Detecting and classifying vine varieties from very high resolution multispectral data

In order to exploit operationally remote sensing data for agricultural applications efficient and automated methods are required towards the accurate detection of vegetation, crops and different crop varieties. To this end, an object-based classification framework has been developed and validated towards the detection of vineyards and the discrimination of vine varieties. Very high resolution satellite data were collected over four wine-growing regions in Greece during a three-year period, i.e., 2012 to 2014. A rule-based classification scheme based on fuzzy logic was employed in order to firstly detect the vine parcels in the pan-sharpened multispectral satellite images. Then the canopy of each parcel was detected and separated from the soil in-between the vine rows. The detection of the different vine varieties followed based on a supervised classification procedure and spectral features. The overall validation and quite promising experimental results indicated that a throughout sensitivity analysis can form efficient operational tools for variety-based data analysis in precision viticulture.