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Distinguishing different kinds of igneous rocks is difficult because of their subtle differences. Synthetic aperture radar (SAR) is sensitive to rock surface morphology, which can help a lot in classification. Dual-pol SAR data have the advantages of low cost, but few articles are using only dual-pol SAR data for igneous rock classification. In this study, we explored the performance of dual-pol SAR data in distinguishing granitoid, tuff, and syenite porphyry. Backscatter coefficients, polarization decomposition parameters, and texture features from gray-level co-occurrence matrix extracted by Sentinel-1 or PALSAR were classified using several machine learning algorithms. The results are as follows. First, the texture information has greater potential for igneous rock classification, but the polarization decomposition parameters contribute less. Second, after comparing machine learning algorithms, AdaBoost algorithm has the highest overall accuracy for either C-band or L-band SAR data. C-band SAR data provide better classification results than L-band. Finally, tuff is the easiest igneous rock to be successfully classified, and L-band dual-pol SAR data have advantages in the discrimination of syenite porphyry. This study outlines the effectiveness of dual-pol SAR data for igneous rock classification, which will help to select SAR data of appropriate wavelengths for specific types of lithology discrimination.

Lithology classification of igneous rocks using C-band and L-band dual-polarization SAR data