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- W2016502309 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." @default.
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- W2016502309 date "2015-07-01" @default.
- W2016502309 modified "2023-09-27" @default.
- W2016502309 title "Parcel based classification for agricultural mapping and monitoring using multi-temporal satellite image sequences" @default.
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- W2016502309 doi "https://doi.org/10.1109/igarss.2015.7325725" @default.
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