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Proceedings Paper

Adaptive multivariate smoothing of satellite image data
Author(s): J. Vandeneede; Patrick Wambacq; Andre J. Oosterlinck
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Paper Abstract

This paper deals with nonlinear smoothing of multispectral satellite images of which the components generally are correlated. A literature search revealed the existence of two recent nonlinear multivariate noise smoothing filters. One is a coupled diffusion equation smoothing filter, which consists of simultaneously solving for each image component, a non-linear diffusion equation that is coupled with the other equations through a discontinuity function. The other is a so called FIR vector median hybrid filter, which in essence is a multivariate median filter. In this paper multivariate versions of three effective nonlinear grey level filters are also presented. Two of them are extensions of weighted local mean filters, the third computes an optimal linear combination between the multivariate pixel vector and its local mean vector. The noise reduction and edge preserving capabilities of these five filters are evaluated in view of an additive noise model for SPOT HRVIR images.

Paper Details

Date Published: 17 November 1995
PDF: 15 pages
Proc. SPIE 2579, Image and Signal Processing for Remote Sensing II, (17 November 1995); doi: 10.1117/12.226862
Show Author Affiliations
J. Vandeneede, Katholieke Univ. Leuven (Belgium)
Patrick Wambacq, Katholieke Univ. Leuven (Belgium)
Andre J. Oosterlinck, Katholieke Univ. Leuven (Belgium)


Published in SPIE Proceedings Vol. 2579:
Image and Signal Processing for Remote Sensing II
Jacky Desachy, Editor(s)

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