A novel architecture for automated delineation of the agricultural fields using partial training data in remote sensing images

Abstract

Digital agricultural services (DAS) rely on timely and accurate spatial information of agricultural fields. Initiatives, including deep learning (DL), have been used to extract accurate spatial information using remote sensing images. However, DL approaches require a large amount of fully segmented and labelled field boundary data for training that is not readily available. Obtaining high-quality training data is often costly and time-consuming. To address this challenge, we develop a multi-scale, multi-task DL-based novel architecture with two modules, an edge enhancement block (EEB) and a spatial attention block (SAB), using partial training data (i.e., weak supervision). This architecture is capable of delineating narrow and weak boundaries of agricultural fields. The model simultaneously learns three tasks: boundary prediction, extent prediction and distance estimation, and enhances the generalisability of the network. The EEB module extracts semantic edge features at multiple levels. The SAB module integrates the features from the encoder and decoder to improve the geometric accuracy of field boundary delineation. We conduct an experiment in Ille-et-Vilaine, France, using time-series monthly composite images from Sentinel-2 to capture key phenological stages of crops. The segmentation output from different months is combined and post-processed to generate individual field instances using hierarchical watershed segmentation. The performance of our method is superior in both pixel-based (86.42% Matthew’s correlation coefficient (MCC)) and object-based accuracy measures (76% shape similarity and 60% intersection over union (IoU)) to existing multi-task models. The ablation study shows that the EEB and SAB modules enhance the efficiency of feature extraction relevant to field extent and boundaries and improve accuracy. We conclude that the developed model and method can be used to improve the extraction of agricultural fields under weak supervision and different settings (satellite sensors and agricultural landscape).