Effective classification for crater detection: A case study on Mars

Author

Wang, Jue ; Ding, Wei ; Fradkin, Barry ; Pham, Cuong H. ; Sherman, Peter ; Tran, Binh D. ; Wang, Dawei ; Yang, Yun ; Stepinski, Tomasz F.

Author_Institution

Univ. of Massachusetts Boston, Boston, MA, USA

fYear

2010

fDate

7-9 July 2010

Firstpage

688

Lastpage

695

Abstract

Craters are important geographical features caused by the impacts of meteoroids. Craters have been widely studied because they contain crucial information about the age and geologic formations of planets. This paper discusses an automated crater-detection framework using knowledge discovery and data mining (KDD) process including sampling, feature selection and creation, and supervised learning methods. The framework is evaluated on a real world case study of Mars crater detection. Compared with the existing method, the F detection rate is improved from 0.613 to 0.772 using a Martial site of area 451,562,500 m².

Keywords

Mars; astronomical techniques; astronomy computing; computer vision; data mining; feature extraction; learning (artificial intelligence); meteorite craters; pattern classification; planetary surfaces; Mars crater detection; automated crater detection framework; crater classification; data mining; feature selection; knowledge discovery; meteoroid impacts; planetary age; planetary geologic formation; sampling; supervised learning methods; Cognitive informatics; Crater Detection Algorithm; Data Preprocessing; Data Sampling; Mars; Patten Classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Cognitive Informatics (ICCI), 2010 9th IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-8041-8

Type

conf

DOI

10.1109/COGINF.2010.5599824

Filename

5599824