Towards automatic lithological classification from remote sensing data using support vector machines

Yu, Le; Porwal, Alok; Holden, Eun‐Jung; Dentith, Mike

doi:10.1016/j.cageo.2011.11.019

Cited by 181 publications

(82 citation statements)

References 27 publications

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“…The SVM is one of the suitable techniques, which has been received growing interest within the remote sensing community [86,87]. It has been successfully used for lithological mapping [88][89][90][91][92].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

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Improving Lithological Mapping by SVM Classification of Spectral and Morphological Features: The Discovery of a New Chromite Body in the Mawat Ophiolite Complex (Kurdistan, NE Iraq)

2014

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Abstract:The mineral ore potential of many mountainous regions of the world, like the Kurdistan region of Iraq, remains unexplored. For logistical and sometimes political reasons, these areas are difficult to map using traditional methods. We highlight the improvement in remote sensing geological mapping that arises from the integration of geomorphic features in classifications. The Mawat Ophiolite Complex (MOC) is located in the NE of Iraq and is known for its mineral deposits. The aims of this study are: (I) to refine the existing lithological map of the MOC; (II) to identify the best discriminatory datasets for lithological classification, including geomorphic features and textures; and (III) to identify potential locations with high concentrations of chromite. We performed a Support Vector Machine (SVM) classification method to allow the joint use of geomorphic features, textures and multispectral data of the Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite. The updated map allowed the identification of a new mafic body and a substantial improvement of the geometry of the known lithological units. The use of geomorphic features allowed for the increase of the overall accuracy from 73% to 79.3%. In addition, we detected chromite occurrences within the ophiolite by applying Spectral Angle Mapping (SAM) technique. We identified two new locations having high concentrations of chromite and verified one of these promising areas and has coarsely crystalline chromite within dunite host rock. The chromium (Cr 2 O 3 ) concentration is ~8.46%. The SAM and SVM methods applied on ASTER satellite data show that these can be used as a powerful tool to explore ore deposits and to further improve lithological mapping in mountainous semi-arid regions.

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…The optimal separation hyperplane is utilized to refer to the decision margin that reduces misclassifications, acquired in the training step [33,90,92]. This method is embedded in the ENVI 4.7 software.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Improving Lithological Mapping by SVM Classification of Spectral and Morphological Features: The Discovery of a New Chromite Body in the Mawat Ophiolite Complex (Kurdistan, NE Iraq)

2014

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“…The sandstone-to-mudstone, which is rich of mica and clay minerals, is mapped by the ratios (6/5 of OLI) and (5/3 of ASTER), since its spectra show high reflectance in band 6 of OLI and band 5 of ASTER, against an absorption at 860 nm (band 5 of OLI) and 800 nm (band 3 of ASTER), corresponding to the near infrared region. This type of absorption is also due to the presence of iron oxides [22][23][24]. In comparison with other spectra, conglomerate shows a high reflectance in band 4 of OLI and band 3 of ASTER, and intense absorption of Al-OH type at 2200 nm (band 7 of OLI and band 6 of ASTER) due to mica or clay minerals [9,23,25], hence the ratios of (4/7 of OLI) and (3/6 of ASTER) are used to map this rock unit.…”

Section: Selection Of Band Combinations For Discriminating Lithologiesmentioning

confidence: 99%

“…This type of absorption is also due to the presence of iron oxides [22][23][24]. In comparison with other spectra, conglomerate shows a high reflectance in band 4 of OLI and band 3 of ASTER, and intense absorption of Al-OH type at 2200 nm (band 7 of OLI and band 6 of ASTER) due to mica or clay minerals [9,23,25], hence the ratios of (4/7 of OLI) and (3/6 of ASTER) are used to map this rock unit. The limestone is discriminated by the ratio (8/4) of ASTER, because its spectrum represents deep absorption in band 8, while the OLI sensor does not cover this region.…”

Section: Selection Of Band Combinations For Discriminating Lithologiesmentioning

confidence: 99%

Application of remotely sensed data in detecting zinc-lead bearing mineralized zones in Westkunlun Huoshaoyun area

Yang

Zhuan

Yao

et al. 2018

J. Phys.: Conf. Ser.

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“…Machine learning presents an attractive way forward, facilitating the use of these data to improve a preliminary lithology map or to produce a starting map from limited observations: in each case, improving an explorer's ability to identify targets. Previous studies, however (e.g., Waske et al, 2009;Harris and Grunsky, 2015), have primarily used a richer and more diverse set of data inputs such as geochemistry or additional spectral information or made use of a different algorithm, such as, for example, support vector machines (SVMs) (Yu et al, 2012) or artificial neural networks (Barnett and Williams, 2009). In this study, we assess the ability of the machine learning algorithm (MLA) Random Forests (RF) to produce a geologic classification using only those geophysical and remote-sensing data that would be available to an explorer in a greenfield, early stage exploration environment.…”

Section: Introductionmentioning

confidence: 99%

Lithologic mapping using Random Forests applied to geophysical and remote-sensing data: A demonstration study from the Eastern Goldfields of Australia

2018

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The Eastern Goldfields of Western Australia is one of the world's premier gold-producing regions; however, large areas of prospective bedrock are under cover and lack detailed lithologic mapping. Away from the near-mine environment, exploration for new gold prospects requires mapping geology using the limited data available with robust estimates of uncertainty. We used the machine learning algorithm Random Forests (RF) to classify the lithology of an underexplored area adjacent to the historically significant Junction gold mine, using geophysical and remotesensing data, with no geochemical sampling available at this reconnaissance stage. Using a sparse training sample, 1.6% of the total ground area, we produce a refined lithologic map. The classification is stable, despite including parts of the study area with later intrusions and variable cover depth, and it preserves the stratigraphic units defined in the training data. We assess the uncertainty associated with this new RF classification using information entropy, identifying those areas of the refined map that are most likely to be incorrectly classified. We find that information entropy correlates well with inaccuracy, providing a mechanism for explorers to direct future expenditure toward areas most likely to be incorrectly mapped or geologically complex. We conclude that the method can be an effective additional tool available to geoscientists in a greenfield, orogenic gold setting when confronted with limited data. We determine that the method could be used either to substantially improve an existing map, or produce a new map, taking sparse observations as a starting point. It can be implemented in similar situations (with limited outcrop information and no geochemical data) as an objective, data-driven alternative to conventional interpretation with the additional value of quantifying uncertainty.

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Towards automatic lithological classification from remote sensing data using support vector machines

Cited by 181 publications

References 27 publications

Improving Lithological Mapping by SVM Classification of Spectral and Morphological Features: The Discovery of a New Chromite Body in the Mawat Ophiolite Complex (Kurdistan, NE Iraq)

Improving Lithological Mapping by SVM Classification of Spectral and Morphological Features: The Discovery of a New Chromite Body in the Mawat Ophiolite Complex (Kurdistan, NE Iraq)

Application of remotely sensed data in detecting zinc-lead bearing mineralized zones in Westkunlun Huoshaoyun area

Lithologic mapping using Random Forests applied to geophysical and remote-sensing data: A demonstration study from the Eastern Goldfields of Australia

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