MULTI-LABEL CLASSIFICATION FOR DRILL-CORE HYPERSPECTRAL MINERAL MAPPING
| Autor: | I. C. Contreras, Richard Gloaguen, Mahdi Khodadadzadeh |
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| Rok vydání: | 2020 |
| Předmět: |
lcsh:Applied optics. Photonics
classifier chains 010504 meteorology & atmospheric sciences Scanning electron microscope Computer science 0211 other engineering and technologies 02 engineering and technology lcsh:Technology 01 natural sciences drill-core hyperspectral data mineral liberation analysis multi-label classification 021101 geological & geomatics engineering 0105 earth and related environmental sciences Multi-label classification Training set Pixel lcsh:T business.industry lcsh:TA1501-1820 Hyperspectral imaging Pattern recognition Mineral mapping VNIR Random forest ComputingMethodologies_PATTERNRECOGNITION machine learning lcsh:TA1-2040 Artificial intelligence Classifier chains lcsh:Engineering (General). Civil engineering (General) business Classifier (UML) random forest |
| Zdroj: | International Society for Photogrammetry and Remote Sensing (ISPRS), 31.08.-02.09.2020, Nice, FranceISPRS Archives, 43(B3),pp. 383-388 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIII-B3-2020, Pp 383-388 (2020) |
| ISSN: | 2194-9034 |
| Popis: | A multi-label classification concept is introduced for the mineral mapping task in drill-core hyperspectral data analysis. As opposed to traditional classification methods, this approach has the advantage of considering the different mineral mixtures present in each pixel. For the multi-label classification, the well-known Classifier Chain method (CC) is implemented using the Random Forest (RF) algorithm as the base classifier. High-resolution mineralogical data obtained from Scanning Electron Microscopy (SEM) instrument equipped with the Mineral Liberation Analysis (MLA) software are used for generating the training data set. The drill-core hyperspectral data used in this paper cover the visible-near infrared (VNIR) and the short-wave infrared (SWIR) range of the electromagnetic spectrum. The quantitative and qualitative analysis of the obtained results shows that the multi-label classification approach provides meaningful and descriptive mineral maps and outperforms the single-label RF classification for the mineral mapping task. |
| Databáze: | OpenAIRE |
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