RAPID CLASSIFICATION OF INFRA-RED … · match best (hull shape vs. absorption feature) and which...
Transcript of RAPID CLASSIFICATION OF INFRA-RED … · match best (hull shape vs. absorption feature) and which...
RAPID CLASSIFICATION OF INFRA-RED HYPERSPECTRAL IMAGERY OF ROCKS WITH DECISION TREES AND WAVELENGTH IMAGES F.J.A. VAN RUITENBEEK, H.M.A VAN DER WERFF,
W.H. BAKKER, F.D. VAN DER MEER, C.H. HECKER, K.A.A. HEIN
Specim hyperspectral camera, sisuchema setup Wavelength range: 1000-2500 nm (short-wavelength infrared) # pixels across: 384 Spatial resolution: 26 µm
INTRODUCTION
Specim.fi
INFRA-RED HYPERSPECTRAL IMAGE ACQUIRED WITH SISUCHEMA
Pixel size: 26 µm
384 pixels
1396 pixels
Reflectance at 1650 nm:1650nm
Silicified, sericitized dacite‐andesite
Provides detailed information on mineralogical composition and microstructure of rocks
Enables characterization of rock type and rock forming processes Spatial scale comparable to “traditional” thin section Input in predictive modeling of rock chemistry, e.g. ore grade, and
other physical-chemical parameters
USE OF HIGH SPATIAL RESOLUTION IR IMAGERY
Image-sizes are typically large (> 1 GB per raw image) Images contain many pixels ( ~ 1 million per image) and bands
(288) Easy generation of large volumes of image data (1 image
acquired in ~5 minutes, incl. sample preparation) Interpretation of imagery is labor intensive and time consuming
(and often subjective)
PROBLEM
Methods are needed for rapid assessment of mineralogical composition
SISUCHEMA VERSUS HYMAP
~512 pixels126 bands450‐2500nm
Hymap (airborne sensor) SisuChema
384 pixels288 bands1000‐2500nm
Atmospheric interference
SISUCHEMA VERSUS ASD
1 point spectrum2151 bands350‐2500nm
ASD SisuChema
384 pixels288 bands1000‐2500nm
Many strategies involve spectral matching of image and reference spectra and thresholding, e.g. Spectral Angle Mapper
Limitations of these methods: A prioiry knowledge of scene is required for the selection of
reference spectra Matching statistics doesn’t show which parts of the spectrum
match best (hull shape vs. absorption feature) and which do not Selection of threshold for a “match” is rather subjective
INTERPRETATION OF HYPERSPECTRAL IMAGERY
Wavelength position of absorption features is used as the dominant spectral charateristic in the interpretation of reflectance spectra
It is directly related to molecular bond in crystal lattice and often specific to (groups) of minerals
This information is extracted from wavelength images calculated from the IR imagery
A decision tree is used for classification of the wavelength imagery
APPROACH IN THIS STUDY
CALCULATION OF WAVELENGTH POSITION
where w(x) is the interpolated reflectance value atposition x; x is the wavelength position in μm; a,b,c are the coefficients of the parabola function.
where wmin is the interpolated wavelength position at minimum reflectance; a,b is the coefficients of the parabola function.
where depth is the interpolated depth of absorption feature.
