A review of machine learning in processing remote sensing data for mineral exploration

Shirmard, Hodjat; Farahbakhsh, Ehsan; Müller, R. Dietmar; Chandra, Rohitash

doi:10.1016/j.rse.2021.112750

Cited by 203 publications

(66 citation statements)

References 174 publications

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“…e experimentation was performed on Pavia University (PU), Pavia Center (PC), and Kennedy Space Center (KSC) datasets, and the results show that the proposed methods achieve better accuracy with low computational cost [58]. It has many applications in different fields of life like speech recognition systems, search engines, and other AI-based applications like robotics [59].…”

Section: Machine Learningmentioning

confidence: 99%

Remote Sensing Image Classification: A Comprehensive Review and Applications

Mehmood

Shahzad

Zafar

et al. 2022

Mathematical Problems in Engineering

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Remote sensing is mainly used to investigate sites of dams, bridges, and pipelines to locate construction materials and provide detailed geographic information. In remote sensing image analysis, the images captured through satellite and drones are used to observe surface of the Earth. The main aim of any image classification-based system is to assign semantic labels to captured images, and consequently, using these labels, images can be arranged in a semantic order. The semantic arrangement of images is used in various domains of digital image processing and computer vision such as remote sensing, image retrieval, object recognition, image annotation, scene analysis, content-based image analysis, and video analysis. The earlier approaches for remote sensing image analysis are based on low-level and mid-level feature extraction and representation. These techniques have shown good performance by using different feature combinations and machine learning approaches. These earlier approaches have used small-scale image dataset. The recent trends for remote sensing image analysis are shifted to the use of deep learning model. Various hybrid approaches of deep learning have shown much better results than the use of a single deep learning model. In this review article, a detailed overview of the past trends is presented, based on low-level and mid-level feature representation using traditional machine learning concepts. A summary of publicly available image benchmarks for remote sensing image analysis is also presented. A detailed summary is presented at the end of each section. An overview regarding the current trends of deep learning models is presented along with a detailed comparison of various hybrid approaches based on recent trends. The performance evaluation metrics are also discussed. This review article provides a detailed knowledge related to the existing trends in remote sensing image classification and possible future research directions.

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Section: Machine Learningmentioning

confidence: 99%

Remote Sensing Image Classification: A Comprehensive Review and Applications

Mehmood

Shahzad

Zafar

et al. 2022

Mathematical Problems in Engineering

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“…One of the most challenging geological applications using remote-sensing-based satellite data is mapping lithological features, especially for large and geologically complex areas that require high-precision lithological maps [1,2]. Furthermore, the limited availability of the spatial and spectral quality of open access image data (e.g., Landsat-5 TM, Landsat-7 ETM+ (enhanced thematic mapper plus), Landsat-8 OLI (operational land imager), ASTER and Sentinel-2) is widely utilized to extract lithological [3][4][5], mineral [6,7] and structural information [8][9][10]. The majority of studies investigating the potential of remote sensing for geological mapping [4,6,[11][12][13] have been conducted over relatively small geographical areas using individual machine learning algorithms (MLAs).…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the limited availability of the spatial and spectral quality of open access image data (e.g., Landsat-5 TM, Landsat-7 ETM+ (enhanced thematic mapper plus), Landsat-8 OLI (operational land imager), ASTER and Sentinel-2) is widely utilized to extract lithological [3][4][5], mineral [6,7] and structural information [8][9][10]. The majority of studies investigating the potential of remote sensing for geological mapping [4,6,[11][12][13] have been conducted over relatively small geographical areas using individual machine learning algorithms (MLAs). Therefore, an innovative approach, based on several factors, is implemented in this research to overcome the aforementioned issues.…”

