Deep Learning and Adaptive Graph-Based Growing Contours for Agricultural Field Extraction
Field mapping and information on agricultural landscapes is of increasing importance for many applications. Monitoring schemes and national cadasters provide a rich source of information but their maintenance and regular updating is costly and labor-intensive. Automatized mapping of ﬁelds based on remote sensing imagery may aid in this task and allow for a faster and more regular observation. Although remote sensing has seen extensive use in agricultural research topics, such as plant health monitoring, crop type classiﬁcation, yield prediction, and irrigation, ﬁeld delineation and extraction has seen comparatively little research interest. In this study, we present a ﬁeld boundary detection technique based on deep learning and a variety of image features, and combine it with the graph-based growing contours (GGC) method to extract agricultural ﬁelds in a study area in northern Germany. The boundary detection step only requires red, green, and blue (RGB) data and is therefore largely independent of the sensor used. We compare diﬀerent image features based on color and luminosity information and evaluate their usefulness for the task of ﬁeld boundary detection. A model based on texture metrics, gradient information, Hessian matrix eigenvalues, and local statistics showed good results with accuracies up to 88.2%, an area under the ROC curve (AUC) of up to 0.94, and F1 score of up to 0.88. The exclusive use of these universal image features may also facilitate transferability to other regions. We further present modiﬁcations to the GGC method intended to aid in upscaling of the method through process acceleration with a minimal eﬀect on results. We combined the boundary detection results with the GGC method for ﬁeld polygon extraction. Results were promising, with the new GGC version performing similarly or better than the original version while experiencing an acceleration of 1.3× to 2.3× on diﬀerent subsets and input complexities. Further research may explore other applications of the GGC method outside agricultural remote sensing and ﬁeld extraction.