Texture analysis for automated classification of geologic structures
By: V. Shankar, J.J. Rodriguez, and M.E. Gettings
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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. ?? 2006 IEEE.
| Publication type | Conference Paper |
|---|---|
| Publication Subtype | Conference Paper |
| Title | Texture analysis for automated classification of geologic structures |
| ISBN | 1424400694; 9781424400690 |
| Volume | 2006 |
| Year Published | 2006 |
| Language | English |
| Larger Work Title | Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation |
| First page | 81 |
| Last page | 85 |
| Conference Title | 7th IEEE Southwest Symposium on Image Analysis and Interpretation |
| Conference Location | Denver, CO |
| Conference Date | 26 March 2006 through 28 March 2006 |
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