Enhancing Hyperspectral Data Throughput Utilizing Wavelet-Based Fingerprints
Multiresolutional decompositions known as spectral fingerprints are often used to extract spectral features from multispectral/hyperspectral data. In this study, the authors 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: (a) the computational expense of the new method is compared with the computational costs of the current method and (b) 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.
- Research Organization:
- Bechtel Nevada Corp. (US)
- Sponsoring Organization:
- US Department of Energy (US)
- DOE Contract Number:
- AC08-96NV11718
- OSTI ID:
- 764641
- Report Number(s):
- DOE/NV/11718-366; TRN: AH200034%%27
- Resource Relation:
- Conference: EUROPTO-European Optical Society and the International Society for Optical Engineering, Florence (IT), 08/24/1999; Other Information: PBD: 1 Sep 1999
- Country of Publication:
- United States
- Language:
- English
Similar Records
Using soil library hyperspectral reflectance and machine learning to predict soil organic carbon: Assessing potential of airborne and spaceborne optical soil sensing
Unsupervised hyperspectral image analysis using independent component analysis (ICA)