Section: Introductionmentioning

confidence: 99%

Improved Lithological Map of Large Complex Semi-Arid Regions Using Spectral and Textural Datasets within Google Earth Engine and Fused Machine Learning Multi-Classifiers

et al. 2022

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In this era of free and open-access satellite and spatial data, modern innovations in cloud computing and machine-learning algorithms (MLAs) are transforming how Earth-observation (EO) datasets are utilized for geological mapping. This study aims to exploit the potentialities of the Google Earth Engine (GEE) cloud platform using powerful MLAs. The proposed method is implemented in three steps: (1) Based on GEE and Sentinel 2A imagery (spectral and textural features), that cover 1283 km2 area, a variety of lithological maps are generated using five supervised classifiers (random forest (RF), support vector machine (SVM), classification and regression tree (CART), minimum distance (MD), naïve Bayes (NB)); (2) the accuracy assessments for each class are performed, by estimating overall accuracy (OA) and kappa coefficient (K) for each classifier; (3) finally, the fusion of classification maps is performed using Dempster–Shafer Theory (DST) for mapping lithological units of the northern part of the complex Paleozoic massif of Rehamna, a large semi-arid region located in the SW of the western Moroccan Meseta. The results were quantitatively compared with existing geological maps, enhanced color composite and validated by field survey investigation. In comparison of individual classifiers, the SVM yields better accuracy of nearly 88%, which was 12% higher than the RF MLA; otherwise, the parametric MLAs produce the weakest lithological maps among other classifiers, with a lower OA of approximately 67%, 54% and 52% for CART, MD and NB, respectively. Noticeably, the highest OA value of 96% is achieved for the proposed approach. Therefore, we conclude that this method allows geoscientists to update previous geological maps and rapidly produce more precise lithological maps, especially for hard-to-reach regions.

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“…Nowadays, machine learning applications in remote sensing are a trending topic that is gradually gaining attention from industry experts, scientists, and researchers (Shirmard et al, 2022) due to the availability of modern techniques in remote sensing, abundant data availability, and, when compared to traditional geological mapping techniques, spatial remote sensing has indisputable benefits in terms of covered area, speed and cost (Zerrouki et al, 2019). Remote sensing technology benefits from machine learning techniques as they enable better resource management, more accurate environmental forecasts, and the discovery of novel insights from large data sets.While there are many benefits to using machine learning in remote sensing, there are some challenges as well.…”

Section: Introductionmentioning

confidence: 99%

Limestone mining area mapping and assessment at the Cement Industrial area using Spectral Index and Machine Learning Algorithms

C¹

2022

Preprint

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Limestone mining contributes significantly to the Gross Domestic Product (GDP) of any country but it comes with some adverse impacts on the environment. The objective of this study is to determine the spatial distribution area of limestone mines using remote sensing, spectral index, and machine learning algorithms and compare their area estimation with industrial data for the financial year 2019. The study area includes a limestone mining area, of approximately 2226.16 ha with an excavation mining area of 487.10 ha at the Yerraguntla cement industrial region, YSR Kadapa district, Andhra Pradesh, India. In this study, we used the normalized vegetation index (NDVI), iterative self organizing data analysis technique (ISODATA), K-Nearest Neighbors (KNN), and random forest (RF) algorithms to analyze multispectral Sentinel-2A satellite data in QGIS 3.18 software tool. The RF classifier estimated a limestone mine area of 379.57 ha with best user accuracy (UA) 97.25% and producer accuracy (PA) 99.18% with a kappa coefficient value of 0.957. The mine area estimated 417.47 ha with UA of 98.99% and PA of 99.10% and kappa value 0.947 of the KNN method, The NDVI method estimated 469.92 ha with UA of 93.63% and PA of 92.04% and kappa value 0.685. This study confirmed that RF classifier well performed in classification with overall accuracy (OA) of 95.79% than KNN (OA of 94.78%), and NDVI (OA of 79.84%) classifiers, and ISODATA is poor in classification with OA of 64.16%. This study supports environmentally sustainable decisions, eco-friendly mine planning and monitoring for limestone mine owners and environmental engineers.

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A review of machine learning in processing remote sensing data for mineral exploration

Cited by 203 publications

References 174 publications

Remote Sensing Image Classification: A Comprehensive Review and Applications

Remote Sensing Image Classification: A Comprehensive Review and Applications

Improved Lithological Map of Large Complex Semi-Arid Regions Using Spectral and Textural Datasets within Google Earth Engine and Fused Machine Learning Multi-Classifiers

Limestone mining area mapping and assessment at the Cement Industrial area using Spectral Index and Machine Learning Algorithms

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