Feature Extraction of Hyperspectral Images Using Matching Pursuit

Hyperspectral images contain rich and fine spectral information, an improvement of land use/cover classification accuracy is expected from the use of such images. However, the classification methods that have been successfully applied to multispectral data in the past are not as effective as to hyperspectral data. The major cause is that the size of training data set does not correspond to the increase of dimensionality of hyperspectral data. Actually, the problem of the “curse of dimensionality” emerges when a statisticbased classification method is applied to the hyperspectral data. A simpler, but sometimes very effective way of dealing with hyperspectral data is to reduce the number of dimensionality. This can be done by feature extraction that a small number of salient features are extracted from the hyperspectral data when confronted with a limited set of training samples. In this paper, we tested some proposed feature extraction methods based on the wavelet transform to reduce the high dimensionality without losing much discriminating power in the new feature space. In addition, a new feature extraction method based on the matching pursuit with wavelet packet is used to extract useful features for classification. An AVIRIS data set was tested to illustrate the classification performance of the new method and be compared with the existing wavelet-based methods of feature extraction.