Prasad, Saurabh2018-12-062018-12-06December 22015-12December 2Portions of this document appear in: Cui, Minshan, and Saurabh Prasad. "Angular discriminant analysis for hyperspectral image classification." IEEE Journal of Selected Topics in Signal Processing 9, no. 6 (2015): 1003-1015. And in: Cui, Minshan, and Saurabh Prasad. "Class-dependent sparse representation classifier for robust hyperspectral image classification." IEEE Transactions on Geoscience and Remote Sensing 53, no. 5 (2015): 2683-2695. And in: Cui, Minshan, and Saurabh Prasad. "Sparsity promoting dimensionality reduction for classification of high dimensional hyperspectral images." In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, pp. 2154-2158. IEEE, 2013.http://hdl.handle.net/10657/3674Remote sensing involves measuring and analyzing objects of interests through data collected by a remote imaging modality without physical contact with the objects. Hyperspectral sensors have become increasingly popular for a variety of remote sensing applications. Hyperspectral data are composed of densely sampled reflectance values over a wide range of the electromagnetic spectrum. Such a wealth of spectral information can provide unique spectral signatures of different materials present in a scene, which makes it especially suitable for classification tasks. In this dissertation, we present new dimensionality reduction (feature extraction) and classification algorithms for high-dimensional hyperspectral data. Specifically, we develop the theory and validate a new dimensionality reduction approach that maximizes angular separation in the lower dimensional subspace. We also propose and develop its ``local'' and ``nonlinear kernel'' variants for robust feature extraction of hyperspectral data. By preserving angular properties, the resulting subspaces demonstrate robustness to a variety of sources of variability that are commonly encountered in remote sensing applications. We also extend this approach to its ``spatial variant'' by incorporating spatial-contextual information along with spectral information from the hyperspectral images. We also optimize and develop a suitable sparse representation based classification framework for hyperspectral images. By extensive experiments on several real-world hyperspectral datasets, we demonstrate that the proposed algorithms significantly outperform the state-of-the-art methods. Further, we also demonstrate the applicability of the proposed methods for a practical environmental remote sensing 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).Remote sensingMachine learning2018-12-06Thesisborn digital