Semi Supervised Machine Learning and Deep Learning Based Analysis for Hyperspectral Remote Sensing Images

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dc.contributor.advisorPrasad, Saurabh
dc.contributor.committeeMemberMayerich, David
dc.contributor.committeeMemberLabate, Demetrio
dc.contributor.committeeMemberHebert, Thomas J.
dc.contributor.committeeMemberPan, Miao
dc.contributor.committeeMemberLunga, Dalton D.
dc.creatorMukherjee, Souvick
dc.creator.orcid0000-0003-0748-8371
dc.date.accessioned2019-11-08T03:30:02Z
dc.date.createdAugust 2019
dc.date.issued2019-08
dc.date.submittedAugust 2019
dc.date.updated2019-11-08T03:30:03Z
dc.description.abstractHyperspectral Image Analysis has been an active area of research, especially in scenarios where discriminative features from classes having similar spectral characteristics have to be learned. We propose and implement novel machine learning techniques to address research problems in the field of Hyperspectral Image Analysis using remote sensing images. Each chapter in this dissertation presents a novel method from the field of machine learning with the end goal of robust classification of Hyperspectral Remote Sensing Images. We describe common problems faced in the field of Hyperspectral Image Analysis, and address those problems by proposing novel techniques. One common problem is the lack of large quantities of labeled data, which leads to the problem of models over fitting to the limited number of labeled training samples. We propose a spatial-spectral unsupervised feature extraction / reduction approach in Chapter 2 of this dissertation. Another approach to address the specific problem of the lack of large quantities of labeled data samples is to use the large number of available unlabeled data samples to perform Semi-Supervised learning. Towards this goal, we propose a Semi-Supervised feature extraction / dimension reduction approach in Chapter 3 of this dissertation. Following the same idea, and inspired by the recent advancements in the field of Deep Learning, we also propose a Semi-Supervised Deep Learning approach in Chapter 4 of this dissertation. Another recent development in the field of Deep Learning for color image analysis involves new variants of neural network architectures called Capsule Neural Networks, which can capture the spatial information along with the underlying context from the original images in a much more robust manner. We propose Semi-Supervised Capsule Neural Networks tailored towards hyperspectral image analysis in Chapter 5 of this dissertation. In the penultimate Chapter of this dissertation, Chapter 6, we propose an algorithm to perform label expansion for Semi-Supervised Deep Learning tasks, applied to the domain of large scale Road Segmentation of big cities (we show our results for Road Segmentation in the city of Las Vegas and Caracas, the capital of Venezuela).
dc.description.departmentElectrical and Computer Engineering, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.citationPortions of this document appear in: Mukherjee, Souvick, Minshan Cui, and Saurabh Prasad. "Spatially Constrained Semisupervised Local Angular Discriminant Analysis for Hyperspectral Images." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, no. 4 (2017): 1203-1212.
dc.identifier.urihttps://hdl.handle.net/10657/5345
dc.language.isoeng
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.subjectSemi-supervised learning
dc.subjectUnsupervised learning
dc.titleSemi Supervised Machine Learning and Deep Learning Based Analysis for Hyperspectral Remote Sensing Images
dc.type.dcmiText
dc.type.genreThesis
local.embargo.lift2021-08-01
local.embargo.terms2021-08-01
thesis.degree.collegeCullen College of Engineering
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.disciplineElectrical Engineering
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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