A comparative analysis of classic Geometrical methods and sparse regression methods for linearly unmixing hyperspectral image data
A 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, 2019 === This research report presents an across-the-board comparative analysis on algorith...
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| Format: | Others |
| Language: | en |
| Published: |
2020
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| Online Access: | https://hdl.handle.net/10539/29542 |
| Summary: | A 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, 2019 === This 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. === XN2020 |
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