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|Title:||Parcel based classification for agricultural mapping and monitoring using multi-temporal satellite image sequences|
|Authors:||KUSSUL NATALIA; LEMOINE GUIDO; GALLEGO PINILLA FRANCISCO; SKAKUN SERGII; LAVRENIUK MYKOLA|
|Citation:||INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) p. 165-168|
|Publisher:||INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS|
|Type:||Articles in periodicals and books|
|Abstract:||In this paper, we propose a new approach to pixel and parcel-based classification of multi-temporal optical satellite imagery. We first restore missing data due to clouds and shadows based on vector and raster data fusion in different phases of classification methodology. Pixel-based classification maps are derived from an ensemble of neural networks, in particular multilayer perceptrons (MLPs).. The proposed approach is applied for regional scale crop classification using multi-temporal Landsat-8 images for the JECAM site in the Kyivska oblast of Ukraine in 2013. The obtained results on crop area estimates are also compared to official statistics.|
|JRC Directorate:||Sustainable Resources|
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