Spectral discrimination of contact metamorphic zones and its potential for mineral exploration, province of Salamanca, Spain

Goossens, M.; Kroonenberg, S.B.

doi:10.1016/0034-4257(94)90101-5

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…x y x f (2) σ1 and σ2 are the standard deviations of the x and y bands, respectively. µ1 and µ2 are the mean values of the x and y bands.…”

Section: The Probabilistic Approachmentioning

confidence: 99%

“…J is the jacobian of x and y for u and υ. Combining relations (1), (2) and (3) gives the following expression for g(u):…”

Section: The Probabilistic Approachmentioning

confidence: 99%

“…It is given by: ) , ( y x y x u ϕ = = (1) In environmental research (mapping of vegetation covers), x and y x and y are the reflectances at the near infrared and red band, respectively [1] . In mineral exploration (mapping of hydrothermal zones), x and y express reflectances at different bands of the middle infrared spectrum [2], [3].…”

Section: Introductionmentioning

confidence: 99%

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The role of the correlation coefficient on the statistical behavior of a simple band ratio

2008

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The subject of the present paper is how are the statistical parameters of an image of a simple band ratio are influenced, in quantitative terms, by the correlation coefficient between the two bands. The possibility to modify the expression for the ratio of highly correlated bands, in order to enhance the tonality contrast between the target of interest and the adjacent pixels, is also studied. Using proper bivariate Gaussian distributions it is shown that the standard deviation and the entropy of the image decrease considerably, as well as the correlation coefficient increases. When the correlation coefficient is high, it is possible to use the square of the simple ratio, in order to improve the contrast of the image. The results and conclusions of this paper may be useful in mapping the vegetation cover or detecting lithological units and mineralization zones with the aid of remote sensing technology.

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“…x y x f (2) σ1 and σ2 are the standard deviations of the x and y bands, respectively. µ1 and µ2 are the mean values of the x and y bands.…”

Section: The Probabilistic Approachmentioning

confidence: 99%

“…J is the jacobian of x and y for u and υ. Combining relations (1), (2) and (3) gives the following expression for g(u):…”

Section: The Probabilistic Approachmentioning

confidence: 99%

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The role of the correlation coefficient on the statistical behavior of a simple band ratio

2008

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“…Porphyry copper deposits usually show four major kinds of hydrothermal alterations: potasic, phyllic, argillic, and propylitic. Various researches [1][2][3][4][5][6][7][8][9] were performed to study discrimination of alteration zones. They show that the conventional image-processing techniques could not differentiate various hydrothermal alteration regions.…”

Section: Introductionmentioning

confidence: 99%

Identifying Hydrothermal Alterations Using Singularity Mapping of PCA Images Based on ASTER Data

Aragh

Mojeddifar

Chegeni

2020

Mining, Metallurgy & Exploration

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This work integrated selective principal component analysis (SPCA) with a singularity fractal model to map hydrothermal alterations of argillic, phyllic, and propylitic in the north-west of Kerman city in Iran. SPCA results were provided for short wave infrared (SWIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor to map phyllic and propylitic alterations. Also, the bands 5-7 were applied to map the argillic alteration zone. SPCA results could present useful information for alteration mapping but it does not show the purest pixels of alteration. Therefore, a fractal model of singularity was applied to highlight the different alteration pixels in the Kerman Cenozoic magmatic arc (KCMA). Thus, the singularity index of different hydrothermal alteration zones was produced. Comparing the obtained results with the field data showed that Kader, Serenu, Meiduk, and Abdar deposits were acceptably identified by alteration mapping. Also, it seems that the singularity index could not discriminate the hydrothermal alterations of argillic and phyllic. The same spectral signature of kaolinite and muscovite minerals is the main reason for misclassifications.

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A combination of probabilistic neural network (PNN) and particle swarm optimization (PSO) algorithms to map hydrothermal alteration zones using ASTER data

et al. 2020

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Spectral discrimination of contact metamorphic zones and its potential for mineral exploration, province of Salamanca, Spain

Cited by 7 publications

References 16 publications

The role of the correlation coefficient on the statistical behavior of a simple band ratio

The role of the correlation coefficient on the statistical behavior of a simple band ratio

Identifying Hydrothermal Alterations Using Singularity Mapping of PCA Images Based on ASTER Data

A combination of probabilistic neural network (PNN) and particle swarm optimization (PSO) algorithms to map hydrothermal alteration zones using ASTER data

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