Limits...
Discrimination between Sedimentary Rocks from Close-Range Visible and Very-Near-Infrared Images.

Del Pozo S, Lindenbergh R, Rodríguez-Gonzálvez P, Kees Blom J, González-Aguilera D - PLoS ONE (2015)

Bottom Line: This paper proposes the use of a low-cost handy sensor, a calibrated visible-very near infrared (VIS-VNIR) multispectral camera for the recognition of different geological formations.The spectral data was recorded by a Tetracam Mini-MCA-6 camera mounted on a field-based platform covering six bands in the spectral range of 0.530-0.801 µm.Twelve sedimentary formations were selected in the Rhône-Alpes region (France) to analyse the discrimination potential of this camera for rock types and close-range mapping applications.

View Article: PubMed Central - PubMed

Affiliation: Department of Cartographic and Land Engineering, University of Salamanca, Polytechnic School of Avila, Avila, Spain.

ABSTRACT
Variation in the mineral composition of rocks results in a change of their spectral response capable of being studied by imaging spectroscopy. This paper proposes the use of a low-cost handy sensor, a calibrated visible-very near infrared (VIS-VNIR) multispectral camera for the recognition of different geological formations. The spectral data was recorded by a Tetracam Mini-MCA-6 camera mounted on a field-based platform covering six bands in the spectral range of 0.530-0.801 µm. Twelve sedimentary formations were selected in the Rhône-Alpes region (France) to analyse the discrimination potential of this camera for rock types and close-range mapping applications. After proper corrections and data processing, a supervised classification of the multispectral data was performed trying to distinguish four classes: limestones, marlstones, vegetation and shadows. After a maximum-likelihood classification, results confirmed that this camera can be efficiently exploited to map limestone-marlstone alternations in geological formations with this mineral composition.

No MeSH data available.


Related in: MedlinePlus

(a) Band-5 reflectance images.(b) Images of probability. (c) Final classified images.
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pone.0132471.g010: (a) Band-5 reflectance images.(b) Images of probability. (c) Final classified images.

Mentions: In the third step of data processing a supervised classification was performed. Four different training sets were chosen to classify final reflectance images into 4 classes: limestone, marlstone, vegetation and shadow. Sandstone was excluded from the classification process as only two formations, Formation 2 and 4, were composed of this material and because the results in Fig 8 indicate that the spectral response of sandstone in the spectral range covered by the sensor is almost identical to that of the pure limestone formation. Formation 1, 3 and 9 were selected to represent pure limestone and Formation 12 representing pure marlstone. Fig 10 illustrates the resulting classified images for the case of four geological formations, pure limestone and marlstone (Formation 1 and Formation 12) and two mixed formations (Formation 4 and Formation 6). In the classified images white pixels represent pixels masked previously to be out of this process.


Discrimination between Sedimentary Rocks from Close-Range Visible and Very-Near-Infrared Images.

Del Pozo S, Lindenbergh R, Rodríguez-Gonzálvez P, Kees Blom J, González-Aguilera D - PLoS ONE (2015)

(a) Band-5 reflectance images.(b) Images of probability. (c) Final classified images.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493113&req=5

pone.0132471.g010: (a) Band-5 reflectance images.(b) Images of probability. (c) Final classified images.
Mentions: In the third step of data processing a supervised classification was performed. Four different training sets were chosen to classify final reflectance images into 4 classes: limestone, marlstone, vegetation and shadow. Sandstone was excluded from the classification process as only two formations, Formation 2 and 4, were composed of this material and because the results in Fig 8 indicate that the spectral response of sandstone in the spectral range covered by the sensor is almost identical to that of the pure limestone formation. Formation 1, 3 and 9 were selected to represent pure limestone and Formation 12 representing pure marlstone. Fig 10 illustrates the resulting classified images for the case of four geological formations, pure limestone and marlstone (Formation 1 and Formation 12) and two mixed formations (Formation 4 and Formation 6). In the classified images white pixels represent pixels masked previously to be out of this process.

Bottom Line: This paper proposes the use of a low-cost handy sensor, a calibrated visible-very near infrared (VIS-VNIR) multispectral camera for the recognition of different geological formations.The spectral data was recorded by a Tetracam Mini-MCA-6 camera mounted on a field-based platform covering six bands in the spectral range of 0.530-0.801 µm.Twelve sedimentary formations were selected in the Rhône-Alpes region (France) to analyse the discrimination potential of this camera for rock types and close-range mapping applications.

View Article: PubMed Central - PubMed

Affiliation: Department of Cartographic and Land Engineering, University of Salamanca, Polytechnic School of Avila, Avila, Spain.

ABSTRACT
Variation in the mineral composition of rocks results in a change of their spectral response capable of being studied by imaging spectroscopy. This paper proposes the use of a low-cost handy sensor, a calibrated visible-very near infrared (VIS-VNIR) multispectral camera for the recognition of different geological formations. The spectral data was recorded by a Tetracam Mini-MCA-6 camera mounted on a field-based platform covering six bands in the spectral range of 0.530-0.801 µm. Twelve sedimentary formations were selected in the Rhône-Alpes region (France) to analyse the discrimination potential of this camera for rock types and close-range mapping applications. After proper corrections and data processing, a supervised classification of the multispectral data was performed trying to distinguish four classes: limestones, marlstones, vegetation and shadows. After a maximum-likelihood classification, results confirmed that this camera can be efficiently exploited to map limestone-marlstone alternations in geological formations with this mineral composition.

No MeSH data available.


Related in: MedlinePlus