Multi-Modal Deep Embedded Clustering (MM-DEC): A Novel Framework for Mineral Detection Using Hyperspectral Imagery
Identification of minerals through remote sensing imagery plays an important role in mineral exploration, especially when ground truth data or field work is not accessible. Hyperspectral imaging, which collects information from hundreds of narrow spectral bands, is a powerful tool that provides intr...
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2025-01-01
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| author | Priyanka Nair Devesh Kumar Srivastava Roheet Bhatnagar |
| author_facet | Priyanka Nair Devesh Kumar Srivastava Roheet Bhatnagar |
| author_sort | Priyanka Nair |
| collection | DOAJ |
| description | Identification of minerals through remote sensing imagery plays an important role in mineral exploration, especially when ground truth data or field work is not accessible. Hyperspectral imaging, which collects information from hundreds of narrow spectral bands, is a powerful tool that provides intricate surface information, but presents challenges due to its high-dimensionality and the complex structure of the data. To this end, we propose Multi-Modal Deep Embedded Clustering (MM-DEC) approach, an innovative unsupervised learning framework that integrates Convolutional Autoencoders(CAEs), Variational Autoencoders (VAEs), and Gray Level Co-occurrence Matrix (GLCM) based texture extraction that is able to exploit the spatial, spectral, and texture features of mineral in consideration We demonstrate the MM-DEC potential to identify hematite prospects in the mineralized Kiriburu area of Jharkhand, India using EO-1 Hyperion hyperspectral data. Preprocessing pipeline includes denoising using Machine Learning(ML) and statistical techniques, followed by major land cover classification based on spectral indices including Normalized Difference Vegetation Index (NDVI); Normalized Difference Water Index (NDWI) and Normalized Difference Soil Index (NDSI). By using the widely applied Pixel Purity Index (PPI), pure spectral pixels are extracted for refining and providing a high-confidence target point of hematite detection. Evaluation metrics based on cluster quality such as Silhouette Score, and Davies-Bouldin Index (DBI), show that MM-DEC is a competent model, as it is able to produce relatively compact and well-defined clusters as compared to conventional clustering methods. We achieved a similarity score of 98.69% in reference to the hematite spectral signatures. With accurate cluster labels, geographical coordinates, and spectral similarity scores, our method establishes a precedent in mineral extraction and resource planning based on remote sensing advancements. |
| format | Article |
| id | doaj-art-8e038dcd9b4d427bb1b18b4d4f8ea9ee |
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| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-8e038dcd9b4d427bb1b18b4d4f8ea9ee2025-08-20T02:26:15ZengIEEEIEEE Access2169-35362025-01-0113584075842410.1109/ACCESS.2025.355483710942353Multi-Modal Deep Embedded Clustering (MM-DEC): A Novel Framework for Mineral Detection Using Hyperspectral ImageryPriyanka Nair0https://orcid.org/0000-0001-8487-2778Devesh Kumar Srivastava1https://orcid.org/0000-0002-7400-8641Roheet Bhatnagar2https://orcid.org/0000-0001-9098-3378Department of Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, IndiaDepartment of Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, IndiaDepartment of Computer Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, IndiaIdentification of minerals through remote sensing imagery plays an important role in mineral exploration, especially when ground truth data or field work is not accessible. Hyperspectral imaging, which collects information from hundreds of narrow spectral bands, is a powerful tool that provides intricate surface information, but presents challenges due to its high-dimensionality and the complex structure of the data. To this end, we propose Multi-Modal Deep Embedded Clustering (MM-DEC) approach, an innovative unsupervised learning framework that integrates Convolutional Autoencoders(CAEs), Variational Autoencoders (VAEs), and Gray Level Co-occurrence Matrix (GLCM) based texture extraction that is able to exploit the spatial, spectral, and texture features of mineral in consideration We demonstrate the MM-DEC potential to identify hematite prospects in the mineralized Kiriburu area of Jharkhand, India using EO-1 Hyperion hyperspectral data. Preprocessing pipeline includes denoising using Machine Learning(ML) and statistical techniques, followed by major land cover classification based on spectral indices including Normalized Difference Vegetation Index (NDVI); Normalized Difference Water Index (NDWI) and Normalized Difference Soil Index (NDSI). By using the widely applied Pixel Purity Index (PPI), pure spectral pixels are extracted for refining and providing a high-confidence target point of hematite detection. Evaluation metrics based on cluster quality such as Silhouette Score, and Davies-Bouldin Index (DBI), show that MM-DEC is a competent model, as it is able to produce relatively compact and well-defined clusters as compared to conventional clustering methods. We achieved a similarity score of 98.69% in reference to the hematite spectral signatures. With accurate cluster labels, geographical coordinates, and spectral similarity scores, our method establishes a precedent in mineral extraction and resource planning based on remote sensing advancements.https://ieeexplore.ieee.org/document/10942353/Deep embedded clusteringCAEGLCMhematiteHyperionhyperspectral imaging |
| spellingShingle | Priyanka Nair Devesh Kumar Srivastava Roheet Bhatnagar Multi-Modal Deep Embedded Clustering (MM-DEC): A Novel Framework for Mineral Detection Using Hyperspectral Imagery IEEE Access Deep embedded clustering CAE GLCM hematite Hyperion hyperspectral imaging |
| title | Multi-Modal Deep Embedded Clustering (MM-DEC): A Novel Framework for Mineral Detection Using Hyperspectral Imagery |
| title_full | Multi-Modal Deep Embedded Clustering (MM-DEC): A Novel Framework for Mineral Detection Using Hyperspectral Imagery |
| title_fullStr | Multi-Modal Deep Embedded Clustering (MM-DEC): A Novel Framework for Mineral Detection Using Hyperspectral Imagery |
| title_full_unstemmed | Multi-Modal Deep Embedded Clustering (MM-DEC): A Novel Framework for Mineral Detection Using Hyperspectral Imagery |
| title_short | Multi-Modal Deep Embedded Clustering (MM-DEC): A Novel Framework for Mineral Detection Using Hyperspectral Imagery |
| title_sort | multi modal deep embedded clustering mm dec a novel framework for mineral detection using hyperspectral imagery |
| topic | Deep embedded clustering CAE GLCM hematite Hyperion hyperspectral imaging |
| url | https://ieeexplore.ieee.org/document/10942353/ |
| work_keys_str_mv | AT priyankanair multimodaldeepembeddedclusteringmmdecanovelframeworkformineraldetectionusinghyperspectralimagery AT deveshkumarsrivastava multimodaldeepembeddedclusteringmmdecanovelframeworkformineraldetectionusinghyperspectralimagery AT roheetbhatnagar multimodaldeepembeddedclusteringmmdecanovelframeworkformineraldetectionusinghyperspectralimagery |