Land cover classification from coarse resolution time series

Abstract : Land cover classification requires both temporal and spatial information. Indeed, vegetation temporal evolution is necessary to discriminate the different land cover types. This information can be derived from coarse resolution sensors such as MERIS (300 × 300 m² pixel size), or SPOT/VGT (1 km² pixel size), whereas high resolution images, such as SPOT4/HRV ones (20×20 m² pixel size), contain the required spatial information. In this paper, a new method is proposed to perform an efficient land cover classification using these two kinds of remote sensing data. This 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.
Type de document :
Communication dans un congrès
31st Symposium on Remote Sensing of Environment, 2005, Saint Petersburg, Russia. http://www.isprs.org/proceedings/2005/ISRSE/html/papers/512.pdf, 2005
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https://hal.archives-ouvertes.fr/hal-00676268
Contributeur : Lionel Moisan <>
Soumis le : dimanche 4 mars 2012 - 16:23:05
Dernière modification le : mercredi 4 janvier 2017 - 16:23:23

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  • HAL Id : hal-00676268, version 1

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Amandine Robin, Sylvie Le Hégarat-Mascle, Lionel Moisan, Hervé Poilvé. Land cover classification from coarse resolution time series. 31st Symposium on Remote Sensing of Environment, 2005, Saint Petersburg, Russia. http://www.isprs.org/proceedings/2005/ISRSE/html/papers/512.pdf, 2005. <hal-00676268>

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