3:30 PM - 3:45 PM
[PPS23-06] Unsupervised Classification of the Moon's Surface Reflectance Spectra and Geological Significance (1)
Keywords:Moon, Geological Map, Unsupervised Classification
In this study, we show some classification results of SELENE Multiband Imager (MI) data and Spectral Profiler (SP) data applied data mining methods, and compare them with a fully manual classification result for a limited area. Our classification procedure consists of two steps; Independent Component Analysis (ICA) and Iterative Self-Organizing Data Analysis (ISODATA). Detail strategy of our procedure is presented by Hareyama et al. in this meeting.
Our procedure generally works well. The classification results in mare region indicate that could detect some types of mare basalt flows. Especially high-Ti basalt in Oceanus Procellarum and the Mare Tranquillitatis are clearly identified. Ejecta deposits of fresh ray craters are also clearly identified. In addition, we compare classification results our procedure around the Aristarchus region with that of fully manual classification result by a researcher (M.O.). These two agrees each other generally. Then, we consider our procedure capture the lunar geological context and useful for the first step of building lunar geological map.