Version 1
: Received: 22 April 2024 / Approved: 22 April 2024 / Online: 23 April 2024 (09:44:59 CEST)
How to cite:
Abubakar, J.; Zhang, Z.; Cheng, Z.; Yao, F.; BIO SIDI D. BOUKO, A. Advancing Skarn Iron Ore Detection Through Multispectral Image Fusion and 3D Convolutional Neural Networks (3D-CNN). Preprints2024, 2024041477. https://doi.org/10.20944/preprints202404.1477.v1
Abubakar, J.; Zhang, Z.; Cheng, Z.; Yao, F.; BIO SIDI D. BOUKO, A. Advancing Skarn Iron Ore Detection Through Multispectral Image Fusion and 3D Convolutional Neural Networks (3D-CNN). Preprints 2024, 2024041477. https://doi.org/10.20944/preprints202404.1477.v1
Abubakar, J.; Zhang, Z.; Cheng, Z.; Yao, F.; BIO SIDI D. BOUKO, A. Advancing Skarn Iron Ore Detection Through Multispectral Image Fusion and 3D Convolutional Neural Networks (3D-CNN). Preprints2024, 2024041477. https://doi.org/10.20944/preprints202404.1477.v1
APA Style
Abubakar, J., Zhang, Z., Cheng, Z., Yao, F., & BIO SIDI D. BOUKO, A. (2024). Advancing Skarn Iron Ore Detection Through Multispectral Image Fusion and 3D Convolutional Neural Networks (3D-CNN). Preprints. https://doi.org/10.20944/preprints202404.1477.v1
Chicago/Turabian Style
Abubakar, J., Fojun Yao and Abdoul-Aziz BIO SIDI D. BOUKO. 2024 "Advancing Skarn Iron Ore Detection Through Multispectral Image Fusion and 3D Convolutional Neural Networks (3D-CNN)" Preprints. https://doi.org/10.20944/preprints202404.1477.v1
Abstract
This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image processing methodologies. Leveraging the capabilities of Aster image, Band Ratio (BR) images, and Principal Component Analysis (PCA), alongside the power of 3D Convolutional Neural Networks (3D-CNN), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed method employs a specific 3D-CNN architecture, accepting input as a 7×7×C image patch, where C represents the combined number of selected Aster image bands, Principal Component (PC) bands, and computed BR images. To evaluate the accuracy of the proposed method, five distinct image bands combinations, including the proposed bands combination, were tested and evaluated based on the overall accuracy (OA), average accuracy (AA), and kappa coefficient. The results demonstrated that while the incorporation of BR images alongside Aster bands initially seemed promising, it introduced significant confusion in certain classifications, leading to unexpected mis-classification rates. Surprisingly, utilizing solely Aster bands as input parameters yielded higher accuracy rates (OA= 93.13%, AA = 91.96%, Kappa = 90.91%) compared to scenarios involving the integration with band ratios (OA = 87.02%, AA = 79.15, and Kappa = 82.60%) or the integration of BR images to PC bands (OA= 87.78%, AA = 82.39%, Kappa = 83.81%). However, the amalgamation of Aster bands with selected PC bands showed slight improvements in accuracy (OA= 94.65%, AA = 92.93%, Kappa = 93.45%), although challenges in accurately classifying certain features persisted. Ultimately, the proposed combination of Aster bands, PC bands, and BR images (proposed bands combination) presented the most visually appealing and statistically accurate results (OA = 96.95%, AA= 94.87%, and Kappa = 95.93%), effectively addressing misclassifications observed in the other combinations. These findings underscore the synergistic contributions of each of the Aster bands, PC bands, and BR images, with Aster bands proving pivotal for optimal skarn classification, PC bands enhancing intrusions classification accuracy, and BR images strengthening wall rock classification accuracy. In conclusion, the proposed combination of input image bands emerges as a robust and comprehensive methodology, demonstrating unparalleled accuracy in the remote sensing detection of skarn iron minerals.
Keywords
Aster bands; Band ratio (BR) images; Principal Component (PC) bands; 3D-CNN
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
