A fast feature extraction method based on integer wavelet transform for hyperspectral images

Hyperspectral remote sensing provides high-resolution spectral data and the potential for remote discrimination between subtle differences in ground covers. However, the high-dimensional data space generated by the hyperspectral sensors creates a new challenge for conventional spectral data analysis techniques. A challenging problem in using hyperspectral data is to eliminate redundancy and preserve useful spectral information for applications. In this paper, a Fast feature extraction (FFE) method based on integer wavelet transform is proposed to extract useful features and reduce dimensionality of hyperspectral images. The FFE method can be directly used to extract useful features from spectral vector of each pixel resident in the hyperspectral images. The FFE method has two main merits: high computational efficiency and good ability to extract spectral features. In order to better testify the effectiveness and the performance of the proposed method, classification experiments of hyperspectral images are performed on two groups of AVIRIS (Airborne visible/infrared imaging spectrometer) data respectively. In addition, three existing methods for feature extraction of hyperspectral images, i.e. PCA, SPCT and Wavelet Transform, are performed on the same data for comparison with the proposed method. The experimental investigation shows that the efficiency of the FFE method for feature extraction outclasses those of the other three methods mentioned above.