ADVANCED MINERAL MAPPING USING VISIBLE TO NEAR INFRARED, SHORTWAVE AND LONGWAVE INFRARED HIGH SPECTRAL RESOLUTION DATA

Transcription

1 ADVANCED MINERAL MAPPING USING VISIBLE TO NEAR INFRARED, SHORTWAVE AND LONGWAVE INFRARED HIGH SPECTRAL RESOLUTION DATA Veronika Kopačková, Lucie Koucká

2 Overview Concept of our approach, QUANTOOLS into QUANTOOLS: mineral mapping using optical or thermal HS data sets QUANTOOLS: mineral mapping using optical and thermal (VNIR/SWIR/LWIR) data Conclusions

3 Motivation the majority of the previous work still exploit spectral data from an optical (VNIR, SWIR) or thermal (LWIR) wavelength range the full-range information (VNIR/SWIR/LWIR) is not combined into a single integrated data product This limits the complexity of the final interpretation as spectral and spatial associations or patterns may be too complex to be seen by naked eyes and thus may remain hidden.

4 Spectral full-range concept VNIR TIR (emissivity) SWIR jarosite goethite Fe3+ minerals White micas Silica optical and thermal spectral data when used together allow mapping different varieties of minerals and thus allow mapping lithology in a more complex way

5 QuanTOOLS new tools allowing automatic detection of multiple absorption feature parameters (multiple absorption maximum wavelengths and depths detected within the VNIR/SWIR/TIR regions)

6 Integration and classification Kopačková, V.; Koucká, L. Integration of Absorption Feature Information from Visible to Longwave Infrared Spectral Ranges for Mineral Mapping. Remote Sens. 2017, 9, 1006.

7 QuanTOOLS Mineral mapping using thermal HS image data (Aisa OWL SPECIM, emissivity data provided by the Tel Aviv University) Noise detection/correction: no Automatic multiple absorptions detection Number of absorptions set: 5 Spectral range: µm Absorptions wavelength (AW) raster (MNF transformation of the 5 most distinct absorption wavelengths matrixes)

8 QuanTOOLS Final classification: 15 different mineral classes detected Sanstone, mudstone, clay, pubble stone Sanstone, clay Volcanic rocks Gypsum Dolostone, limestone Limestone, clay, mudstone Limestone clay, gypsum

9 QuanTOOLS case studies SPECIM, SisuCHEMA (SWIR data) sample size is 200 x 300 x 45 mm Final clasification (5 classes) wavelength depth classification

10 QuanTOOLS case studies Rodalquilar hydrothermal system Quantools (CGS) Wavelength mapper (ITC) Van Der Meer, F. Kopačková V. Koucká, L. Bakker, W. Van Der Werff, H. (2017): Wavelength feature mapping as a proxy to mineral chemistry for investigating geologic systems: an example from the Rodalquilar epithermal system. International Journal of Applied Earth Observation and Geoinformation.

11 Full-range approach: Image data HyMap 09/2010 AHS 07/2011 Pre-processing Atmospheric correction (reflectance, emissivity) Corregistering Masking water and vegetation Kopačková, V.; Koucká, L. Integration of Absorption Feature Information from Visible to Longwave Infrared Spectral Ranges for Mineral Mapping. Remote Sens. 2017, 9, 1006.

12 Full-range approach: Image data 2 scenarios: The absorption feature parameters derived fro m VNIR/SWIR HyMap data used for the MNF transformation and consequent mineral mapping The absorption feature parameters derived fro m VNIR/SWIR/LWIR data (HyMap and AHS) used for the MNF transformation and consequent mineral mapping Kopačková, V.; Koucká, L. Integration of Absorption Feature Information from Visible to Longwave Infrared Spectral Ranges for Mineral Mapping. Remote Sens. 2017, 9, 1006.

13 Study area Kopačková, V.; Koucká, L. Integration of Absorption Feature Information from Visible to Longwave Infrared Spectral Ranges for Mineral Mapping. Remote Sens. 2017, 9, 1006.

14 Results: Medard pit (B) classification using Scenario 1 (absorption feature parameters derived only from the HyMap data were used for the consequent mineral mapping), (C) classification using Scenario 2 (absorption feature parameters derived from both datasets, HyMap and AHS, were used for the consequent mineral mapping), (D) enlargement of the area of interest. *Class 2 and *Class 9: two additional classes mapped when using Scenario 2. Kopačková, V.; Koucká, L. Integration of Absorption Feature Information from Visible to Longwave Infrared Spectral Ranges for Mineral Mapping. Remote Sens. 2017, 9, 1006.

15 Results: Lítov dump A) (B) classification using Scenario 1 (absorption feature parameters derived only from the HyMap data were used for the consequent mineral mapping), (C) classification using Scenario 2 (absorption feature parameters derived from both datasets, HyMap and AHS, were used for the consequent mineral mapping), (D) enlargement of the area of interest. *Class 2 and *Class 9: two additional classes mapped when using Scenario 2. Kopačková, V.; Koucká, L. Integration of Absorption Feature Information from Visible to Longwave Infrared Spectral Ranges for Mineral Mapping. Remote Sens. 2017, 9, 1006.

16 Spectral property of the classes The average class spectrum derived from the HyMap data: (A) the VIS/NIR spectral range ( μm), (B) the SWIR range ( μm). Kopačková, V.; Koucká, L. Integration of Absorption Feature Information from Visible to Longwave Infrared Spectral Ranges for Mineral Mapping. Remote Sens. 2017, 9, 1006.

17 Spectral property of the classes (A) Emissivity of some silicates is displayed using the Arizona University Spectral library [68], the original spectra are displayed together with the equivalent spectra resampled to the spectral resolution of the AHS data. (B) The average class spectrum derived from the AHS data. The mapped classes correspond to the mineral classes in Table 1, *Class 2 and *Class 9: two additional classes mapped when using Scenario 2. Kopačková, V.; Koucká, L. Integration of Absorption Feature Information from Visible to Longwave Infrared Spectral Ranges for Mineral Mapping. Remote Sens. 2017, 9, 1006.

18 Mineral validation the different spectralranges of the two different sensors (HyMap and AHS) was integrated and led to a mineral classification that differentiated between the diverse Fe3+-bearing minerals and phyllosilicates as well as lignite and quartz contents and overall resulted in a more complex mineral/lithology classification. Kopačková, V.; Koucká, L. Integration of Absorption Feature Information from Visible to Longwave Infrared Spectral Ranges for Mineral Mapping. Remote Sens. 2017, 9, 1006.

19 Conclusions We can conclude that our approach: the approach used here does not require prior definition of the endmembers; moreover, there is no need for prior knowledge or data on the specific conditions QUANTools, the new toolbox developed, allows automatic and errorless multiple-absorption feature parameters extraction from different spectral ranges, and these parameters can be further integrated into one product, which can consequently be successfully used for mineral mapping/classification this multi-range spectral integration leads to more complex ineral/lithology classification the approach can be used to integrate the spectral information acquired by different sensors (e.g., HyMap and AHS, CASI/SASI TASI).