Principal components based techniques for hyperspectral image data
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Authors
Fountanas, Leonidas
Subjects
Remote sensing
Hyperspectral imagery
Principal Components Analysis
Minimum Noise Transform
Hyperspectral imagery
Principal Components Analysis
Minimum Noise Transform
Advisors
Olsen, Christopher
Date of Issue
2004-12
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
PC and MNF transforms are two widely used methods that are utilized for various applications such as dimensionality reduction, data compression and noise reduction. In this thesis, an in-depth study of these two methods is conducted in order to estimate their performance in hyperspectral imagery. First the PCA and MNF methods are examined for their effectiveness in image enhancement. Also, the various methods are studied to evaluate their ability to determine the intrinsic dimension of the data. Results indicate that, in most cases, the scree test gives the best measure of the number of retained components, as compared to the cumulative variance, the Kaiser, and the CSD methods. Then, the applicability of PCA and MNF for image restoration are considered using two types of noise, Gaussian and periodic. Hyperspectral images are corrupted by noise using a combination of ENVI and MATLAB software, while the performance metrics used for evaluation of the retrieval algorithms are visual interpretation, rms correlation coefficient spectral comparison, and classification. In Gaussian noise, the retrieved images using inverse transforms indicate that the basic PC and MNF transform perform comparably. In periodic noise, the MNF transform shows less sensitivity to variations in the number of lines and the gain factor.
Type
Thesis
Description
Series/Report No
Department
Identifiers
NPS Report Number
Sponsors
Funder
Format
xvi, 85 p. : col. maps ;
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
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Copyright is reserved by the copyright owner.