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Feature Extraction Algorithms for Automatic Craters Identification
Nicole Christoff
Technical University of Sofia 8 Kl. Ohridski Blvd, Sofia 1000 Bulgaria., Aix-Marseille Université CNRS, LSIS UMR 7296 France
Abstract: Recently the feature selection algorithms are extensively studied. Using 3D data, the features are drawn for automatic classification and identify craters. This will also help to text the performance of the classifiers. Our intention in this work is to observe the discriminative power of the original values, hereafter called “pure” values, of a minimal curvature by only converting them in the range of grey scale. We have tested the system and found that the five different classifiers show that better accuracy results are obtained over the features selected from the grey scale image. We also found that the method from computer vision is applied for the crater detection.
Keywords: Mars Orbiter Laser Altimeter, 3D Mesh, Automatic Craters Detection, Machine Learning Feature Extraction Algorithms for Automatic Craters Identification
DOI:https://doi.org/10.6025/jmpt/2021/12/1/1-8
Full_Text   PDF 2.10 MB   Download:   278  times
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