W1, D1: Wavelength and depth of deepest featureW2, D2: Wavelength and depth of 2nd featureW3, D3: Wavelength and depth of 3rd feature
W1
D1
W3
D3
W2
D2
Wavelength mapW1 fused with D1
2100nm
2400nm
Wavelength image
Only for exploratory analysis -> no classified mineral map Small variation in wavelength positions often not visible Deep absorption features dominate over shallow features
LIMITATION OF WAVELENGTH MAPPING
Silicified, chloritised amygdaloidal (dacite)‐andesite
Amygdale L: 3.2mm PP Amygdale L: 3.2mm XP
classification using decision treesClassified map
D1 > 0.05
W1 > 2225nm
W1 > 2300nm
Scatter plot of wavelength image
2100nm
2400nm
Wavelength image
Based on analysis absorption features in spectra of USGS spectral library and other spectra (total of 400+ spectra)
DESIGN OF DECISION TREE
Decision tree 2100‐2400nm (Al‐OH, Fe‐OH, Mg‐OH & carbonate features):
W1 2200‐2210W2 2340‐2400
W1 2210‐2220W2 2340‐2400
W1 2200‐2210W2 2160‐2280
CLASSIFICATION WITH DECISION TREE
chalcedony_cu91‐6a.4502.aschydrogrossular_nmnh120555.10236.ascillite_gds4.10903.ascillite_imt1.10982.ascillite_imt1.11041.ascmontmorillonite_saz1.14498.ascmontmorillonite_sca2.14557.ascmuscovite_gds116.15173.ascmuscovite_gds118.15287.ascmuscovite_hs24.15512.ascmuscovite_il107.15566.ascvesuvianite_hs446.23527.asc
dickite_nmnh46967.6913.ascendellite_gds16.7379.aschalloysite_cm13.8921.asckaolinite_cm3.11788.asckaolinite_cm5.11846.asckaolinite_cm7.11904.asckaolinite_cm9.11962.asckaolinite_gds11.12060.asckaolinite_kga1.12117.asckaolinite_kl502.12272.asckaolinite_pfn1_kga2.12176.asc
goethite_mpcma2b.8351.ascillite_il105.10969.asclepidolite_nmnh105541.12766.asclepidolite_nmnh88526‐1.12832.ascmargarite_gds106.13344.ascmontmorillonite_cm20.14324.ascmontmorillonite_cm26.14382.ascmuscovite_gds107.14887.ascmuscovite_gds114.15116.ascmuscovite_gds117.15230.ascmuscovite_gds119.15344.ascmuscovite_gds120.15401.ascmuscovite_hs146.15457.ascnanohematite_br93‐34b2.15589.ascorthoclase_hs13.17283.ascroscoelite_en124.19682.ascspodumene_hs210.21114.asctourmaline_hs282.22996.asc
Short‐list of candidate spectra
Albedo – R1650
Rock sample: Silicified, sericitised amydaloidal andesite
Classified
W1 2200‐2210W2 2340‐2400
W1 2210‐2220W2 2340‐2400
W1 2200‐2210W2 2160‐2280
CLASSIFICATION WITH DECISION TREE
dickite_nmnh46967.6913.ascendellite_gds16.7379.aschalloysite_cm13.8921.asckaolinite_cm3.11788.asckaolinite_cm5.11846.asckaolinite_cm7.11904.asckaolinite_cm9.11962.asckaolinite_gds11.12060.asckaolinite_kga1.12117.asckaolinite_kl502.12272.asckaolinite_pfn1_kga2.12176.asc
Short‐list of candidate spectraRock sample: Silicified, sericitised amydaloidal andesite
Albedo – R1650 Classified
W1 2200‐2210W2 2340‐2400
W1 2210‐2220W2 2340‐2400
W1 2200‐2210W2 2160‐2280
CLASSIFICATION WITH DECISION TREE
dickite_nmnh46967.6913.ascendellite_gds16.7379.aschalloysite_cm13.8921.asckaolinite_cm3.11788.asckaolinite_cm5.11846.asckaolinite_cm7.11904.asckaolinite_cm9.11962.asckaolinite_gds11.12060.asckaolinite_kga1.12117.asckaolinite_kl502.12272.asckaolinite_pfn1_kga2.12176.asc
Short‐list of candidate spectraRock sample: Silicified, sericitised amydaloidal andesite
Amygdale 2.5mm
Albedo – R1650 Classified
Hydrothermally altered rocks Associated with VMS Cu-Zn deposits Pervasive alteration of volcanic rock Archean (3.2 Ga) submarine setting
CASE STUDY
Albedo – reflectance at 1650nm
Weakly sericite altered and silicified muddy chert
Silicified, seriticized xenocrystic‐phenocrystic (dacite)‐andesite
Silicified, sericitized phenocrystic (dacite)‐andesite
Silicified, sericitized weakly phenocrystic dacite
Silicified, sericitized weakly phenocrystic quenched dacite
Silicified, sericitized weakly phenocrystic dacite
Silicified, sericitized amygdaloidal andesite
Silicified, sericitized weakly amydaloidal titanium‐rich andesite
Ferruginous, chloritised basalt
Ferruginous, chloritized (pyroxene‐bearing) andesite
