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

Tetteh, Gideon Okpoti; Gocht, Alexander; Conrad, Christopher

doi:10.1016/j.compag.2020.105696

Cited by 19 publications

(40 citation statements)

References 45 publications

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“…In this study, we used cloud-free Sentinel-2 images downloaded from the Copernicus Open Access Hub (https://scihub.copernicus.eu) covering the German federal state of Lower Saxony. The images were pre-processed in the previous study of Tetteh et al [47] using the standard procedure of converting the top-of-atmosphere Level-1C images to the bottom-of-atmosphere Level-2A images with Sen2Cor [48] in the Sentinel Application Platform (SNAP) software. For each Level-2A image, the visible (red, green, blue) and near-infrared bands were extracted and composed into an image made up of four bands.…”

Section: Study Area and Datamentioning

confidence: 99%

“…This image is henceforth named VNIR. Each VNIR image has a spatial resolution of 10 m. To identify the optimal MRS parameters needed for segmenting agricultural parcels for every part of Lower Saxony, Tetteh et al [47] clipped the VNIR images with 10 × 10 km tile grids numbering 562 and additionally masked out all non-agricultural areas such as forests, built-up areas, water bodies, and roads. Out of these 562 images, we selected 21 tiles that spread across Lower Saxony as our study sites ( Figure 1).…”

Section: Study Area and Datamentioning

confidence: 99%

“…Starting with one-pixel objects as seed points, in numerous subsequent steps, where the difference in heterogeneity between an object and any of its neighbors is minimal, the two objects are merged into a bigger one [22]. The heterogeneity of an object is calculated using the color and shape of that object [22,47]. The pairwise merging process is terminated when a user-given threshold is met [22].…”

Section: Image Segmentationmentioning

confidence: 99%

“…In the second optimization approach, we employed Bayesian optimization to optimize all three MRS parameters. The optimal segmentation results identified by each metric were evaluated with parcels from the Land Parcel Identification System (LPIS), which is a spatial database of agricultural parcels and their land-use types as declared by farmers within the European Union (EU) [46,47]. The optimal segmentation results of the Böck and AD metrics were compared to each other per each optimization approach.…”

Section: Introductionmentioning

confidence: 99%

“…The optimal segmentation results of the Böck and AD metrics were compared to each other per each optimization approach. Further, we compared the optimal segmentation results of the unsupervised Bayesian optimization approaches based on the Böck and AD metrics to the benchmark segmentation results of Tetteh et al [47], where they used supervised Bayesian optimization.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised Parameterization for Optimal Segmentation of Agricultural Parcels from Satellite Images in Different Agricultural Landscapes

et al. 2020

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Image segmentation is a cost-effective way to obtain information about the sizes and structural composition of agricultural parcels in an area. To accurately obtain such information, the parameters of the segmentation algorithm ought to be optimized using supervised or unsupervised methods. The difficulty in obtaining reference data makes unsupervised methods indispensable. In this study, we evaluated an existing unsupervised evaluation metric that minimizes a global score (GS), which is computed by summing up the intra-segment uniformity and inter-segment dissimilarity within a segmentation output. We modified this metric and proposed a new metric that uses absolute difference to compute the GS. We compared this proposed metric with the existing metric in two optimization approaches based on the Multiresolution Segmentation (MRS) algorithm to optimally delineate agricultural parcels from Sentinel-2 images in Lower Saxony, Germany. The first approach searches for optimal scale while keeping shape and compactness constant, while the second approach uses Bayesian optimization to optimize the three main parameters of the MRS algorithm. Based on a reference data of agricultural parcels, the optimal segmentation result of each optimization approach was evaluated by calculating the quality rate, over-segmentation, and under-segmentation. For both approaches, our proposed metric outperformed the existing metric in different agricultural landscapes. The proposed metric identified optimal segmentations that were less under-segmented compared to the existing metric. A comparison of the optimal segmentation results obtained in this study to existing benchmark results generated via supervised optimization showed that the unsupervised Bayesian optimization approach based on our proposed metric can potentially be used as an alternative to supervised optimization, particularly in geographic regions where reference data is unavailable or an automated evaluation system is sought.

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Section: Study Area and Datamentioning

confidence: 99%

Section: Study Area and Datamentioning

confidence: 99%