Analysis of Ensemble of Neural Networks and Fuzzy Logic Classification in Process of Semantic Segmentation of Martian Geomorphological Settings

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 geomorphological forms, such as ripples.