Imaging Spectroscopy Applied to Mineral Mapping Over Large Areas: Impact of Residual Atmospheric Artefacts in Reflectance Spectra on Mineral Identification and Mapping

Author(s):  
Raymond F. Kokaly ◽  
Gregg A. Swayze ◽  
K. Eric Livo ◽  
Todd M. Hoefen ◽  
Bernard E. Hubbard ◽  
...  
Author(s):  
S. B. Sayyad ◽  
Z. R. Mohammed ◽  
R. R. Deshmukh

<p><strong>Abstract.</strong> The imaging spectroscopy offers an opportunity to map and discriminate different minerals on the lunar surface which further helps to understand the origin, evolution process, and the crustal composition on the surface of the moon. Compositional mapping of the lunar surface is considered as a standard approach for mineral mapping. This paper reports surface mineralogy of the lunar surface from Mare Vaporum using Chandrayaan-1 Hyperspectral remotely sensed data from HySi sensor. False color composite is created using different band shaping algorithms like band strength; band curve and band tilt parameters at crucial wavelength for spatial analysis. The Spectral analysis has been done by deriving reflectance spectra at varying locations from the area under study. The Study shows the mineral map with different categories of minerals which are high-Ca pyroxene and/or olivine and low Ca-pyroxene. However because of the limited spectral coverage of HySi, data at the longer wavelengths required to discriminate among different group of minerals.</p>


Geosciences ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 195
Author(s):  
Soichiro Tanaka ◽  
Hideo Tsuru ◽  
Kazuaki Someno ◽  
Yasushi Yamaguchi

Hydrothermal alteration minerals, which are important as indicators in the exploration of ore deposits, exhibit diagnostic absorption peaks in the short-wavelength infrared region. We propose an approach for the identification of alteration minerals that uses a deep learning method and compare it with conventional identification methods which use numerical calculation. Inexpensive spectrometers often tend to show errors in the wavelength direction, even after wavelength calibration, which causes erroneous mineral identification. In this study, deep learning is applied to extract features from reflectance spectra to remove such errors. Two typical deep learning methods—a convolutional neural network and a multi-layer perceptron—were applied to spectral reflectance data, with and without hull quotient processing, and their accuracy rates and f-values were evaluated. There was an improvement in mineral identification accuracy when hull quotient processing was applied to the learning data.


Author(s):  
R. Clark ◽  
J. Boardman ◽  
J. Mustard ◽  
F. Kruse ◽  
C. Ong ◽  
...  

2000 ◽  
Vol 6 (3) ◽  
pp. 187-200 ◽  
Author(s):  
Harold R. Lang ◽  
Steven M. Baloga

Abstract The fundamental promise of imaging spectroscopy is to provide surface mineralogy based on remotely-acquired, gridded reflectance spectra of comparable quality to those from high resolution laboratory and field spectrometers. For regulatory and environmental monitoring, validating imaging spectrometer data is a major issue with this emerging technology. In this paper we validate 1997 Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) reflectance spectra covering 0.4 mu m-2.4 mu m at a stable, flat, manmade target at Ray Mine, Arizona, for EPA/NASA assessment of the utility of remote sensing for monitoring acid drainage from an active open pit copper mine. For validation, we a) compare qualitatively, laboratory and field reflectance spectra with corresponding AVIRIS spectra; b) compare quantitatively, mineralogically diagnostic statistics from field spectra with the same statistics from field spectra with the same statistics from AVIRIS spectra; and c) demonstrate a methodology for validating imaging spectrometer data for environmental applications.


2020 ◽  
Vol 12 (11) ◽  
pp. 1723
Author(s):  
Etienne Ducasse ◽  
Karine Adeline ◽  
Xavier Briottet ◽  
Audrey Hohmann ◽  
Anne Bourguignon ◽  
...  

Clay minerals play an important role in shrinking–swelling of soils and off–road vehicle mobility mainly due to the presence of smectites including montmorillonites. Since soils are composed of different minerals intimately mixed, an accurate estimation of its abundance is challenging. Imaging spectroscopy in the short wave infrared spectral region (SWIR) combined with unmixing methods is a good candidate to estimate clay mineral abundance. However, the performance of unmixing methods is mineral-dependent and may be enhanced by using appropriate spectral preprocessings. The objective of this paper is to carry out a comparative study in order to determine the best couple spectral preprocessing/unmixing method to quantify montmorillonite in intimate mixtures with clays, such as montmorillonite, kaolinite and illite, and no-clay minerals, such as calcite and quartz. To this end, a spectral database is built with laboratory hyperspectral imagery from 51 dry pure mineral samples and intimate mineral mixtures of controlled abundances. Six spectral preprocessings, standard normal variate (SNV), continuum removal (CR), continuous wavelet transform (CWT), Hapke model, first derivative (1st SGD) and pseudo–absorbance (Log(1/R)), are applied and compared with reflectance spectra. Two linear unmixing methods, fully constrained least square method (FCLS) and multiple endmember spectral mixture analysis (MESMA), and two non-linear unmixing methods, generalized bilinear method (GBM) and multi-linear model (MLM), are compared. Global results showed that the benefit of spectral preprocessings occurs when spectral absorption features of minerals overlap for SNV, CR, CWT and 1st SGD, whereas the use of reflectance spectra performs the best when no overlap is present. With one mineral having no spectral feature (quartz), montmorillonite abundance estimation is difficult and gives RMSE higher than 50%. For the other mixtures, performances of linear and non-linear unmixing methods are similar. Consequently, the recommended couple spectral preprocessing/unmixing method based on the trade-off between its simplicity and performance is 1st SGD/FCLS for clay binary and ternary mixtures (RMSE of 9.2% for montmorillonite–illite mixtures, 13.9% for montmorillonite–kaolinite mixtures and 10.8% for montmorillonite–illite–kaolinite mixtures) and reflectance/FCLS for binary mixtures with calcite (RMSE of 8.8% for montmorillonite–calcite mixtures). These performances open the way to improve the classification of expansive soils.


