首页|Studies from Institute for Astronomy Provide New Data on Machine Learning (Applying Machine Learning To a Nonlinear Spectral Mixing Model for Mapping Lunar Soils Composition Using Chandrayaan-1 M3 Data)
Studies from Institute for Astronomy Provide New Data on Machine Learning (Applying Machine Learning To a Nonlinear Spectral Mixing Model for Mapping Lunar Soils Composition Using Chandrayaan-1 M3 Data)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news originating from Kharkiv, Ukraine, by N ewsRx correspondents, research stated, “We present a newly developed method whic h combines the nonlinear spectral mixing model of Shkuratov et al. (1999) with a machine learning algorithm to map the lunar regolith composition using spectral data. The new method performs orders of magnitude faster than the traditionally used numerical optimization approaches, allowing the mapping of regolith proper ties (including mineralogical composition, average grain size and optical maturi ty) over large areas of the lunar surface.”
KharkivUkraineEuropeCyborgsEmerging TechnologiesMachine LearningInstitute for Astronomy
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