable representing data scale for each pixel. Additionally, it is shown that imposing mutuality hurts the performance of adaptive edge-allocation techniques or any technique that aims to assign a low number of edges (<10) to any pixel. A simple k bias addresses this problem.
dc.format.extent 229 pages
dc.language.iso en
dc.rights Copyright 2017 Jeff Stevens
dc.subject Remote sensing en_US
dc.subject Computer science en_US
dc.subject Concertation of Measure en_US
dc.subject Graph Theory en_US
dc.subject Hubness en_US
dc.subject Hyperspectral Imaging en_US
dc.subject Imaging Spectroscopy en_US
dc.subject Spectral Graph Construction en_US
dc.title NEW METHODS OF SPECTRAL-DENSITY BASED GRAPH CONSTRUCTION AND THEIR APPLICATION TO HYPERSPECTRAL IMAGE ANALYSIS
dc.type Dissertation
thesis.degree.level Ph.D.
thesis.degree.discipline Computational Sciences and Informatics
thesis.degree.grantor George Mason University


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