Date of Award

2019

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

Committee Chair

Vineetha Menon

Committee Member

Ramazan Aygun

Committee Member

Tathagata Mukherjee

Subject(s)

Remote sensing--Data processing., Big data., Machine learning.

Abstract

Hyperspectral remote sensing presents a unique Big Data research paradigm through its rich data collected in the form of hundreds of spectral bands which embody vital spatial and spectral information about the underlying terrains. Typical hyperspectral data analysis methods are often based on spectral information. Although there have been prior efforts in the literature for incorporation of spatial, spectral, contextual and other forms of information to improve the classification performance of hyperspectral data analysis, this additional information extraction and knowledge discovery process come at the expense of increased computation and memory requirements. Therefore, the caveats of large scale data analysis such as increased computation, transmission and memory requirements present a major impediment to efficient automation and classification performance of hyperspectral data analysis methods. Hence, to address the aforementioned challenges this thesis presents several novel deep learning-based hyperspectral data analysis models, which provide an efficient means for automation and extraction of the spatial and spectral information present in hyperspectral data compared to the conventional spatial- or spectral information-only based methods. Experimental results reveal that the proposed hyperspectral data analysis models outperform the conventional spectral-spatial feature extraction techniques.

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