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Land use classification at meso-scale using remotely sensed data

Résumé : In this paper we present a framework to generate a land cover classification from coarse spatial resolution remotely sensed data acquired by NOAA-AVHRR sensor. We define a model for the pixels’ content and a process allowing to compute the individual proportions of the different land cover types for each pixel. The method is based on a linear mixture model of reflectances and exploits the good temporal frequency of NOAA acquisitions. The result provides a description in terms of land covers percentage within each NOAA pixel. A quality evaluation is performed on a test area for which high spatial resolution and temporal NOAA data are simultaneously available.
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Submitted on : Wednesday, April 13, 2016 - 10:25:59 AM
Last modification on : Friday, February 4, 2022 - 3:10:10 AM
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  • HAL Id : inria-00532742, version 1



Sonia Bouzidi, Fabien Lahoche, Isabelle Herlin, Volker Hochschild, Helmut Staudenrausch. Land use classification at meso-scale using remotely sensed data. Proceedings of the International Society for Photogrammetry and Remote Sensing (ISPRS), Jul 2000, Amsterdam, Netherlands. pp.205-212. ⟨inria-00532742⟩



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