Computer vision-based framework for extracting geological lineaments from optical remote sensing data

TL;DR

This paper introduces a computer vision framework combining edge detection and line extraction techniques to identify geological lineaments from optical remote sensing data, aiding mineral exploration.

Contribution

The framework integrates various computer vision methods for enhanced extraction of geological features from satellite imagery, validated on Landsat 8 data with improved correlation to geological maps.

Findings

Best correlation with geological maps using noise reduction and Laplacian filter.

Directional filters show stronger correlation with manual interpretation and mineralization sites.

Framework applicable for mineral prospectivity mapping in optical remote sensing data.

Abstract

The extraction of geological lineaments from digital satellite data is a fundamental application in remote sensing. The location of geological lineaments such as faults and dykes are of interest for a range of applications, particularly because of their association with hydrothermal mineralization. Although a wide range of applications have utilized computer vision techniques, a standard workflow for application of these techniques to mineral exploration is lacking. We present a framework for extracting geological lineaments using computer vision techniques which is a combination of edge detection and line extraction algorithms for extracting geological lineaments using optical remote sensing data. It features ancillary computer vision techniques for reducing data dimensionality, removing noise and enhancing the expression of lineaments. We test the proposed framework on Landsat 8 data of a mineral-rich portion of the Gascoyne Province in Western Australia using different dimension reduction techniques and convolutional filters. To validate the results, the extracted lineaments are compared to our manual photointerpretation and geologically mapped structures by the Geological Survey of Western Australia (GSWA). The results show that the best correlation between our extracted geological lineaments and the GSWA geological lineament map is achieved by applying a minimum noise fraction transformation and a Laplacian filter. Application of a directional filter instead shows a stronger correlation with the output of our manual photointerpretation and known sites of hydrothermal mineralization. Hence, our framework using either filter can be used for mineral prospectivity mapping in other regions where faults are exposed and observable in optical remote sensing data.