Abstract
Today we are faced with impressive progress in machine learning and artificial intelligence. This not only applies to autonomous driving for car manufacturers but also to Earth observation, where we need reliable and efficient techniques for the automated analysis and understanding of remote sensing data.
While automated classification of satellite images dates back more than 50 years, many recently published deep learning concepts aim at still more reliable and user-oriented image analysis tools. On the other hand, we should also be continuously interested in innovative data analysis approaches that have not yet reached widespread use.
We demonstrate how established applications and tools for image classification and change detection can profit from advanced information theory together with automated quality control strategies. As a typical example, we deal with the task of coastline detection in satellite images; here, rapid and correct image interpretation is of utmost importance for riskless shipping and accurate event monitoring.
If we combine current machine learning algorithms with new approaches, we can see how current deep learning concepts can still be enhanced. Here, information theory paves the way toward interesting innovative solutions.
The validation of the proposed methods will be demonstrated on two target areas: the first one is the Danube Delta, which is the second largest river delta in Europe and is the best preserved one on the continent. Since 1991, the Danube Delta has been inscribed on the UNESCO World Heritage List due do its biological uniqueness. The second one is Belgica Bank in the north-east of Greenland which is an area of extensive fast land-locked ice that is ideal for monitoring seasonal variations of the ice cover and icebergs.
To analyze these two areas, we selected Synthetic Aperture Radar (SAR) images provided by Sentinel-1, a European twin satellite (Taini G et al., SENTINEL-1 satellite system architecture: design, performances and operations. IEEE international geoscience and remote sensing symposium, Munich, pp 1722–1725, 2012) which has an observation rate of one image every 6 days in the case of the Danube Delta and of at least two images per day in the case of Belgica Bank.
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Acknowledgments
The first scenario, the protected area of the Danube Delta, was supported by the H2020 ECOPOTENTIAL project (under grant agreement No. 641762), while the selection of the second scenario, the area of Belgica Bank, was supported by the H2020 ExtremeEarth project (under grant agreement No. 825258). We would like to thank Nick Hughes from Norwegian Meteorological Institute, Norway, for his supporting discussions regarding the selection of the area for the second scenario.
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Dumitru, C.O., Schwarz, G., Dax, G., Andrei, V., Ao, D., Datcu, M. (2020). Active and Machine Learning for Earth Observation Image Analysis with Traditional and Innovative Approaches. In: Arabnia, H.R., Daimi, K., Stahlbock, R., Soviany, C., Heilig, L., Brüssau, K. (eds) Principles of Data Science. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-43981-1_10
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