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Exploratory unsupervised mapping of CRISM imagery using summary product signatures
Elyse Allender ([email protected])1, Tomasz F. Stepinski1
1Space Informatics Lab, University of Cincinnati, OH.
Conclusions Yo.
Methodology:
- CRISM CAT pre-processing: PHT + ATM + destriping + summary product creation from PDS image.
- Graph-based segmentation into homogeneous superpixels.
- DEMUD [4] applied to data.
- Number of mineral classes determined.
- NN superpixel classification. Optional thresholding.
- Output map and interpretation graphs of each class.
- Graph-based segmentation into homogeneous superpixels.
- Number of mineral classes determined.- Number of mineral classes determined.
- NN superpixel classification. Optional thresholding.
- Output map and interpretation graphs of each class.
Fe/Mg phyllosilicate
mixed mineralogy
kaolinit
class threeclass five
A B C
class oneclass four
class two
kaolinitemontmorilloniteFe/Mg phyllosilicates
pyroxene
olivine
altered olivinemontmorillonite
pyroxene
Fe/Mg phyllosilicates
mixed mineralogy
Introduction:We develop a novel method of performing unsupervised mineralogical mapping on CRISM imagery which uses summary parameter products as input, rather than the full spectral function. Maps produced using this method may be looked upon as 'super' browse products, and graphs of the content of each class help guide the user to a clearer interpretation of what the class contains relative the the mean content of the image. This method extracts the most 'unique' classes from the image first, enabling the detection of rare mineralogy. We propose this method be implemented globally for the purposes of scientific discovery.
0.04
0.05
0.06
0.07
0.08
Wavelength (nm)
LNO
RM
I/F
1000
1400
1800
2200
2600
Figure 2. Resulting mineral map for the Nili Fossae site. Four classes were detected by DEMUD including the altered olivine class containing the rare mineral magnesite. The interpretation graphs for each class are displayed (A to D), giving the user a better idea of the contents of each class with respect to the mean 'spectrum' of the image. This map can be used to guide futher mineral analysis. For example, the yellow 'olivine' class is visibly enhanced in olivine detection parameters.
Figure 3. Comparison with SMACC spectral unmixing results produced by HiiHat software [5]. The spectral results produced using this method are not as easily interpreted with a limited knowledge of spectral functions and their relation to specific minerals, however, these endmembers can be compared to a spectral library for identification. A limitation being that there may be no adequate match. Conclusions:
Our method is well suited for large exploratory surveys of CRISM images. Our results are interpretable, and may be used as a guide for the detailed manual analysis of minerals present in the broader classes discovered.
Figure 1. Study site within Nili Fossae: Traditional methods of mineralogical analysis include the creation of a suite of RGB maps using spectral bands selected to highlight minerals known a priori (left) as in [1], and the creation of mineral browse products - here, CAR (centre) and HYD (right) - in which spectral parameter products are created to highlight specific mineral classes as in [2]. The advantage of our method is that only one map need be generated for each image, instead of a suite of RGB combinations.
Results:
References:[1]: Ehlmann et al. (2009), J. Geophys. Res., 114, E00D08 [2]: Carter et al. (2010), Science, 328(5986), 1682-6 [3]: Felzenszwalb & Huttenlocher (2004), Int. J. Comp. Vision, 59(2), 167-181 [4]: Wagstaff et al. (2013), Proc. of AAAI-13 [5]: Mandrake et al. (2010), LPSC 2010 Abstract 1441.
Figure 4. Results from Stokes crater site (top) and Hale crater site
montmorillonite
BDI1
000I
RIR
AO
LIN
DEX
LCPI
ND
EXH
CPIN
DEX
ISLO
PE1
BD14
35BD
1500
* ICE
R1BD
1750
BD19
00BD
I200
0BD
2100
BD22
10BD
2290
D23
00SI
ND
EX* IC
ER2
BDCA
RBBD
3000
BD31
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3200
BD34
00BD
1270
O2
BD14
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2OBD
2000
CO2
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2600
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R2R2
700
BD27
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R3O
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DEX
2BD
1900
RBD
1980
BD22
00D
oub2
200
BD22
30BD
2500
0.0
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0.6
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1.0
1.2 altered olivine
Fe/Mg phyllosilicates
BDI1
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DEX
LCPI
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DEX
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1500
* ICE
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1750
BD19
00BD
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0BD
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2290
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BDCA
RBBD
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3200
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00BD
1270
O2
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2OBD
2000
CO2
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* IR
R2R2
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BD27
00* IR
R3O
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1980
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oub2
200
BD22
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0.0
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1.0
1.2pyroxene
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EX* IC
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RBBD
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O2
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2OBD
2000
CO2
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2600
* IR
R2R2
700
BD27
00* IR
R3O
LIN
DEX
2BD
1900
RBD
1980
BD22
00D
oub2
200
BD22
30BD
2500
0.0
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1.0
1.2
mixed mineralogy
BDI1
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RIR
AO
LIN
DEX
LCPI