Geophysics ◽  
1987 ◽  
Vol 52 (7) ◽  
pp. 907-923
Author(s):  
Lawrence C. Rowan ◽  
Alexander F. H. Goetz ◽  
Elsa Abbott

During the November 12–14, 1981, mission of the space shuttle Columbia, the Shuttle Multispectral Infrared Radiometer (SMIRR) recorded radiances in ten channels along a 100 m wide groundtrack across the western Saudi Arabian shield. The ten channels are located in the 0.5 to 2.4 μm region, with five positioned between 2.0 and 2.40 μm for measuring absorption features that are diagnostic of OH‐bearing and [Formula: see text] minerals. This exceptionally well exposed area consists of late Proterozoic metamorphic, intermediate to silicic intrusive, and interlayered clastic sedimentary and intermediate silicic volcanic rocks that have not been studied previously using SMIRR data. Plots or traces of unnormalized SMIRR channel ratios were examined before field studies to locate areas with high spectral contrast, especially in the 2.0 μm to 2.40 μm channels. Reflectance spectra were measured in the laboratory for rock and soil samples collected in these areas, and the mineralogic causes of the main absorption features were determined using X‐ray diffraction. Laboratory SMIRR spectra were produced by convolving the ten SMIRR filters with the laboratory spectra. Then, normalized SMIRR reflectance spectra were generated along the groundtrack using normalization coefficients calculated for a field sample representing a uniform, low‐spectral contrast area. Field evaluation shows that unnormalized SMIRR ratio traces are useful, even without specific mineralogic information, for distinguishing rocks that are characterized by Al‐OH, Mg‐OH, and/or [Formula: see text], [Formula: see text], and [Formula: see text] absorption features. Analysis of field samples permits suites of minerals causing absorption features to be identified. However, specific mineral identification cannot be achieved consistently using the SMIRR ratio traces or normalized SMIRR spectra, because the Al‐OH and Mg‐OH absorption features can be caused by more than one of the minerals commonly present. The normalized SMIRR spectra are especially useful for identifying subtle Al‐OH and Mg‐OH absorption features that are difficult to identify in the unnormalized ratio traces and for comparing the relative intensities of absorption features. Al‐OH absorption is related to muscovite, smectite, illite, and kaolinite, whereas Mg‐OH absorption is caused by chlorite, amphibole, and biotite. The principal sources of error in using SMIRR spectral measurements for identifying mineral groups along the orbit 27 groundtrack are inaccuracies in field location and lithologic heterogeneity that is not represented adequately by field samples. Calibration errors may account for systematic albedo and absorption intensity differences between calculated laboratory SMIRR spectra and normalized SMIRR spectra. SMIRR instrument noise and atmospheric factors appear to be less important sources of error. However, as higher spectral and spatial resolution systems are developed for mineral identification, radiometric precision and atmospheric factors will become more important.


2011 ◽  
Vol 7 (1) ◽  
pp. 47-63
Author(s):  
K. Szalay ◽  
J. Deákvári ◽  
F. Firtha ◽  
I. Tolner ◽  
Á. Csorba ◽  
...  

The hyperspectral imaging spectroscopy is a promising future tool in the field of optical remote sensing and it creates new perspective for modern information management in site specific agricultural production. One can determine quantitative relationships between the environmental and physiological parameters of vegetation cover and the soil quality parameters as well as the features of the reflectance spectra by the newgeneration data monitoring and sampling method. These reflectance spectra have characteristics of the different crops and provide with the possibility of accurate classification and detection. The objective was to present the technological capabilities of hyperspectral imaging and show some exprimental results of nutrient sensitive changes in the winter wheat spectra. There were found two characteristic wavelength ranges: the 500 to 800 nm for wheat kernel samples and the 1650 nm to 1800 nm for wheat ear samples where fertilizer treatments showed definite trend on the basis of the normalized reflectance spectra.


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