Lithology intelligent identification using support vector machine and adaptive cellular automata in multispectral remote sensing image

Xianmin W. Wang; Ruiqing Niu; Ke Wu

doi:10.1117/1.3598315

1 July 2011 Lithology intelligent identification using support vector machine and adaptive cellular automata in multispectral remote sensing image

Xianmin W. Wang, Ruiqing Niu, Ke Wu

Author Affiliations +

Optical Engineering, Vol. 50, Issue 7, 076201 (July 2011). https://doi.org/10.1117/1.3598315

Abstract

Remote sensing provides a new idea and an advanced method for lithology identification, but lithology identification by remote sensing is quite difficult because 1. the disciplines of lithology identification in a concrete region are often quite different from the experts' experience; 2. In the regions with flourishing vegetation, lithology information is poor, so it is very difficult to identify the lithologies by remote sensing images...An intelligent method proposed in this paper for lithology identification based on support vector machine (SVM) and adaptive cellular automata (ACA) is expected to solve the above problems. The method adopted Landsat-7 ETM+ images and 1:50000 geological map as the data origins. It first derived the lithology identification factors on three aspects: 1. spectra, 2. texture and 3. Vegetation cover. Second, it plied the remote sensing images with the geological map and established the SVM to obtain the transition rules according to the factor values of the samples. Finally, it established an ACA model to intelligently identify the lithologies according to the transition and neighborhood rules. In this paper an ACA model is proposed and compared with the traditional one.

©(2011) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Xianmin W. Wang, Ruiqing Niu, and Ke Wu "Lithology intelligent identification using support vector machine and adaptive cellular automata in multispectral remote sensing image," Optical Engineering 50(7), 076201 (1 July 2011). https://doi.org/10.1117/1.3598315

Published: 1 July 2011

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