COMPARISOM OF WAVELET-BASED AND HHT-BASED FEATURE EXTRACTION METHODS FOR HYPERSPECTRAL IMAGE CLASSIFICATION

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.