TY  - JOUR
AU  - Shafri, Helmi Z.M. 
AU  - Mather, Paul M. 
PY  - 2005
TI  - Wavelet Shrinkage in Noise Removal of Hyperspectral Remote Sensing Data
JF  - American Journal of Applied Sciences
VL  - 2
IS  - 7
DO  - 10.3844/ajassp.2005.1169.1173
UR  - https://thescipub.com/abstract/ajassp.2005.1169.1173
AB  - 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.