American Journal of Applied Sciences

Wavelet Shrinkage in Noise Removal of Hyperspectral Remote Sensing Data

Helmi Z.M. Shafri and Paul M. Mather

DOI : 10.3844/ajassp.2005.1169.1173

American Journal of Applied Sciences

Volume 2, Issue 7

Pages 1169-1173


It is common in hyperspectral remote sensing studies to perform analysis based on derivative spectroscopy. However, this technique is particularly sensitive to noise in the data. Thus, noise removal is essential before any derivative analysis. Various methods of noise removal are described in the literature. A newly developed method based on the wavelet transform appears promising, though there is little practical guidance on its use. In this study, the investigation of several important parameters that govern Wavelet-Based Denoising (WBD) is undertaken. The optimal parameter settings are then evaluated for use in spectral analysis using field Spectroradiometer hyperspectral data.


© 2005 Helmi Z.M. Shafri and Paul M. Mather. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.