2023-01-14May 20222022-05-13Portions 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.https://hdl.handle.net/10657/13325Deep 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.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).Deep learningMulti-Channel Image AnalysisRemote sensingBiomedicineHyperspectral ImagingMultiplex ImagingGraph Convolutional Neural Network2023-01-14Thesisborn digital