Test of different classification methodologies for land cover mapping over France using SPOT/VEGETATION data: applications to the years 2002 and 2003

The present study aims at testing several methodologies of land cover mapping over France at 1 km resolution based on the remotely sensed observations provided by the operational SPOT 4-5/VEGETATION (VGT) Earth observing system. Neural networks classifications are performed to test alternatives for...

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Bibliographische Detailangaben
Hauptverfasser:	Kyung-Soo Han, Tanguy, Y., Champeaux, J.-L., Hagolle, O.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Applied geophysics Clouds Discrete cosine transforms Earth sciences Earth, ocean, space Exact sciences and technology Internal geophysics Multi-layer neural network Neural networks Polynomials Reflectivity Remote sensing Spatial resolution System testing Vegetation mapping
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Beschreibung
Zusammenfassung:	The present study aims at testing several methodologies of land cover mapping over France at 1 km resolution based on the remotely sensed observations provided by the operational SPOT 4-5/VEGETATION (VGT) Earth observing system. Neural networks classifications are performed to test alternatives for the classification of multi-temporal remote sensing data, such as normalized reflectance data and 10-day maximum value composite NDVI (normalized difference vegetation index). The new products shows an improvement of the accuracy compared to Global Land Cover 2000 project (GLC 2000) map over France.
DOI:	10.1109/IGARSS.2004.1369861