Comparisom of wavelet-based and HHT-based feature extraction methods for hyperspectral image classification
Journal
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Journal Volume
39
Pages
121-126
Date Issued
2012
Author(s)
Huang, X.M.
Abstract
Hyperspectral images, which contain rich and fine spectral information, can be used to identify surface objects and improve land use/cover classification accuracy. Due to the property of high dimensionality of hyperspectral data, 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 feature extraction is used to reduce the dimensionality of hyperspectral images more frequently. There are two types of feature extraction methods. The first type is based on statistical property of data. The other type is based on time-frequency analysis. In this study, the time-frequency analysis methods are used to extract the features for hyperspectral image classification. Firstly, it has been proven that wavelet-based feature extraction provide an effective tool for spectral feature extraction. On the other hand, Hilbert-Huang transform (HHT), a relative new time-frequency analysis tool, has been widely used in nonlinear and nonstationary data analysis. In this study, wavelet transform and HHT are implemented on the hyperspectral data for physical spectral analysis. Therefore, we can get a small number of salient features, reduce the dimensionality of hyperspectral images and keep the accuracy of classification results. An AVIRIS data set is used to test the performance of the proposed HHT-based feature extraction methods; then, the results are compared with wavelet-based feature extraction. According to the experiment results, HHT-based feature extraction methods are effective tools and the results are similar with wavelet-based feature extraction methods.
Subjects
Classification; Feature Extraction; Hilbert-Huang Transform; Hyperspectral Image; Wavelet Transform
SDGs
Other Subjects
Classification (of information); Extraction; Feature extraction; Image processing; Independent component analysis; Land use; Mathematical transformations; Photogrammetry; Remote sensing; Spectroscopy; Spectrum analysis; Statistical tests; Wavelet analysis; Wavelet transforms; Accuracy of classifications; Dimensionality reduction; Feature extraction methods; Hilbert Huang transforms; Hyper-spectral images; Hyperspectral image classification; Spectral feature extraction; Time-frequency analysis methods; Image classification
Publisher
journal article
Type
journal article