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- W2901139022 abstract "Automatic analysis of Sentinel image time series is recommended for monitoring agricultural land use in Europe. To improve classification capacities, we propose a temporal structured classification combining Sentinel images and former vintages of the Land-Parcel Identification System. Inter-annual crop rotations are learned and combined with the satellite images using a Conditional Random Field. The proposed methodology is tested on a 233 km <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> study area located in France and with a 25 categories national nomenclature. The classification results are globally improved." @default.
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- W2901139022 date "2018-07-01" @default.
- W2901139022 modified "2023-09-30" @default.
- W2901139022 title "Crop-Rotation Structured Classification using Multi-Source Sentinel Images and LPIS for Crop Type Mapping" @default.
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- W2901139022 doi "https://doi.org/10.1109/igarss.2018.8518427" @default.
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