Texture analysis for automated classification of geologic structures

Vivek Shankar, Jeffrey J. Rodriguez, Mark E. Gettings

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Texture present in aeromagnetic anomaly images offers an abundance of useful geological information for discriminating between rock types, but current analysis of such images still relies on tedious, human interpretation. This study is believed to be the first effort to quantitatively assess the performance of texture-based digital image analysis for this geophysical exploration application. We computed several texture measures and determined the best subset using automated feature selection techniques. Pattern classification experiments measured the ability of various texture measures to automatically predict rock types. The classification accuracy was significantly better than a priori probability and prior weights-of-evidence results. The accuracy rates and choice of texture measures that minimize the error rate are reported.

Original languageEnglish (US)
Title of host publication7th IEEE Southwest Symposium on Image Analysis and Interpretation
Pages81-85
Number of pages5
StatePublished - Nov 21 2006
Event7th IEEE Southwest Symposium on Image Analysis and Interpretation - Denver, CO, United States
Duration: Mar 26 2006Mar 28 2006

Publication series

NameProceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation
Volume2006

Other

Other7th IEEE Southwest Symposium on Image Analysis and Interpretation
CountryUnited States
CityDenver, CO
Period3/26/063/28/06

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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    Shankar, V., Rodriguez, J. J., & Gettings, M. E. (2006). Texture analysis for automated classification of geologic structures. In 7th IEEE Southwest Symposium on Image Analysis and Interpretation (pp. 81-85). [1633727] (Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation; Vol. 2006).