Parcel based classification for agricultural mapping and monitoring using multi-temporal satellite image sequences
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.
KUSSUL Natalia;
LEMOINE Guido;
GALLEGO PINILLA Francisco;
SKAKUN Sergii;
LAVRENIUK Mykola;
2020-04-27
INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS
JRC99016
https://ieeexplore.ieee.org/document/7325725,
https://publications.jrc.ec.europa.eu/repository/handle/JRC99016,
10.1109/IGARSS.2015.7325725 (online),
Additional supporting files
| File name | Description | File type | |
|---|
