Home WakeSpace Scholarship › Electronic Theses and Dissertations

RANDOM PROJECTION AND SVD METHODS IN HYPERSPECTRAL IMAGING

Electronic Theses and Dissertations

Item Files

Item Details

abstract
Hyperspectral imaging provides researchers with abundant information with which to study the characteristics of objects in a scene. Processing the massive hyperspectral imagery datasets in a way that efficiently provides useful information becomes an important issue. In this thesis, we consider methods which reduce the dimension of hyperspectral data while retaining as much useful information as possible.
subject
Classification
Dimension Reduction
Hyperspectral Imaging
Random Projection
Reconstruction
SVD
contributor
Zhang, Jiani (author)
Erway, Jennifer (committee chair)
Jiang, Miaohua (committee member)
Hu, Xiaofei (committee member)
Pauca, Paul V (committee member)
date
2012-09-05T08:35:17Z (accessioned)
2012-09-05T08:35:17Z (available)
2012 (issued)
degree
Mathematics (discipline)
identifier
http://hdl.handle.net/10339/37432 (uri)
language
en (iso)
publisher
Wake Forest University
title
RANDOM PROJECTION AND SVD METHODS IN HYPERSPECTRAL IMAGING
type
Thesis

Usage Statistics