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A method for mapping and monitoring of iron ore stopes based on hyperspectral remote sensing-ground data and a 3D deep neural network

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Abstract

This research explores a new hyperspectral remote sensing processing method that combines remote sensing and ground data, and builds a model based on a novel 3D convolutional neural network and fusion data. The method can monitor and map changes in iron ore stopes. First, we used an unmanned aerial vehicle-borne hyperspectral imager to take a hyperspectral image of the iron ore stope; second, collected iron ore samples and then used a ground-based spectrometer to measure the spectral data of these samples; third, combined the hyperspectral remote sensing data with the ground data and then proposed a data augmentation method. Fourth, based on the 3D convolutional neural network and deep residual network, an iron ore stope classification model is proposed. Finally, the model is applied to monitor and map iron ore stopes. The experimental results show that the proposed method is effective, and the overall accuracy is 99.62% for the five-class classification problem. The method provides a quick, accurate, and low-cost way to monitor iron ore stopes.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 52074064 and Grant 62173073; in part by the National Key Research and Development Program of China under 2020AAA0109200; in part by the Natural Science Foundation of Science and Technology Department of Liaoning Province, under 2021-BS-054; in part by the Fundamental Research Funds for the Central Universities, China under Grant N2204006, Grant N2104026, Grant N2018008, and Grant N2001002; in part by Liaoning Revitalization Talents Program under XLYC2008020; and in part by the Control, Automation in Production and Improvement of Technology Institute (CAPITI), Vietnam.

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Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China

    Dong Xiao

  2. Control, Automation in Production and Improvement of Technology Institute (CAPITI), Hanoi, 10000, Vietnam

    Quoc Huy Vu & Ba Tuan Le

  3. Key Laboratory of Intelligent Diagnosis and Safety for Metallurgical Industry, Liaoning Province, Northeastern University, Shenyang, 110819, China

    Dong Xiao

  4. Dong Thap University, Dong Thap, Cao Lãnh, 870000, Vietnam

    Thai Thuy Lam Ha

  5. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Ba Tuan Le

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  1. Dong Xiao
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Correspondence to Ba Tuan Le.

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Xiao, D., Vu, Q.H., Le, B.T. et al. A method for mapping and monitoring of iron ore stopes based on hyperspectral remote sensing-ground data and a 3D deep neural network. Neural Comput & Applic 35, 12221–12232 (2023). https://doi.org/10.1007/s00521-023-08353-y

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