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Proceedings Paper

Toward a fuzzy vegetation classifier and an optimal compositing strategy for simulated satellite data on land cover
Author(s): Gil Lissens; Els Brems; Frank Veroustraete
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Paper Abstract

It is well known that remotely sensed reflectances in the visible and near-IR spectral regions are subject to perturbations caused by the atmospheric and geometrical characteristics at the time an image is taken. This paper starts off with a description of a newly developed analysis tool, SATCO, which simulates satellite signals of different surfaces, under different geometrical and atmospheric conditions in the first three spectral bands of the VEGETATION sensor on the SPOT4 platform, due to be launched early 1998. SATCO results are the used in the development of a database that will be the core of a new 'fuzzy' methodology for extracting the top-of-canopy (TOC) reflectances at nadir viewing conditions. From hereon, a simple compositing strategy is developed, resulting in estimated values for TOC over a ten day period. Results of this new methodology compared to conventional compositing strategies based on vegetation indices show a distinct reduction of the root mean square error of the estimated TOC with respect to the true values.

Paper Details

Date Published: 22 December 1997
PDF: 9 pages
Proc. SPIE 3217, Image Processing, Signal Processing, and Synthetic Aperture Radar for Remote Sensing, (22 December 1997); doi: 10.1117/12.295635
Show Author Affiliations
Gil Lissens, Flemish Institute for Technological Research (Belgium)
Els Brems, Flemish Institute for Technological Research (Belgium)
Frank Veroustraete, Flemish Institute for Technological Research (Belgium)

Published in SPIE Proceedings Vol. 3217:
Image Processing, Signal Processing, and Synthetic Aperture Radar for Remote Sensing
Jacky Desachy; Shahram Tajbakhsh, Editor(s)

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