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EXH
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DEX
ISLO
PE1
BD14
35BD
1500
* ICE
R1BD
1750
BD19
00BD
I200
0BD
2100
BD22
10BD
2290
D23
00SI
ND
EX* IC
ER2
BDCA
RBBD
3000
BD31
00BD
3200
BD34
00BD
1270
O2
BD14
00H
2OBD
2000
CO2
BD23
50BD
2600
* IR
R2R2
700
BD27
00* IR
R3O
LIN
DEX
2BD
1900
RBD
1980
BD22
00D
oub2
200
BD22
30BD
2500
0.0
0.2
0.4
0.6
0.8
1.0
1.2
BDI1
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RIR
AO
LIN
DEX
LCPI
ND
EXH
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DEX
ISLO
PE1
BD14
35BD
1500
* ICE
R1BD
1750
BD19
00BD
I200
0BD
2100
BD22
10BD
2290
D23
00SI
ND
EX* IC
ER2
BDCA
RBBD
3000
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00BD
3200
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00BD
1270
O2
BD14
00H
2OBD
2000
CO2
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2600
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R2R2
700
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R3O
LIN
DEX
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oub2
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F
wavelength (nm)1000 1430 1860 2290 2710
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.075
A
B
C
D
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F
class one class two
class three class fourclass five
'background' mineralogy
pyroxene
carbonate
olivine
A B C
class one class two
class three class four
LNO
RM I/
F
wavelength (nm)1000 1430 1860 2290 2710
.05
.055
.06
.065
.07
.075
Ccarbonates
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1.0
1.2
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*IRR3
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b220
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00
olivine
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A
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2290
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EX*IC
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RBBD
3000
BD31
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EXBD
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O2
BD14
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OBD
2000
CO2
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2600
*IRR2
R270
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2700
*IRR3
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EX2
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BD19
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b220
0BD
2230
BD25
00
“background” mineralogy
BDI1
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1500
*ICER
1BD
1750
BD19
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BD22
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2290
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ND
EX*IC
ER2
BDCA
RBBD
3000
BD31
00BD
3200
BD34
00CI
ND
EXBD
1270
O2
BD14
00H2
OBD
2000
CO2
BD23
50BD
2600
*IRR2
R270
0BD
2700
*IRR3
OLI
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EX2
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BD19
80BD
2200
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b220
0BD
2230
BD25
00
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class one class two
class three class four
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BDI1
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RIR
AO
LIN
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LCPI
ND
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DEX
ISLO
PE1
BD14
35BD
1500
* ICE
R1BD
1750
BD19
00BD
I200
0BD
2100
BD22
10BD
2290
D23
00SI
ND
EX* I
CER2
BDCA
RBBD
3000
BD31
00BD
3200
BD34
00BD
1270
O2
BD14
00H
2OBD
2000
CO2
BD23
50BD
2600
* IRR
2R2
700
BD27
00* I
RR3
OLI
ND
EX2
BD19
00R
BD19
80BD
2200
Dou
b220
0BD
2230
BD25
00
altered olivine /magnesite
0.0
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1.0
1.2 olivine
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35BD
1500
* ICE
R1BD
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BD19
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I200
0BD
2100
BD22
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2290
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ND
EX* I
CER2
BDCA
RBBD
3000
BD31
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3200
BD34
00BD
1270
O2
BD14
00H
2OBD
2000
CO2
BD23
50BD
2600
* IRR
2R2
700
BD27
00* I
RR3
OLI
ND
EX2
BD19
00R
BD19
80BD
2200
Dou
b220
0BD
2230
BD25
00
0.0
0.2
0.4
0.6
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1.0
1.2
BDI1
000I
RIR
AO
LIN
DEX
LCPI
ND
EXH
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DEX
ISLO
PE1
BD14
35BD
1500
* ICE
R1BD
1750
BD19
00BD
I200
0BD
2100
BD22
10BD
2290
D23
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ND
EX* I
CER2
BDCA
RBBD
3000
BD31
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3200
BD34
00BD
1270
O2
BD14
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2OBD
2000
CO2
BD23
50BD
2600
* IRR
2R2
700
BD27
00* I
RR3
OLI
ND
EX2
BD19
00R
BD19
80BD
2200
Dou
b220
0BD
2230
BD25
00
0.0
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1.0
1.2 hydrated smectites
BDI1
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2100
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2290
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EX* I
CER2
BDCA
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3000
BD31
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3200
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1270
O2
BD14
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2OBD
2000
CO2
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2600
* IRR
2R2
700
BD27
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RR3
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EX2
BD19
00R
BD19
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2200
Dou
b220
0BD
2230
BD25
00
A B
C D
olivine/mafic mixture
SVD on all superpixels (initialize model)
From all superpixels, select the one most dissimilar (Euclidean) to initial model
Perform SVD on single selected superpixel, creating new model
Select the most dissimilar superpixel to the updated model
Perform SVD on set of n superpixels (those already seen)
Repeat for x iterations
