Optimal parameters for delineating agricultural parcels from satellite images based on supervised Bayesian optimization

Accurate spatial information of agricultural parcels is fundamental to any system used in monitoring greenhouse gas emissions, biodiversity developments, and nutrient loading in agriculture. The inefficiency of the traditional methods used in obtaining this information is increasingly paving the way for Remote Sensing (RS). The Multiresolution Segmentation (MRS) algorithm is a well-known method for segmenting objects from images. The quality of segmentation depends on the a priori knowledge of which scale, shape and compactness values to use. With each parameter taking a varied range of input values, this research developed an automated approach for identifying the optimal parameter set without testing all possible combinations. At the core of our approach is Bayesian optimization, which is a sequential model-based optimization (SMBO) method for maximizing or minimizing an objective function. We maximized the Jaccard index, which is a measure that indicates the similarity between segmented agricultural objects and their corresponding reference parcels. As the optimal parameter combination varies between different agricultural landscapes, they were determined at a grid resolution of 10 km. Mono-temporal Sentinel-2 images covering Lower Saxony in Germany were tiled to these grids and the optimal parameters were subsequently identified for each tiled grid. The optimal parameter combinations identified over the grids varied considerably, which indicated that a single parameter combination would have failed to achieve optimal segmentation. We found that the quality of segmentation correlated with the size of agricultural parcels. Under-segmentation was largely minimized but in areas with a predominant agricultural land-use, it was unavoidable. In agricultural parcels composed of heterogeneous pixels, over-segmentation was prevalent. Our approach outperformed other segmentation optimization methods existing in the literature.