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ORCID
N/A
Access Type
Open Access Thesis
Document Type
thesis
Degree Program
Electrical & Computer Engineering
Degree Type
Master of Science in Electrical and Computer Engineering (M.S.E.C.E.)
Year Degree Awarded
2015
Month Degree Awarded
February
Abstract
Hyperspectral signature classification is a kind of quantitative analysis approach for hyperspectral imagery which performs detection and classification of the constituent materials at pixel level in the scene. The classification procedure can be operated directly on hyperspectral data or performed by using some features extracted from corresponding hyperspectral signatures containing information like signature energy or shape. In this paper, we describe a technique that applies non-homogeneous hidden Markov chain (NHMC) models to hyperspectral signature classification. The basic idea is to use statistical models (NHMC models) to characterize wavelet coefficients which capture the spectrum structural information at multiple levels. Experimental results show that the approach based on NHMC models outperforms existing approaches relevant in classification tasks.
DOI
https://doi.org/10.7275/6455188
First Advisor
Marco F Duarte
Second Advisor
Mario Parente
Third Advisor
Patrick A Kelly
Recommended Citation
Feng, Siwei, "Wavelet-Based Non-Homogeneous Hidden Markov Chain Model For Hyperspectral Signature Classification" (2015). Masters Theses. 145.
https://doi.org/10.7275/6455188
https://scholarworks.umass.edu/masters_theses_2/145