Title :
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
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;
Conference_Titel :
Cognitive Informatics (ICCI), 2010 9th IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-8041-8
DOI :
10.1109/COGINF.2010.5599824
