Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/36669
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Advanced Mineral Deposit Mapping via Deep Learning and SVM Integration With Remote Sensing Imaging Data
Author(s): Jan, Nazir
Minallah, Nasru
Sher, Madiha
Wasim, Muhammad
Khan, Shahid
Al‐Rasheed, Amal
Ali, Hazrat
Contact Email: ali.hazrat@stir.ac.uk
Issue Date: 14-Oct-2024
Date Deposited: 5-Nov-2024
Citation: Jan N, Minallah N, Sher M, Wasim M, Khan S, Al‐Rasheed A & Ali H (2024) Advanced Mineral Deposit Mapping via Deep Learning and SVM Integration With Remote Sensing Imaging Data. <i>Engineering Reports</i>, 7 (1). https://onlinelibrary.wiley.com/doi/full/10.1002/eng2.13031; https://doi.org/10.1002/eng2.13031
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.
URL: https://onlinelibrary.wiley.com/doi/full/10.1002/eng2.13031
DOI Link: 10.1002/eng2.13031

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