Abstract
In this study, a crater detection system for a large-scale image database is proposed. The original images are grouped according to spatial frequency patterns and both optimized parameter sets and noise reduction techniques used to identify candidate craters. False candidates are excluded using a self-organizing map (SOM) approach. The results show that despite the fact that a accurate classification is achievable using the proposed technique, future improvements in detection process of the system are needed.
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Honda, R., Iijima, Y., Konishi, O. (2002). Mining of Topographic Feature from Heterogeneous Imagery and Its Application to Lunar Craters. In: Arikawa, S., Shinohara, A. (eds) Progress in Discovery Science. Lecture Notes in Computer Science(), vol 2281. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45884-0_29
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DOI: https://doi.org/10.1007/3-540-45884-0_29
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