Abstract:
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[EN] Crop classification based on satellite and aerial imagery is a recurrent application in remote sensing. It has been used as input for
creating and updating agricultural inventories, yield prediction and land management. ...[+]
[EN] Crop classification based on satellite and aerial imagery is a recurrent application in remote sensing. It has been used as input for
creating and updating agricultural inventories, yield prediction and land management. In the context of the Common Agricultural
Policy (CAP), farmers get subsidies based on the crop area cultivated. The correspondence between the declared and the actual crop
needs to be monitored every year, and the parcels must be properly maintained, without signs of abandonment. In this work, Sentinel2 time series images and 4-band Very High Resolution (VHR) aerial orthoimages from the Spanish National Programme of Aerial
Orthophotography (PNOA) were combined in a pre-trained Convolutional Neural Network (CNN) (VGG-19) adapted with a double
goal: (i) the classification of agricultural parcels in different crop types; and (ii) the identification of crop condition (i.e., abandoned
vs. non-abandoned) of permanent crops in a Mediterranean area of Spain. A total of 1237 crop parcels from the CAP declarations of
2019 were used as ground truth to classify into cereals, fruit trees, olive trees, vineyards, grasslands and arable land, from which
80% were used for training and 20% for testing. The overall accuracy obtained was greater than 93% both, at parcel and area levels.
Olive trees were the least accurate crop, mostly misclassified with fruit trees, and young vineyards were slightly confused with cereal
and arable land. In the assessment of crop condition, only 9.65% of the abandoned plots were missed (omission errors), and 7.21% of
plots were over-detected (commission errors), having a 99% of overall accuracy from a total of 1931 image subset samples. The
proposed methodology based on CNN is promising for its operational application in crop monitoring and in the detection of
abandonments in the context of CAP subsidies, but a more exhaustive number of training samples is needed for extension to other
crop types and geographical areas.
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Thanks:
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This research has been funded by the Conselleria d'Agricultura,
Medi Ambient, Canvi Climàtic i Desenvolupament Rural,
Generalitat Valenciana, throught the nominative line S847000.
The authors also thank the Institut ...[+]
This research has been funded by the Conselleria d'Agricultura,
Medi Ambient, Canvi Climàtic i Desenvolupament Rural,
Generalitat Valenciana, throught the nominative line S847000.
The authors also thank the Institut Cartogràfic Valencià for
providing very high resolution aerial imagery data of the study
area.
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