Prasad, Saurabh2018-12-052018-12-05December 22014-12December 2Portions 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.http://hdl.handle.net/10657/3655With 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.application/pdfengThe 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).Hyperspectral imaging2018-12-05Thesisborn digital