Prasad, Saurabh2019-09-142019-09-14May 20172017-05May 2017Portions of this document appear in: Wu, Hao, and Saurabh Prasad. "Dirichlet process based active learning and discovery of unknown classes for hyperspectral image classification." IEEE Transactions on Geoscience and Remote Sensing 54, no. 8 (2016): 4882-4895. And in: Wu, Hao, and Saurabh Prasad. "Convolutional recurrent neural networks forhyperspectral data classification." Remote Sensing 9, no. 3 (2017): 298.https://hdl.handle.net/10657/4620Hyperspectral imaging is a technique which uses hyperspectral sensors to collect spectral information across the electromagnetic spectrum for each pixel in the image of a scene, with the purpose of identifying materials and detecting objects. The recorded hyperspectral data cover a wide range of wavelengths including visible and invisible light. The rich spectral information provides much more precise characteristics of materials, compared to natural color images. With such a wealth of spectral information, hyperspectral images have a variety of applications in remote sensing, such as target detection and land cover classification. In this dissertation, new algorithms and methods for semi-supervised learning and deep learning are proposed for hyperspectral image analysis. Specifically, a new semi-supervised dimensionality reduction algorithm named Semi-supervised Local Fisher Discriminant Analysis (SLFDA) is proposed to find a lower dimensional subspace for the high dimensional hyperspectral data, aiming to perform discriminant analysis on both labeled and unlabeled samples. Another semi-supervised learning system is also proposed that uses active learning to detect and identify unknown classes in a scene. We also present a deep learning method based on convolutional recurrent neural networks (CRNN) for hyperspectral data classification. Furthermore, a novel semi-supervised deep learning method that combines deep learning with semi-supervised learning is proposed for hyperspectral image classification. With extensive experiments on several real-world hyperspectral image datasets, we demonstrate that the proposed methods significantly outperform the state-of-the-art.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).Semi-supervised learningDeep learningDirichlet process mixtureHyperspectral imaging2019-09-14Thesisborn digital