Determining the intrinsic dimension of a hyperspectral image is an important step in the
spectral unmixing process, since under- or over- estimation of this number may lead to
incorrect unmixing for unsupervised methods. In this thesis we introduce a new method
for determining the intrinsic dimension, using recent advances in Random Matrix Theory
(RMT). This method is not sensitive to non-i.i.d. and correlated noise, and it is entirely
unsupervised and free from any user-determined parameters. The new RMT method is
mathematically derived, and robustness tests are run on synthetic data to determine how
the results are a ected by: image size; noise levels; noise variability; noise approximation;
spectral characteristics of the endmembers, etc. Success rates are determined for many
di erent synthetic images, and the method is compared to two principal state of the
art methods, Noise Subspace Projection (NSP) and HySime. All three methods are
then tested on twelve real hyperspectral images, including images acquired by satellite,
airborne and land-based sensors. When images that were acquired by di erent sensors
over the same spatial area are evaluated, RMT gives consistent results, showing the
robustness of this method to sensor characterisics.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/12375 |
| Date | 04 February 2013 |
| Creators | Cawse-Nicholson, Kerry |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
Page generated in 0.0033 seconds