Fast wavelet-based algorithms for multiresolutional decomposition and feature extraction of hyperspectral signatures

Spectral features are often extracted from multispectral/hyperspectral data using a multiresolutional decomposition known as the spectral fingerprint. While the spectral fingerprint method has proven to be quite powerful, it has also shown several shortcomings: 1) its implementation requires multiple convolutions with Laplacian-of-Gaussian (LoG) filters which are computationally expensive, 2) it requires a truncation of the filter impulse response which can cause spurious errors, and 3) it provides information about the sizes and areas of radiance features but not the shapes. It is proposed that a wavelet-based spectral fingerprint can overcome these shortcomings while maintaining the advantages of the traditional method. In this study, we investigate the use of the wavelet transform modulus-maximus (mod-max) method to generate a wavelet-based spectral fingerprint. The computation of the wavelet-based fingerprint is based on recent fast wavelet algorithms. The analyses consists of two parts: 1) the computational expense of the new method is compared with the computational costs of current methods, and 2) the outputs of the wavelet-based methods are compared with those of current methods to determine any practical differences in the resulting spectral fingerprints.