| Summary: | The aim of the research was to analyze the possibilities of using deep learning methods for classifying multisource image data for Mars. It should be emphasized that the main goal of the research was to develop a methodology for integrating image data acquired from orbiters (MRO mission's HIRISE camera) and <italic>in situ</italic> (opportunity rover's NAVCAM camera) and to use their combined analytical potential. We used a VGG-16-based network for this article, which is well-characterized in the literature and has been successfully applied in a wide range of applications. The article proposes a methodology for the supervised classification of landforms on Mars. The proposed solution was evaluated using the Meridiani Planum area, utilizing neural network deep learning and was based on multisource image data. We found that our approach classified aeolian reliefs correctly for more than 94% of the test dataset. The classification accuracy increased to almost 96% when using panoramas developed from opportunity's images and the derivatives of the digital terrain models used during the classification process. It is possible to broaden the proposed concept of multisource classification and the customized deep learning system to the analysis of other regions of Mars and to multispectral imaging without losing the generalizability of the solution.
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