Classification methodology: * First method Unsupervised classification (k-means) on annual temporal 10-days (MVC) NDVI profiles * Second method - Selection of clear dates uniformly distributed during the year Each pixel is defined: P i (B0 J1,B2 J1,B3 J1,MIR J1,… B0 Jn,B2 Jn,B3 Jn,MIR Jn ) - Principal Component Analysis and selection of principal components the more explicative -Unsupervised classification (kmeans) on principal components
Confusion matrix between LC classification And Corine Land Cover used as reference for FRANCE Urban Crops Pastu. BLF CF MF Shrubs Urban Crops Pastures Forests Water Shrubs 64.4 2.2 0.6 0.9 0.7 0.7 0.4 19.4 81.3 20.2 17.9 3.8 8.7 7.2 3.4 9.0 71.0 8.8 2.9 11.2 21.1 2.9 5.6 4.3 71.6 89.2 74.9 18.0 2.3 0 0.1 0 0.9 0.2 1.2 2. 0 0.8 0.3 2.0 3.2 44.8
Unsatisfactory detection of permanent crops Unsatisfactory discrimination of coniferous and broad-leaved forests Problem of detection of urban areas: Use of an urban mask (not up to date?) Detection of water bodies Use of a water mask? Discontinuities of land cover classes at the frontier of 2 different regions How to treat mixed classes in nomenclature: pastures-crops, forests-crops-pastures Problems …questions….ANSWERS (?)
CONCLUSIONS Good accuracy with VEGETATION data some improvements were obtained with S1 channels (B2,B3,MIR) VALIDATION: we have an updated Corine Land Cover file (Spot, Landsat 1999) for Provence-Alpes-Cote d’Azur region Some problems must be solved before running the operational algorithm over France
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