Automatic Detection of Anomalous Time Trends from Satellite Image Series to Support Agricultural Monitoring

2021 
The increasing availability of huge amounts of satellite data, together with the increasing computation power available at relatively low cost, is requiring and at the same time allowing the development of new algorithms to automatically extract information from the data. In this work, we propose a new method for automatic detection, from series of satellite images, of possible anomalies relative to crop parcels declared by farmers in the framework of EU's Common Agricultural Policy (CAP). Differently from other recently explored methods, our technique is not based on a crop classification approach. On the contrary, we approached the problem as an anomaly detection problem, and our method bases only on the quite realistic and general assumption that declarations are mostly correct, with a moderate number of outliers. Therefore, our technique is robust to variations in weather conditions, terrain morphology and agriculture practices. In order to detect the anomalies, the method computes the “distances” between the different parcels with a given declared crop. In particular, the time series of the features extracted from the satellite data on the different parcels are compared. Their distance is defined according to the Dynamic Time Warping (DTW) method, robust to temporal variations. The tests performed were very good, and the technique has been already operationally used with satisfactory results. In particular, the automatic anomaly detection approach has made it possible to verify all the farmers' declarations in a large area (a significant portion of Italy), and will make it possible to process even larger areas, such as the whole Italy or the whole Europe.
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