Staff Publications

Staff Publications

  • external user (warningwarning)
  • Log in as
  • language uk
  • About

    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 346422
Title Consideration of smoothing techniques for hyperspectral remote sensing
Author(s) Vaiphasa, C.
Source ISPRS Journal of Photogrammetry and Remote Sensing 60 (2006)2. - ISSN 0924-2716 - p. 91 - 99.
DOI https://doi.org/10.1016/j.isprsjprs.2005.11.002
Department(s) Resource Ecology
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2006
Keyword(s) feature-selection - derivative analysis - vegetation - spectra - model - differentiation - discrimination - inversion - canopies - accuracy
Abstract Spectral smoothing filters are popularly used in a large number of modern hyperspectral remote sensing studies for removing noise from the data. However, most of these studies subjectively apply ad hoc measures to select filter types and their parameters. We argue that this subjectively minded approach is not appropriate for choosing smoothing methods for hyperspectral applications. In our case study, it is proved that smoothing filters can cause undesirable changes to statistical characteristics of the spectral data; thereby, affecting the results of the analyses that are based on statistical class models. If preserving statistical properties of the original hyperspectral data is desired, smoothing filters should then be used, if necessary, after careful consideration of which smoothing techniques will minimize disturbances to the statistical properties of the original data. A comparative t-test is proposed as a method for choosing a smoothing filter suitable for hyperspectral data at hand.
Comments
There are no comments yet. You can post the first one!
Post a comment
 
Please log in to use this service. Login as Wageningen University & Research user or guest user in upper right hand corner of this page.