Enhancing hyperspectral data throughput utilizing wavelet-based fingerprints

Lori Mann Bruce; Jiang Li

doi:10.1117/12.373260

14 December 1999 Enhancing hyperspectral data throughput utilizing wavelet-based fingerprints

Lori Mann Bruce, Jiang Li

Proceedings Volume 3871, Image and Signal Processing for Remote Sensing V; (1999) https://doi.org/10.1117/12.373260
Event: Remote Sensing, 1999, Florence, Italy

Abstract

Multiresolutional decompositions known as spectral fingerprints are often used to extract spectral features from multispectral/hyperspectral data. In this study, we investigate the use of wavelet-based algorithms for generating spectral fingerprints. The wavelet-based algorithms are compared to the currently used method, traditional convolution with first-derivative Gaussian filters. The comparison analyses consists of two parts: (1) the computational expense of the new method is compared with the computational costs of the current method and (2) the outputs of the wavelet-based methods are compared with those of the current method to determine any practical differences in the resulting spectral fingerprints. The results show that the wavelet-based algorithms can greatly reduce the computational expense of generating spectral fingerprints, while practically no differences exist in the resulting fingerprints. The analysis is conducted on a database of hyperspectral signatures, namely, Hyperspectral Digital Image Collection Experiment (HYDICE) signatures. The reduction in computational expense is by a factor of about 30, and the average Euclidean distance between resulting fingerprints is on the order of 0.02.

Citation Download Citation

Lori Mann Bruce and Jiang Li "Enhancing hyperspectral data throughput utilizing wavelet-based fingerprints", Proc. SPIE 3871, Image and Signal Processing for Remote Sensing V, (14 December 1999); https://doi.org/10.1117/12.373260

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