Exploratory unsupervised mapping of CRISM imagery...
Transcript of Exploratory unsupervised mapping of CRISM imagery...
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.
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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
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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