Morphological Crater Classification via Convolutional Neural Network with Application on MOLA data

Conference: ANNA '18 - Advances in Neural Networks and Applications 2018
09/15/2018 - 09/17/2018 at St. St. Konstantin and Elena Resort, Bulgaria

Proceedings: ANNA '18

Pages: 5Language: englishTyp: PDF

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Christoff, Nicole; Manolova, Agata (Technical University of Sofia, Faculty of Telecommunications, Sofia, Bulgaria)
Jorda, Laurent (Aix-Marseille Universite, CNRS, LAM UMR 7326, Marseille, France)
Mari, Jean-Luc (Aix-Marseille Universite, Universite de Toulon, CNRS, LIS, Marseille, France)

The only approach for a surface age dating is the impact crater count. In order to facilitate this process, many automatic approaches have been proposed for the impact crater detection. However, the origin and the morphological features of those impact craters can influence the accurate crater count. In this article, we propose a novel approach for crater morphological classification. The developed method is based on a study of a 3D triangulated mesh of Mars’ sample. We use a curvature analysis and local quantization method in combination with a convolution neural network to automatically classify impact craters in three categories: valid, secondary and degraded craters.