Active and Machine Learning for Earth Observation Image Analysis with Traditional and Innovative Approaches

• Main Authors: Dumitru, Corneliu Octavian, Schwarz, Gottfried, Dax, Gabriel, Vlad, Andrei, Ao, Dongyang, Datcu, Mihai
• Other Authors: Arabnia, H. R., Daimi, K., Stahlbock, R., Soviany, C., Heilig, L., Brussau, K.
• Format: Book Part
• Language: unknown
• Published: Springer Nature Switzerland AG 2020
• Subjects: EO Data Science; Belgica Bank; Greenland
• Online Access: https://elib.dlr.de/138139/; https://link.springer.com/book/10.1007/978-3-030-43981-1

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 towards 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.