{"@context":"https://schema.org","@type":"ScholarlyArticle","headline":"Planetary scientific target detection via deep learning: A case study for finding shatter cones in Mars rover images","author":[{"@type":"Person","name":"A. Bechtold"},{"@type":"Person","name":"G. Paar"},{"@type":"Person","name":"Filippo Garolla"},{"@type":"Person","name":"Rebecca Nowak"},{"@type":"Person","name":"L. Fritz"},{"@type":"Person","name":"C. Traxler"},{"@type":"Person","name":"O. Sidla"},{"@type":"Person","name":"C. Koeberl"}],"datePublished":"2023","isPartOf":{"@type":"Periodical","name":"Meteoritics & Planetary Science"},"identifier":"https://doi.org/10.1111/maps.14054","description":"AbstractPast, present, and forthcoming planetary rover missions to Mars and other planetary bodies are equipped with a large number of scientific cameras. The very large number of images resulting from this, combined with tight time constraints for navigation, measurements, and analyses, pose a major challenge for the mission teams in terms of scientific target evaluation. Shatter cones are the only macroscopic evidence for impact‐induced shock metamorphism and therefore impact craters on Earth. The typical features of shatter cones, such as striations and horsetail structures, are particularly suitable for machine learning methods. The necessary training images do not exist for such a case; therefore, we pursued the approach of producing them artificially. Using PRo3D, a viewer developed for the interactive exploration and geologic analysis of high‐resolution planetary surface reconstructions, we virtually placed shatter cones in 3‐D background scenes processed from true Mars rover imagery. We use PRo3D‐rendered images of such scenes as training data for machine learning architectures. Terrestrial analog studies in Ethiopia supported our lab work and were used to test the resulting neural network of this feasibility study. The result showed that our approach with shatter cones in artificial Mars rover scenes is suitable to train neural networks for automatic detection of shatter cones. In addition, we have identified several aspects that can be used to improve the training of the neural network and increase the recognition rate. For example, using background data with a higher resolution in order to have equal resolution of object (shatter cone) and Martian background and increase the number of objects that can be placed in the training data set. Also using better lighting reconstructions and a better radiometric adaption between object and Martian background would further improve the results.","url":"https://innscience.ca/en/community/literature/f49e0aa3-2269-4f50-8902-dcc0e902a3a5"}