A Comparative Study of Convolutional Neural Networks and Conventional Machine Learning Models for Lithological Mapping Using Remote Sensing Data

Rohitash Chandra,Elnaz Heidari,Biswajeet Pradhan,Dietmar Müller,Ehsan Farahbakhsh,Hojat Shirmard,Amin Beiranvand Pour

doi:10.3390/rs14040819

Abstract

Lithological mapping is a critical aspect of geological mapping that can be useful in studying the mineralization potential of a region and has implications for mineral prospectivity mapping. This is a challenging task if performed manually, particularly in highly remote areas that require a large number of participants and resources. The combination of machine learning (ML) methods and remote sensing data can provide a quick, low-cost, and accurate approach for mapping lithological units. This study used deep learning via convolutional neural networks and conventional ML methods involving support vector machines and multilayer perceptron to map lithological units of a mineral-rich area in the southeast of Iran. Moreover, we used and compared the efficiency of three different types of multispectral remote-sensing data, including Landsat 8 operational land imager (OLI), advanced spaceborne thermal emission and reflection radiometer (ASTER), and Sentinel-2. The results show that CNNs and conventional ML methods effectively use the respective remote-sensing data in generating an accurate lithological map of the study area. However, the combination of CNNs and ASTER data provides the best performance and the highest accuracy and adaptability with field observations and laboratory analysis results so that almost all the test data are predicted correctly. The framework proposed in this study can be helpful for exploration geologists to create accurate lithological maps in other regions by using various remote-sensing data at a low cost.

Highlights

Geological maps offer fundamental knowledge that can be used in a number of fields, such as landslide and earthquake hazard assessment, infrastructure planning, and the discovery of groundwater and deep Earth resources [1,2,3,4]
Based on the prediction accuracy obtained by the receiver operating characteristics (ROCs) curves and the thin-section analysis of the rock samples collected from the study area, the combination of convolutional neural networks (CNNs) and ASTER data provided the most accurate lithological map for our study area
We showed the efficiency of applying machine- and deep-learning techniques on remote-sensing data and observed that the CNN and ASTER data combination provides the most accurate lithological map of the study area based on the ROC curves, and the test data were successfully predicted

Summary

Introduction

Geological maps offer fundamental knowledge that can be used in a number of fields, such as landslide and earthquake hazard assessment, infrastructure planning, and the discovery of groundwater and deep Earth resources [1,2,3,4]. In the past few decades, in addition to ground-based field observations, remote-sensing data have been used in geological mapping and mineral prospective mapping [5,6,7]. Due to recent advancements in multi- and hyperspectral remote-sensing sensors, there is an urgent need to further develop the use of satellite imagery for mapping geological features [12]. Different multiand hyperspectral remote-sensing data have been extensively and effectively used for the discovery of ore deposits, for identifying a variety of alteration zones associated with metallic mineralizations, but rarely for discriminating lithological units [14,15,16]

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