DBC: Deep Boundaries Combination for Farmland Boundary Detection Based on UAV Imagery

Benefiting from the advantages of flexibility and timeliness, Unmanned Aerial Vehicles (UAVs) play an important role in crop growth monitoring, precision agriculture and intelligent agriculture. This paper focuses on the farmland boundary detection in UAV images. Traditional farmland boundary detection methods have problems such as over-segmentation and discontinuous boundary. To address these problems, we propose a Deep Boundaries Combination (DBC) algorithm for the detection of farmland plots boundaries in UAV remote sensing images. DBC uses deep convolutional networks to obtain edge probability map of farmland images, and then applies Oriented Watershed Transform (OWT) and Ultrametric Contour Map (UCM) to convert edge probability map into closed boundary hierarchy tree, which layers the boundaries by edge probability. We perform experiments on two farmland images acquired by UAV. Experimental results show that our method can extract more accurate farmland boundaries than other methods.