Deep Learning for Multi-Channel Image Analysis with Applications to Remote Sensing and Biomedicine

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dc.contributor.committeeMemberPrasad, Saurabh
dc.contributor.committeeMemberRoysam, Badrinath
dc.contributor.committeeMemberMayerich, David
dc.contributor.committeeMemberReddy, Rohith K.
dc.contributor.committeeMemberMaric, Dragan
dc.creatorForoozandeh Shahraki, Farideh
dc.creator.orcid0000-0003-2275-5634
dc.date.accessioned2023-01-14T23:50:17Z
dc.date.createdMay 2022
dc.date.issued2022-05-13
dc.date.updated2023-01-14T23:50:18Z
dc.description.abstractDeep neural networks are emerging as a popular choice for multi-channel image analysis – compared with other machine learning approaches, they have been shown to be more effective for a variety of applications in hyperspectral (HSI) and multispectral imaging. We focus on application specific nuances and design choices with respect to deploying such networks for robust analysis of hyperspectral and multispectral images. We provide quantitative and qualitative results with a variety of deep learning architectures in remote sensing, biomedical FTIR and multiplex rat brain images. In this work, not only are traditional deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Convolutional-Recurrent Neural Networks (CRNNs) investigated, we also design and develop Graph based convolutional neural networks (GCNs) with applications to hyperspectral images. To optimize GCNs for HSI data, we proposed a new method of adjacency matrix construction (a semi-supervised adjacency matrix) which leverages class specific and cluster specific properties of the underlying imagery data. Finally, we designed a semi-automatic pipeline that utilizes registration and deep semantic segmentation for aligning (fitting) a brain atlas on multiplex rat brain images.
dc.description.departmentElectrical and Computer Engineering, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.citationPortions of this document appear in: Berisha, S., Shahraki, F. F., Mayerich, D., & Prasad, S. (2020). Deep Learning for Hyperspectral Image Analysis, Part I: Theory and Algorithms. Hyperspectral Image Analysis, 37.) and (Shahraki, F. F., Saadatifard, L., Berisha, S., Lotfollahi, M., Mayerich, D., & Prasad, S. (2020). Deep Learning for Hyperspectral Image Analysis, Part II: Applications to Remote. Hyperspectral Image Analysis: Advances in Machine Learning and Signal Processing, 69; and in: Shahraki, F. F., & Prasad, S. (2018, November). Graph convolutional neural networks for hyperspectral data classification. In 2018 IEEE global conference on signal and information processing (GlobalSIP) (pp. 968-972). IEEE.) and (Shahraki, F. F., & Prasad, S. (2020). Joint Spatial and Graph Convolutional Neural Networks-A Hybrid Model for Spatial-Spectral Geospatial Image Analysis. In IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium (pp. 4003-4006). IEEE.
dc.identifier.urihttps://hdl.handle.net/10657/13325
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.subjectDeep learning
dc.subjectMulti-Channel Image Analysis
dc.subjectRemote sensing
dc.subjectBiomedicine
dc.subjectHyperspectral Imaging
dc.subjectMultiplex Imaging
dc.subjectGraph Convolutional Neural Network
dc.titleDeep Learning for Multi-Channel Image Analysis with Applications to Remote Sensing and Biomedicine
dc.type.dcmiText
dc.type.genreThesis
dcterms.accessRightsThe full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period.
local.embargo.lift2024-05-01
local.embargo.terms2024-05-01
thesis.degree.collegeCullen College of Engineering
thesis.degree.departmentElectrical and Computer Engineering, Department of
thesis.degree.disciplineElectrical Engineering
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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