Drill-Core Hyperspectral and Geochemical Data Integration in a Superpixel-Based Machine Learning Framework

Acosta, Isabel Cecilia Contreras; Khodadadzadeh, Mahdi; Tolosana‐Delgado, Raimon; Gloaguen, Richard

doi:10.1109/jstars.2020.3011221

Cited by 20 publications

(11 citation statements)

References 70 publications

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“…Examples of these are whole-rock geochemistry or high resolution mineralogical analysis providing identification and quantification of minerals. Such information can be used as a reference to generate training and test data sets [75], [76] as can be seen in Fig. 7.…”

Section: B Supervised Approachesmentioning

confidence: 99%

The Potential of Machine Learning for a More Responsible Sourcing of Critical Raw Materials

Ghamisi¹,

Shahi²,

Duan³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The digitization and automation of the raw material sector is required to attain the targets set by the Paris Agreements and support the sustainable development goals defined by the United Nations. While many aspects of the industry will be affected, most of the technological innovations will require smart imaging sensors. In this review, we assess the relevant recent developments of Machine Learning for the processing of imaging sensor data. We first describe the main imagers and the acquired data types as well as the platforms on which they can be installed. We briefly describe radiometric and geometric corrections as these procedures have been already described extensively in previous works. We focus on the description of innovative processing workflows and illustrate the most prominent approaches with examples. We also provide a list of available resources, codes, and libraries for researchers at different levels, from students to senior researchers, willing to explore novel methodologies on the challenging topics of raw material extraction, classification, and process automatization.

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Section: B Supervised Approachesmentioning

confidence: 99%

The Potential of Machine Learning for a More Responsible Sourcing of Critical Raw Materials

Ghamisi¹,

Shahi²,

Duan³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

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“…Qingyan Wang, Qi Zhang, Shouqiang Kang, Yujing Wang are with the School of Measurement-Control and Communication Engineering, Harbin mineral exploration [7], and other areas [8].…”

Section: Introductionmentioning

confidence: 99%

Graph-Based Semisupervised Learning With Weighted Features for Hyperspectral Remote Sensing Image Classification

Wang

Zhang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Graph neural network has excellent performance in obtaining the similarity relationship of samples, so it has been widely used in computer vision. But hyperspectral remote sensing image (HSI) has some problems such as data redundancy, noise, lack of labeled samples, and insufficient utilization of spatial information. These problems affect the accuracy of HSI classification using graph neural networks. To solve the above problems, this paper proposes graph-based semi-supervised learning with weighted features for HSI classification. The method proposed in this paper first uses the stacked autoencoder network to extract features, which is used to remove the redundancy of HSI data. Then, the similarity attenuation coefficient is introduced to improve the original feature weighting scheme. In this way, the contribution difference of adjacent pixels to the center pixel is reflected. Finally, to obtain more generalized spectral features, a shallow feature extraction mechanism is added to the stacked autoencoder network. And features that have good generalization can solve the problem of the lack of labeled samples. The experiment on three different types of datasets demonstrates that the proposed method in this paper can get better classification performance in the case of the scarcity of labeled samples than other classification methods.

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“…ML algorithms such as artificial neural network (ANN), support vector machine (SVM), regression tree (RT), and random forest (RF) are powerful data driven methods that are becoming extremely popular in such applications as the mapping of mineral prospectivity [26][27][28], mapping geochemical anomalies [29][30][31], geological mapping [32][33][34][35], drill-core mapping [36][37][38], and mineral phase segmentation for X-ray microcomputed tomography data [39][40][41].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

2021

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Machine learning is a subcategory of artificial intelligence, which aims to make computers capable of solving complex problems without being explicitly programmed. Availability of large datasets, development of effective algorithms, and access to the powerful computers have resulted in the unprecedented success of machine learning in recent years. This powerful tool has been employed in a plethora of science and engineering domains including mining and minerals industry. Considering the ever-increasing global demand for raw materials, complexities of the geological structure of ore deposits, and decreasing ore grade, high-quality and extensive mineralogical information is required. Comprehensive analyses of such invaluable information call for advanced and powerful techniques including machine learning. This paper presents a systematic review of the efforts that have been dedicated to the development of machine learning-based solutions for better utilizing mineralogical data in mining and mineral studies. To that end, we investigate the main reasons behind the superiority of machine learning in the relevant literature, machine learning algorithms that have been deployed, input data, concerned outputs, as well as the general trends in the subject area.

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Drill-Core Hyperspectral and Geochemical Data Integration in a Superpixel-Based Machine Learning Framework

Cited by 20 publications

References 70 publications

The Potential of Machine Learning for a More Responsible Sourcing of Critical Raw Materials

The Potential of Machine Learning for a More Responsible Sourcing of Critical Raw Materials

Graph-Based Semisupervised Learning With Weighted Features for Hyperspectral Remote Sensing Image Classification

A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

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