Document Type

Open Access Thesis

Embargo Period

12-11-2014

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

Advisor Name

Marco

Advisor Middle Initial

F

Advisor Last Name

Duarte

Co-advisor Name

Mario

Co-advisor Last Name

Parente

Third Advisor Name

Patrick

Third Advisor Middle Initial

A

Third Advisor Last Name

Kelly

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.

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