Lithological classification and analysis based on random forest and multiple features: a case study in the Qulong copper deposit, China

Abstract

.Surface cover diversity and the complexity of geological structures can seriously impact the accuracy of mineral mapping. To address this issue, we propose a method for lithological classification and analysis based on random forest (RF) and multiple features. Feature vectors, including spectral, polarization, texture, and terrain features, are constructed to provide multidimensional information. Subsequently, these feature vectors are screened based on their discriminative properties for different lithologies to reduce feature redundancy. Finally, the results of lithological classification can be obtained using the RF algorithm based on the selected features. In the experiments conducted in the Qulong copper deposit area, data from Sentinel-1A, Sentinel-2A, and Terra satellites were used to extract multidimensional features. After calculating the Bhattacharyya distance and analyzing the probability density distribution, 17 features selected were input into the RF classifier, achieving an accuracy of 88.83% in lithological classification. This represents a 7.5% improvement compared to exclusively relying on spectral features, and suggests that the proposed method of combining spectral, polarization, texture, and terrain features provides new possibilities for improving the accuracy of field lithological classification.

Automatic lithology identification in meteorite impact craters using machine learning algorithms

“…The findings presented in this study support the applicability of tree-based algorithms for lithology classification, aligning with earlier research 14 , 31 , 32 that identifies RF as a favorable ML algorithm for this task. This convergence of evidence further reinforces RF's superior performance in the accurate classification of lithologies.…”

Granite Extraction Based on the SDGSAT-1 Satellite Thermal Infrared Spectrometer Imagery

“…On the other hand, acquiring high-spectral and high-resolution data involves significant costs, and these datasets often have limited coverage. (2) Complex processes: The process of manual interpretation, feature selection, and model training is labor-intensive and time-consuming, making it challenging to achieve rapid extraction of granitic lithology [ 19 ]. There is an urgent need for the development of a fast and efficient method for granite lithology extraction.…”