Research Article Open Access

Wavelet Shrinkage in Noise Removal of Hyperspectral Remote Sensing Data

Helmi Z.M. Shafri1 and Paul M. Mather2
  • 1 Universiti Putra Malaysia, (UPM), 43400 Serdang, Selangor, Malaysia
  • 2 Geographic Information Science Research, the University of Nottingham, NG7 2RD Nottingham, United Kingdom

Abstract

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.

American Journal of Applied Sciences
Volume 2 No. 7, 2005, 1169-1173

DOI: https://doi.org/10.3844/ajassp.2005.1169.1173

Submitted On: 28 June 2005 Published On: 31 July 2005

How to Cite: Shafri, H. Z. & Mather, P. M. (2005). Wavelet Shrinkage in Noise Removal of Hyperspectral Remote Sensing Data. American Journal of Applied Sciences, 2(7), 1169-1173. https://doi.org/10.3844/ajassp.2005.1169.1173

  • 2,946 Views
  • 2,228 Downloads
  • 10 Citations

Download

Keywords

  • Wavelets
  • denoising
  • hyperspectral