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Article

Semantic Segmentation of Sentinel-2 Imagery for Mapping Irrigation Center Pivots

by 1,2,*, 2 and 1
1
Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, Austria
2
Vista Remote Sensing in Geosciences GmbH, 80333 Munich, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(23), 3937; https://doi.org/10.3390/rs12233937
Received: 4 November 2020 / Revised: 23 November 2020 / Accepted: 30 November 2020 / Published: 1 December 2020
(This article belongs to the Special Issue Irrigation Mapping Using Satellite Remote Sensing)
Estimating the number and size of irrigation center pivot systems (CPS) from remotely sensed data, using artificial intelligence (AI), is a potential information source for assessing agricultural water use. In this study, we identified two technical challenges in the neural-network-based classification: Firstly, an effective reduction of the feature space of the remote sensing data to shorten training times and increase classification accuracy is required. Secondly, the geographical transferability of the AI algorithms is a pressing issue if AI is to replace human mapping efforts one day. Therefore, we trained the semantic image segmentation algorithm U-NET on four spectral channels (U-NET SPECS) and the first three principal components (U-NET principal component analysis (PCA)) of ESA/Copernicus Sentinel-2 images on a study area in Texas, USA, and assessed the geographic transferability of the trained models to two other sites: the Duero basin, in Spain, and South Africa. U-NET SPECS outperformed U-NET PCA at all three study areas, with the highest f1-score at Texas (0.87, U-NET PCA: 0.83), and a value of 0.68 (U-NET PCA: 0.43) in South Africa. At the Duero, both models showed poor classification accuracy (f1-score U-NET PCA: 0.08; U-NET SPECS: 0.16) and segmentation quality, which was particularly evident in the incomplete representation of the center pivot geometries. In South Africa and at the Duero site, a high rate of false positive and false negative was observed, which made the model less useful, especially at the Duero test site. Thus, geographical invariance is not an inherent model property and seems to be mainly driven by the complexity of land-use pattern. We do not consider PCA a suited spectral dimensionality reduction measure in this. However, shorter training times and a more stable training process indicate promising prospects for reducing computational burdens. We therefore conclude that effective dimensionality reduction and geographic transferability are important prospects for further research towards the operational usage of deep learning algorithms, not only regarding the mapping of CPS. View Full-Text
Keywords: center pivot systems; irrigation; semantic segmentation; U-NET; neural network; AI; Sentinel-2 center pivot systems; irrigation; semantic segmentation; U-NET; neural network; AI; Sentinel-2
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MDPI and ACS Style

Graf, L.; Bach, H.; Tiede, D. Semantic Segmentation of Sentinel-2 Imagery for Mapping Irrigation Center Pivots. Remote Sens. 2020, 12, 3937. https://doi.org/10.3390/rs12233937

AMA Style

Graf L, Bach H, Tiede D. Semantic Segmentation of Sentinel-2 Imagery for Mapping Irrigation Center Pivots. Remote Sensing. 2020; 12(23):3937. https://doi.org/10.3390/rs12233937

Chicago/Turabian Style

Graf, Lukas; Bach, Heike; Tiede, Dirk. 2020. "Semantic Segmentation of Sentinel-2 Imagery for Mapping Irrigation Center Pivots" Remote Sens. 12, no. 23: 3937. https://doi.org/10.3390/rs12233937

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