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Automatic Crater Classification Framework Based on Shape Parameters


Affiliations
1 Department of Computer Science, Gujarat University, Ahmedabad 380 015, India
2 Indian Institute of Remote Sensing, Indian Space Research Organization, Dehradun 248 001, India
 

This communication presents a framework for automatically classifying a crater image into one of its preservation states namely fresh, floor-fractured and degraded introducing a class of algorithms known as crater classification algorithms (CCA). This study involves identification of discriminatory parameters of classes, development and implementation of algorithms to automatically evaluate the parameters from a given Digital Elevation Model testing on representative craters of each class and evolve a decision tree framework for automatically classifying given crater image into its preservation class. This classification can be applied to craters that exhibit ambiguous topographies to test whether they were formed by impact erosion or igneous modification.

Keywords

Classification Algorithms, Computational Intelligence, Impact Craters, Shape Parameters.
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  • Automatic Crater Classification Framework Based on Shape Parameters

Abstract Views: 183  |  PDF Views: 74

Authors

Suchit Purohit
Department of Computer Science, Gujarat University, Ahmedabad 380 015, India
Savita Gandhi
Department of Computer Science, Gujarat University, Ahmedabad 380 015, India
Prakash Chauhan
Indian Institute of Remote Sensing, Indian Space Research Organization, Dehradun 248 001, India

Abstract


This communication presents a framework for automatically classifying a crater image into one of its preservation states namely fresh, floor-fractured and degraded introducing a class of algorithms known as crater classification algorithms (CCA). This study involves identification of discriminatory parameters of classes, development and implementation of algorithms to automatically evaluate the parameters from a given Digital Elevation Model testing on representative craters of each class and evolve a decision tree framework for automatically classifying given crater image into its preservation class. This classification can be applied to craters that exhibit ambiguous topographies to test whether they were formed by impact erosion or igneous modification.

Keywords


Classification Algorithms, Computational Intelligence, Impact Craters, Shape Parameters.

References





DOI: https://doi.org/10.18520/cs%2Fv115%2Fi7%2F1351-1358