Silicified, and chloritised amygdaloidal (dacite)‐andesite
Micrograph thin section
Al‐rich illite‐muscovite <2210nm
Shallow Fe‐OH feature 2260‐
2300nm
Fe‐chlorite
Al‐poor illite‐muscovite >2210nm
kaolinite
Al‐rich illite‐muscovite
< 2203nm – chert
Chlorite
Mg‐Fe chlorite
Interpreted mineralogy:
Albedo – reflectance at 1650nm
Classification – general decision tree
Pervasive alteration
Pervasive alteration
Illite‐muscovite alteration
Kaolinite filled amygdales
Illite‐musc rich amygdales
Zonation:• Al‐rich illite‐muscovite • Al‐poor illite‐muscovite • Chorite +/‐ illite‐muscovite
Classification of wavelength images with decision trees provides method for rapid assessment of mineral composition
No a priory information on mineralogy of rock sample is required Focus on mineral absorption features (diagnostic for many
minerals, unlike hull-shapes) Objective and reproducible result
SUMMARY AND CONCLUSIONS
Scene-specific optimization of decision tree Automation of processing steps Extraction of microstructural / textural information
FURTHER WORK
Objective: To optimize decision tree for specific scene – sample set Enhancement of spectral variation in wavelength images Calculation of summary products, such as illite and kaolinite
crystallinity, ferrous drop, etc Visual-spatial analysis of contrast enhanced images and selection
of additional end member ROIs Analysis of ROIs: Spectra and scatter plots of wavelength
positions and depth of absorption features and summary products Improvement of slicing intervals and update of decision tree
STEP 2 – DETAILED IMAGE ANALYSIS
Albedo: Reflectance at 1650nm
Illite‐musc crystallinity
Summary product:Illite‐musc crystallinity = Depth H20 / Depth Al‐OH
Depth Al‐OH
Depth H20
Classified with decision tree
CC of W1, W2,W3 between 1850‐2100nm
Illite‐musc crystallinity
Classified with decision tree
Update decision three with crystallinity data
Illite/muscovite matrix
Well‐ordered illite/muscovite phenocryst
Poorly‐ordered illite/muscovite
Interpretation:
Phenocryst Length: 1.4 mm
Xenocryst Length: 2 mm
Albedo – reflectance at 1650nm
Weakly sericite altered and silicified muddy chert
Silicified, seriticized xenocrystic‐phenocrystic (dacite)‐andesite
Silicified, sericitized phenocrystic (dacite)‐andesite
Silicified, sericitized weakly phenocrystic dacite
Silicified, sericitized weakly phenocrystic quenched dacite
Silicified, sericitized weakly phenocrystic dacite
Silicified, sericitized amygdaloidal andesite
Silicified, sericitized weakly amydaloidal titanium‐rich andesite
Ferruginous, chloritised basalt
Ferruginous, chloritized (pyroxene‐bearing) andesite
Silicified, and chloritised amygdaloidal (dacite)‐andesite
Micrograph thin section
Al‐rich illite‐muscovite <2210nm
Shallow Fe‐OH feature 2260‐
2300nm
Fe‐chlorite
Al‐poor illite‐muscovite >2210nm
kaolinite
Al‐rich illite‐muscovite
< 2203nm – chert
Chlorite
Mg‐Fe chlorite
Ordered illite/muscovite
Disordered illite/muscovite
“Ordered” kaolinite
Ordered illite/muscovite
Disordered illite/muscovite
Interpreted mineralogy:
Albedo – reflectance at 1650nm
Classification – general decision tree
Pervasive alteration
Pervasive alteration
Illite‐muscovite alteration
Kaolinite filled amygdales
Illite‐musc rich amygdales
Zonation:• Al‐rich illite‐muscovite • Al‐poor illite‐muscovite • Chorite +/‐ illite‐muscovite
Albedo – reflectance at 1650nm
Classification – general decision tree
Classification –decision tree ‐ sample specific
Al‐rich illite‐muscovite <2210nm
Shallow Fe‐OH feature 2260‐
2300nm
Fe‐chlorite
Al‐poor illite‐muscovite >2210nm
kaolinite
Al‐rich illite‐muscovite
< 2203nm – chert
Chlorite
Mg‐Fe chlorite
Ordered illite/muscovite
Disordered illite/muscovite
“Ordered” kaolinite
Ordered illite/muscovite
Disordered illite/muscovite
Interpreted mineralogy:
PhenocrystsXenocrystsAmygdales
Volcanic texture!