Smoothing vegetation spectra with wavelets

K. S. Schmidt*, A. K. Skidmore

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

69 Citations (Scopus)


In hyperspectral remote sensing, spectra are increasingly analysed using methods developed for laboratory studies, such as derivative analysis. These techniques require smooth reflectance spectra. Therefore, there is a need for smoothing algorithms that fulfil the requirement of preserving local spectral features while simultaneously removing noise. Noise occurs in variable intensity and over different band widths. Several methods for smoothing a signal exist, including the widely used median and mean filters, the Savitzky–Golay filter applied to laboratory spectra, the cubic spline, and the recently developed transform-based thresholding using the wavelet transform. We compare all these methods using reflectance spectra of the canopy of salt marsh vegetation. The best trade-off between noise reduction and the preservation of spectral features was found to be the wavelet transform, specifically using a translation invariant de-noising based on the non-decimated or stationary wavelet transform.

Original languageEnglish
Pages (from-to)1167-1184
Number of pages18
JournalInternational Journal of Remote Sensing
Issue number6
Publication statusPublished - 2004
Externally publishedYes


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