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dc.contributor.advisorPrasad, Saurabh
dc.creatorPriya, Tanu
dc.date.accessioned2018-12-05T17:13:28Z
dc.date.available2018-12-05T17:13:28Z
dc.date.created2014-12
dc.date.issuedDecember 2014
dc.date.submittedDecember 2014
dc.identifier.citationPortions of this document appear in: Priya, Tanu, Saurabh Prasad, and Hao Wu. "Superpixels for Spatially Reinforced Bayesian Classification of Hyperspectral Images." IEEE Geosci. Remote Sensing Lett. 12, no. 5 (2015): 1071-1075.
dc.identifier.urihttp://hdl.handle.net/10657/3655
dc.description.abstractWith rapid development of multi-channel optical imaging sensors, hyperpsectral data has become increasingly popular, necessitating development of algorithms for robust image analysis with such data. This thesis contributes methods that efficiently and robustly exploits superpixels for hyperspectral data. We study and quantify the efficacy of state-of-the-art superpixel generation algorithms for a variety of hyperspectral images. In this work, superpixel level analysis is proposed for two different hyperspectral image analysis problems — remote sensing image classification and person re-identification via forward looking hyperspectral imagery. Specifically, for remote sensing images, we propose a framework based on superpixels that provides spatial context for robust classification, and, for ground-based “natural” hyperspectral images, efficacy and utility of superpixels is demonstrated, in a multi-view setup, through a pilot study on a person re-identification task.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectHyperspectral imaging
dc.titleSuperpixels for Hyperspectral Image Analysis
dc.date.updated2018-12-05T17:13:28Z
dc.type.genreThesis
thesis.degree.nameMaster of Science in Electrical Engineering
thesis.degree.levelMasters
thesis.degree.disciplineElectrical Engineering
thesis.degree.grantorUniversity of Houston
thesis.degree.departmentElectrical and Computer Engineering, Department of
dc.contributor.committeeMemberRoysam, Badrinath
dc.contributor.committeeMemberShah, Shishir Kirit
dc.type.dcmiText
dc.format.digitalOriginborn digital
dc.description.departmentElectrical and Computer Engineering, Department of
thesis.degree.collegeCullen College of Engineering


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