A comparative analysis of classic Geometrical methods and sparse regression methods for linearly unmixing hyperspectral image data

Simple item page
dc.contributor.authorNicolae, Aurel
dc.date.accessioned2020-09-08T06:47:11Z
dc.date.available2020-09-08T06:47:11Z
dc.date.issued2019
dc.descriptionA research report submitted to the Faculty of Science, University of Witwatersrand, Johannesburg, in the ful lment of the requirements for the degree of Masters of Science by Coursework and Research Report, 2019en_ZA
dc.description.abstractThis research report presents an across-the-board comparative analysis on algorithms for linearly unmixing hyperspectral image data cubes. Convex geometry based endmember extraction algorithms (EEAs) such as the pixel purity index (PPI) algorithm and N-FINDR have been commonly used to derive the material spectral signatures called endmembers from the hyperspectral images. The estimation of their corresponding fractional abundances is done by solving the related inverse problem in a least squares sense. Semi-supervised sparse regression algorithms such as orthogonal matching pursuit (OMP) and sparse unmixing algorithm via variable splitting and augmented Lagrangian (SUnSAL) bypass the endmember extraction process by employing widely available spectral libraries a priori, automatically returning the fractional abundances and sparsity estimates. The main contribution of this work is to serve as a rich resource on hyperspectral image unmixing, providing end-to-end evaluation of a wide variety of algorithms using di erent arti cial data sets.en_ZA
dc.description.librarianXN2020en_ZA
dc.facultyFaculty of Scienceen_ZA
dc.identifier.urihttps://hdl.handle.net/10539/29542
dc.language.isoenen_ZA
dc.schoolSchool Computer Science and Applied Mathematicsen_ZA
dc.titleA comparative analysis of classic Geometrical methods and sparse regression methods for linearly unmixing hyperspectral image dataen_ZA
dc.typeDissertationen_ZA
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
A Nicolae 9703007H - RR 20190728.pdf
Size:
12.28 MB
Format:
Adobe Portable Document Format
Description:
Main work
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Electronic Theses and Dissertations (Masters)