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Remote Sens. 2019, 11(8), 907; https://doi.org/10.3390/rs11080907

Evaluation of the Potential of Convolutional Neural Networks and Random Forests for Multi-Class Segmentation of Sentinel-2 Imagery

European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy
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The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission.
Received: 28 February 2019 / Revised: 4 April 2019 / Accepted: 6 April 2019 / Published: 14 April 2019
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Abstract

Motivated by the increasing availability of open and free Earth observation data through the Copernicus Sentinel missions, this study investigates the capacity of advanced computational models to automatically generate thematic layers, which in turn contribute to and facilitate the creation of land cover products. In concrete terms, we assess the practical and computational aspects of multi-class Sentinel-2 image segmentation based on a convolutional neural network and random forest approaches. The annotated learning set derives from data that is made available as result of the implementation of European Union’s INSPIRE Directive. Since this network of data sets remains incomplete in regard to some geographic areas, another objective of this work was to provide consistent and reproducible ways for machine-driven mapping of these gaps and a potential update of the existing ones. Finally, the performance analysis identifies the most important hyper-parameters, and provides hints on the models’ deployment and their transferability. View Full-Text
Keywords: INSPIRE; Sentinel-2; land cover; machine learning; convolutional neural network; random forest; segmentation; supervised learning; performance analysis INSPIRE; Sentinel-2; land cover; machine learning; convolutional neural network; random forest; segmentation; supervised learning; performance analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Syrris, V.; Hasenohr, P.; Delipetrev, B.; Kotsev, A.; Kempeneers, P.; Soille, P. Evaluation of the Potential of Convolutional Neural Networks and Random Forests for Multi-Class Segmentation of Sentinel-2 Imagery. Remote Sens. 2019, 11, 907.

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