Agricultural field delimitation using active learning and random forests margin

Agricultural practices and spatial arrangements of fields have a strong impact on water flows in cultivated landscapes. In order to monitor landscapes at a large scale, there is a strong need for automatic or semi-automatic field delineation. Field measurements for delineating parcel network are not efficient, thus very high resolution satellite imagery should help delineating agricultural fields in a automatic way. This study focuses on agricultural field delineation based on the classification of very high resolution satellite imagery. A hybrid approach is proposed and combines a region-based approach and active learning (AL) techniques. Random forest (RF) classifier is used for classification and feature selection. The margin concept is used as uncertainty measure in active learning algorithm. Satisfying results are shown on a Geoeye image. AL RF model is compared to simple and global RF models that are built from adjacent and geographically distant fields respectively.