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Article

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

1
Thünen Institute of Farm Economics, Bundesallee 63, 38116 Braunschweig, Germany
2
Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, D-10099 Berlin, Germany
3
Institute of Geosciences and Geography, Martin-Luther-University Halle-Wittenberg, 06099 Halle, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(18), 3096; https://doi.org/10.3390/rs12183096
Received: 27 July 2020 / Revised: 15 September 2020 / Accepted: 18 September 2020 / Published: 21 September 2020
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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. View Full-Text
Keywords: agricultural parcels; OBIA; multiresolution segmentation; unsupervised segmentation evaluation; spatial autocorrelation; weighted variance; bayesian optimization; optimal segmentation agricultural parcels; OBIA; multiresolution segmentation; unsupervised segmentation evaluation; spatial autocorrelation; weighted variance; bayesian optimization; optimal segmentation
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MDPI and ACS Style

Tetteh, G.O.; Gocht, A.; Schwieder, M.; Erasmi, S.; Conrad, C. Unsupervised Parameterization for Optimal Segmentation of Agricultural Parcels from Satellite Images in Different Agricultural Landscapes. Remote Sens. 2020, 12, 3096. https://doi.org/10.3390/rs12183096

AMA Style

Tetteh GO, Gocht A, Schwieder M, Erasmi S, Conrad C. Unsupervised Parameterization for Optimal Segmentation of Agricultural Parcels from Satellite Images in Different Agricultural Landscapes. Remote Sensing. 2020; 12(18):3096. https://doi.org/10.3390/rs12183096

Chicago/Turabian Style

Tetteh, Gideon O., Alexander Gocht, Marcel Schwieder, Stefan Erasmi, and Christopher Conrad. 2020. "Unsupervised Parameterization for Optimal Segmentation of Agricultural Parcels from Satellite Images in Different Agricultural Landscapes" Remote Sensing 12, no. 18: 3096. https://doi.org/10.3390/rs12183096

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