International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1061–1068, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1061-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1061–1068, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1061-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  21 Aug 2020

21 Aug 2020

AUTOMATED CLASSIFICATION OF CROP TYPES AND CONDITION IN A MEDITERRANEAN AREA USING A FINE-TUNED CONVOLUTIONAL NEURAL NETWORK

L. A. Ruiz, J. Almonacid-Caballer, P. Crespo-Peremarch, J. A. Recio, J. E. Pardo-Pascual, and E. Sánchez-García L. A. Ruiz et al.
  • Geo-Environmental Cartography and Remote Sensing Group (CGAT), Universitat Politècnica de València, Camí de Vera s/n, 46022 València, Spain

Keywords: Crop classification, Convolutional neural network, VGG-19, Crop abandonment, Orthoimages

Abstract. 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, Sentinel- 2 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.