Name
Abstract | ||
|---|---|---|
Hyperspectral data are increasingly being used to map minerals in drill core samples allowing a non-invasive and non-destructive characterization of the mineral assemblages, and therefore, the mineralogical composition of a system, its variability, and structural features. The analysis of drill core hyperspectral data is traditionally carried out by a visual interpretation of the spectra and a comparison with reference libraries using spectral similarity measures. Although this approach produces good results it is time-consuming and subjective. In this work, we introduce, for the first time, an innovative automatic mineral mapping technique for drill core hyperspectral data by using a machine learning approach. More specifically, we propose to exploit detailed information coming from the Scanning Electron Microscopy (SEM)-based Mineral Liberation Analysis (MLA) to train a supervised classifier. For the extraction of input features, a traditional technique is explored, i.e., Principal Component Analysis (PCA). For the classification step, we suggest to use Random Forest (RF) because of its significant performance when there are few training samples available. Experimental results conducted on a VNIR-SWIR drill core hyperspectral dataset, show accurate classification results. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/WHISPERS.2018.8747022 | 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) |
Keywords | Field | DocType |
Drill core hyperspectral data,Mineral Liberation Analysis,Random Forest,mineral mapping | Computer science,Visual interpretation,Hyperspectral imaging,Artificial intelligence,Classifier (linguistics),Drill,Random forest,Machine learning,Principal component analysis | Conference |
ISSN | ISBN | Citations |
2158-6268 | 978-1-7281-1582-5 | 1 |
PageRank | References | Authors |
0.48 | 2 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cecilia Contreras | 1 | 1 | 1.16 |
| Mahdi Khodadadzadeh | 2 | 68 | 9.12 |
| Laura Tusa | 3 | 1 | 1.16 |
| Pedram Ghamisi | 4 | 827 | 46.28 |
| Richard Gloaguen | 5 | 133 | 32.68 |