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Proceedings Paper

A multiscale multitemporal land cover classification method using a Bayesian approach
Author(s): A. Robin; S. Mascle-Le Hégarat; L. Moisan
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Paper Abstract

As vegetation time evolution is one of the most relevant information to discriminate the different land cover types, land cover classification requires both temporal and spatial information. Due to the physical properties of remote sensors, this temporal information can only be derived from coarse resolution sensors such as MERIS (300×300 m2 pixel size) or SPOT/VGT (1 km2 pixel size). In this paper, we propose to use jointly high and coarse spatial resolution to perform an efficient high resolution land cover classification. The method is based on Bayesian theory and on the linear mixture model permitting, through a simulated annealing algorithm, to perform a high resolution classification from a coarse resolution time series.

Paper Details

Date Published: 18 October 2005
PDF: 12 pages
Proc. SPIE 5982, Image and Signal Processing for Remote Sensing XI, 598204 (18 October 2005); doi: 10.1117/12.627604
Show Author Affiliations
A. Robin, CETP (France)
MAP5 (France)
S. Mascle-Le Hégarat, CETP (France)
L. Moisan, MAP5 (France)

Published in SPIE Proceedings Vol. 5982:
Image and Signal Processing for Remote Sensing XI
Lorenzo Bruzzone, Editor(s)

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