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dc.contributor.advisor Merenyi, Erzsebet
dc.creatorBue, Brian
dc.date.accessioned 2013-09-16T14:51:44Z
dc.date.accessioned 2013-09-16T14:51:58Z
dc.date.available 2013-09-16T14:51:44Z
dc.date.available 2013-09-16T14:51:58Z
dc.date.created 2013-05
dc.date.issued 2013-09-16
dc.date.submitted May 2013
dc.identifier.urihttp://hdl.handle.net/1911/71929
dc.description.abstract Remotely-sensed hyperspectral imagery has become one the most advanced tools for analyzing the processes that shape the Earth and other planets. Effective, rapid analysis of high-volume, high-dimensional hyperspectral image data sets demands efficient, automated techniques to identify signatures of known materials in such imagery. In this thesis, we develop a framework for automatic material identification in hyperspectral imagery using adaptive similarity measures. We frame the material identification problem as a multiclass similarity-based classification problem, where our goal is to predict material labels for unlabeled target spectra based upon their similarities to source spectra with known material labels. As differences in capture conditions affect the spectral representations of materials, we divide the material identification problem into intra-domain (i.e., source and target spectra captured under identical conditions) and inter-domain (i.e., source and target spectra captured under different conditions) settings. The first component of this thesis develops adaptive similarity measures for intra-domain settings that measure the relevance of spectral features to the given classification task using small amounts of labeled data. We propose a technique based on multiclass Linear Discriminant Analysis (LDA) that combines several distinct similarity measures into a single hybrid measure capturing the strengths of each of the individual measures. We also provide a comparative survey of techniques for low-rank Mahalanobis metric learning, and demonstrate that regularized LDA yields competitive results to the state-of-the-art, at substantially lower computational cost. The second component of this thesis shifts the focus to inter-domain settings, and proposes a multiclass domain adaptation framework that reconciles systematic differences between spectra captured under similar, but not identical, conditions. Our framework computes a similarity-based mapping that captures structured, relative relationships between classes shared between source and target domains, allowing us apply a classifier trained using labeled source spectra to classify target spectra. We demonstrate improved domain adaptation accuracy in comparison to recently-proposed multitask learning and manifold alignment techniques in several case studies involving state-of-the-art synthetic and real-world hyperspectral imagery.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subjectHyperspectral
Material identification
Metric learning
Domain adaptation
Similarity measures
Remote sensing
Classification
dc.title Adaptive Similarity Measures for Material Identification in Hyperspectral Imagery
dc.contributor.committeeMember Jermaine, Christopher M.
dc.contributor.committeeMember Subramanian, Devika
dc.contributor.committeeMember Wagstaff, Kiri
dc.date.updated 2013-09-16T14:51:58Z
dc.identifier.slug 123456789/ETD-2013-05-499
dc.type.genre Thesis
dc.type.material Text
thesis.degree.department Electrical and Computer Engineering
thesis.degree.discipline Engineering
thesis.degree.grantor Rice University
thesis.degree.level Doctoral
thesis.degree.name Doctor of Philosophy


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