Application of Deep Learning to Optical and SAR Images for the Classification of Agricultural Areas in Italy

Modern agriculture is facing new challenges about food production for a growing population in a sustainable manner. Crop mapping at local and regional scale could provide valuable information in support of agricultural policy. This paper describes a field mapping investigation in a populated area in Tuscany (Italy). Satellite images from Sentinel-1 C-band and COSMO-SkyMed X-band SAR and Sentinel-2 optical sensors are input of classifiers based on deep learning and convolutional neural networks. Results pinpointed that the use of optical images allowed the best overall classification accuracy (99.7%), nevertheless X-band SAR imagery, providing an accuracy of 94.6%, could be a good substitute of optical indices in case of lack of cloud-free multispectral data.