Hyperspectral Image Analysis Using Hilbert-Huang Transform
Date Issued
2011
Date
2011
Author(s)
Huang, Xiu-Man
Abstract
Hyperspectral images, which contain rich and fine spectral information, can be used to identify surface objects and improve land use/cover classification accuracy. However, traditional statistics-based classifiers cannot be directly used on such images with limited training samples. This problem is referred as “curse of dimensionality”. The commonly used method to solve this problem is dimensionality reduction, and this can be done by feature extraction for hyperspectral images. In this study, the Hilbert-Huang transform (HHT) will be applied to hyperspectral image analysis. HHT, consisting of empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA), is a relatively new adaptive time-frequency analysis tool and has been used extensively in nonlinear and nonstationary data analysis. In this study, the EMD is implemented on spectral curve for absorption band analysis firstly. The experiment results show that absorption features can be detected on IMF components effectively. The other objective of this study is to apply HHT on the hyperspectral data for physically spectral analysis. The spectral features are then extracted based on the results of physically spectral analysis, so that we can get a small number of salient features, reduce the dimensionality of hyperspectral images and keep the accuracy of classification results. Finally, two AVIRIS data sets are used to test the performance of the proposed HHT-based methods. According to the experiment results, the HHT-based methods are effective for dimensionality reduction and classification.
Subjects
Hyperspectral Image
Absorption Band
Feature Extraction
Classification
SDGs
Type
thesis
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