Authors:
Kamil Choromański
1
;
Joanna Kozakiewicz
2
;
Mateusz Sobucki
3
;
Magdalena Pilarska-Mazurek
1
and
Robert Olszewski
1
Affiliations:
1
Faculty of Geodesy and Cartography, Warsaw University of Technology, Plac Politechniki 1, 00-665 Warsaw, Poland
;
2
Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, prof. Stanisława Łojasiewicza 11, 30-348 Krakow, Poland
;
3
Faculty of Geography and Geology, Jagiellonian University, Gronostajowa 7, 30-387 Krakow, Poland
Keyword(s):
Deep Learning, Semantic Segmentation, Mars, CNNs, FIS, Aeolian Landscape.
Abstract:
Deep learning analysis of multisource Martian data (both from orbiter and rover) allows for the separation and classification of different geomorphological settings. However, it is difficult to determine the optimal neural network model for unambiguous semantic segmentation due to the specificity of Martian data and blurring of the boundary of individual settings (which is its immanent property). In this paper, the authors describe several variants of multisource deep learning processing system for Martian data and develop a methodology for semantic segmentation of geomorphological settings for this planet based on the combination of selected solutions output. Network ensemble with use of the weighted averaging method improved results comparing to single network. The paper also discusses the decision rule extraction method of individual Martian geomorphological landforms using fuzzy inference systems. The results obtained using FIS tools allow for the extraction of single geomorpholo
gical forms, such as ripples.
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