Advanced Mineral Deposit Mapping via Deep Learning and SVM Integration With Remote Sensing Imaging Data
ABSTRACT Automating mineral delineation and rock type analysis using remote sensing imaging data is a critical application of machine learning. Traditional machine learning methods often struggle with accuracy and precise map generation. This study aims to enhance performance through a refined deep...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Engineering Reports |
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| Online Access: | https://doi.org/10.1002/eng2.13031 |
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| author | Nazir Jan Nasru Minallah Madiha Sher Muhammad Wasim Shahid Khan Amal Al‐Rasheed Hazrat Ali |
| author_facet | Nazir Jan Nasru Minallah Madiha Sher Muhammad Wasim Shahid Khan Amal Al‐Rasheed Hazrat Ali |
| author_sort | Nazir Jan |
| collection | DOAJ |
| description | ABSTRACT Automating mineral delineation and rock type analysis using remote sensing imaging data is a critical application of machine learning. Traditional machine learning methods often struggle with accuracy and precise map generation. This study aims to enhance performance through a refined deep learning model. In this work, we present a deep learning pipeline to map the mineral deposits in the study area. Initially, we apply a deep convolutional neural network (CNN) to a specialized mineral dataset to map mineral deposits within the study area. Subsequently, we build a hybrid model combining deep CNN layers with a support vector machine (SVM). This merger significantly improves classification accuracy from an initial 92.7% to 95.3%. In our approach, CNN layers function as feature extractors while the SVM serves as the classification model. Moreover, we conduct an evaluation of the SVM using polynomial kernels of degrees 3, 6, 9, and 12. The results indicate that the SVM with a degree of 12 achieved the highest classification accuracy, followed by degrees 9, 6, and 3. Experimental results demonstrate the effectiveness of our proposed method for classifying remote sensing imaging data, showcasing its potential for advancing mineral delineation and rock type analysis. |
| format | Article |
| id | doaj-art-0bf9aa7821294333a2e3fe8bbc1975a0 |
| institution | Kabale University |
| issn | 2577-8196 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-0bf9aa7821294333a2e3fe8bbc1975a02025-01-31T00:22:48ZengWileyEngineering Reports2577-81962025-01-0171n/an/a10.1002/eng2.13031Advanced Mineral Deposit Mapping via Deep Learning and SVM Integration With Remote Sensing Imaging DataNazir Jan0Nasru Minallah1Madiha Sher2Muhammad Wasim3Shahid Khan4Amal Al‐Rasheed5Hazrat Ali6Department of Computer Systems Engineering University of Engineering and Technology Peshawar Peshawar PakistanDepartment of Computer Systems Engineering University of Engineering and Technology Peshawar Peshawar PakistanDepartment of Computer Systems Engineering University of Engineering and Technology Peshawar Peshawar PakistanDepartment of Computer Science City University of Science and Information Technology Peshawar Peshawar PakistanDepartment of Electrical and Computer Engineering COMSATS University Islamabad, Abbottabad Campus Abbottabad PakistanDepartment of Information Systems College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University Riyadh Saudi ArabiaComputing Science and Mathematics University of Stirling Stirling UKABSTRACT Automating mineral delineation and rock type analysis using remote sensing imaging data is a critical application of machine learning. Traditional machine learning methods often struggle with accuracy and precise map generation. This study aims to enhance performance through a refined deep learning model. In this work, we present a deep learning pipeline to map the mineral deposits in the study area. Initially, we apply a deep convolutional neural network (CNN) to a specialized mineral dataset to map mineral deposits within the study area. Subsequently, we build a hybrid model combining deep CNN layers with a support vector machine (SVM). This merger significantly improves classification accuracy from an initial 92.7% to 95.3%. In our approach, CNN layers function as feature extractors while the SVM serves as the classification model. Moreover, we conduct an evaluation of the SVM using polynomial kernels of degrees 3, 6, 9, and 12. The results indicate that the SVM with a degree of 12 achieved the highest classification accuracy, followed by degrees 9, 6, and 3. Experimental results demonstrate the effectiveness of our proposed method for classifying remote sensing imaging data, showcasing its potential for advancing mineral delineation and rock type analysis.https://doi.org/10.1002/eng2.13031carbonated mineralsdeep learningimage processingremote sensingSentinel2support vector machine |
| spellingShingle | Nazir Jan Nasru Minallah Madiha Sher Muhammad Wasim Shahid Khan Amal Al‐Rasheed Hazrat Ali Advanced Mineral Deposit Mapping via Deep Learning and SVM Integration With Remote Sensing Imaging Data Engineering Reports carbonated minerals deep learning image processing remote sensing Sentinel2 support vector machine |
| title | Advanced Mineral Deposit Mapping via Deep Learning and SVM Integration With Remote Sensing Imaging Data |
| title_full | Advanced Mineral Deposit Mapping via Deep Learning and SVM Integration With Remote Sensing Imaging Data |
| title_fullStr | Advanced Mineral Deposit Mapping via Deep Learning and SVM Integration With Remote Sensing Imaging Data |
| title_full_unstemmed | Advanced Mineral Deposit Mapping via Deep Learning and SVM Integration With Remote Sensing Imaging Data |
| title_short | Advanced Mineral Deposit Mapping via Deep Learning and SVM Integration With Remote Sensing Imaging Data |
| title_sort | advanced mineral deposit mapping via deep learning and svm integration with remote sensing imaging data |
| topic | carbonated minerals deep learning image processing remote sensing Sentinel2 support vector machine |
| url | https://doi.org/10.1002/eng2.13031 |
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