1. Sparse Superpixel Unmixing of CRISM Hyperspectral Images 1 NASA / Caltech / JPL / Instrument Software and Science Data Systems Images courtesy NASA / Caltech JPL / Brown University. This presentation Copyright 2009 California Institute of Technology. US Government Support Acknowledged. David R. Thompson, JPL (David.R.Thompson@jpl.nasa.gov) Martha S. Gilmore, Wesleyan University Becky Castaño, JPL

2. Sparse Superpixel Unmixing Problem Background Sparse Unmixing Superpixel Segmentation Preliminary Results 2 NASA / Calech / JPL / Instrument Software and Science Data Systems Agenda MRO (Courtesy NASA/JPL/Caltech)

3. Motivation 3 NASA / Caltech / JPL / Instrument Software and Science Data Systems

4. Motivation “Intelligent Assistant” for data mining, fast image analysis Tactical observation selection Detection of anomalous or important mineralogy Challenges: Source constituents unknown High signal to noise Sparse unmixing Recovers constituents from an overcomplete source library Superpixel segmentation speeds results for whole images NASA / Caltech / JPL / Instrument Software and Science Data Systems 4 multispectral (survey) hyperspectral (targeted)

5. Sparse unmixing Unmixing with an overcomplete source library Linear mixing model NASA / Calech / JPL / Instrument Software and Science Data Systems 5 Mixing coefficients Overcomplete library of source signals Gaussian noise Reconstruction Constituents Phyllosilicate Mafics

6. Bayesian Unmixing Sparsity-inducing exponential prior on mixing coefficients Objective function: maximize p(coefficients|data) Gradient ascent [similar to Moussaui et al. 2008] NASA / Calech / JPL / Instrument Software and Science Data Systems 6 Controls sparsity

7. Datasets and Preprocessing Compact Reconnaissance Imaging Spectrometer (CRISM) images of Nili Fossae region “Full-resolution targeted” images frt00003e12, frt00003fb9 (233 bands in 1.0 to 2.5 micrometer range) Atmospheric correction with Volcano division NASA / Calech / JPL / Instrument Software and Science Data Systems 7 frt00003e12 frt00003fb9

8. Bayesian Unmixing NASA / Calech / JPL / Instrument Software and Science Data Systems 8 Constituents Site B reconstruction Constituents Mafics Site A reconstruction Phyllosilicate Mafics

9. MCMC Probabilistic Unmixing Gibbs sampler for mixing coefficients, proposal distributions based on multivariate Gaussian NASA / Calech / JPL / Instrument Software and Science Data Systems 9

10. Sparse Superpixel Unmixing Problem Background Datasets & Preprocessing Sparse Unmixing Superpixel Segmentation Preliminary Results 10 NASA / Calech / JPL / Instrument Software and Science Data Systems Agenda MRO (Courtesy NASA/JPL/Caltech)

11. Superpixel Segmentation “Superpixels” are image segments corresponding to homogeneous sub-regions [Ren et al. 2003, Mori et al 2005] Potential advantages: Noise reduction Faster processing NASA / Calech / JPL / Instrument Software and Science Data Systems 11 Image created with code by Mori et al., Courtesy CMU

12. Superpixel Segmentation Felzenszwalb graph partitioning algorithm [Felzenszwalb et al. 2004] Compute edge weights using Euclidean distance between spectra NASA / Calech / JPL / Instrument Software and Science Data Systems 12

13. Superpixel Segmentation Felzenszwalb graph partitioning algorithm [Felzenszwalb et al. 2004] NASA / Calech / JPL / Instrument Software and Science Data Systems 13 Iteratively join segments when there is no evidence of a boundary between them

14. Felzenszwalb graph partitioning algorithm [Felzenszwalb et al. 2004] Superpixel Segmentation Compare strongest joining edge to weakest edge of spanning trees Weighted with an additive bias prevents small regions NASA / Calech / JPL / Instrument Software and Science Data Systems 14

15. 15 NASA / Calech / JPL / Instrument Software and Science Data Systems Superpixel Segmentation originalcoarsefine

16. Mapping Results Abundance measure produced by combining mixing coefficients from Olivine, Phyllosilicate library samples Evaluated correlation with hand-crafted summary products NASA / Calech / JPL / Instrument Software and Science Data Systems 16 Olivine detections OLINDEX standard Phyllosilicate detections D2300 standard

17. Mapping Results High correlation scores for both minerals, images NASA / Calech / JPL / Instrument Software and Science Data Systems 17 ImageIndexSegment- ation Corr.Precis.Recall 3e12OLINDCoarse0.870.890.91 Fine0.910.920.83 D2300Coarse0.670.760.55 Fine0.730.800.53 3fb8OLINDCoarse0.870.910.86 Fine0.920.940.87

18. Conclusions Superpixel segmentation has utility for fast summary data products Demonstration of gradient ascent unmixing with sparsity-inducing priors NASA / Calech / JPL / Instrument Software and Science Data Systems 18 MRO (Courtesy NASA/JPL/Caltech)

19. Future Work Superpixel-enhanced endmember extraction NASA / Calech / JPL / Instrument Software and Science Data Systems 19 Traditional endmember extraction, SMACC algorithm (noise artifacts, 3/5 actual classes detected) New automatic method based on superpixels (5/5 actual classes detected) “Ground truth” classes from geologist classification 1 2 3 4 5 1 3 2 5 4 3 2 1 2 2 5 3 4 3

20. Future Work Superpixel-enhanced endmember extraction Endmember superpixels serve as regions of interest for automated feature detection NASA / Calech / JPL / Instrument Software and Science Data Systems 20 Mean spectrum of target region

21. MCMC Probabilistic Unmixing 21 NASA / Calech / JPL / Instrument Software and Science Data Systems

22. Acknowledgements Thanks to Brown University for the CAT/ENVI tools used in atmospheric correction and reprojection Sponsorship by NASA AMMOS / MGSS Multimission Ground Support hyperspectral.jpl.nasa.gov NASA / Calech / JPL / Instrument Software and Science Data Systems 22

23. Backup Slides 23 NASA / Calech / JPL / Instrument Software and Science Data Systems

24. Superpixel Segmentation Felzenszwalb graph partitioning algorithm [Felzenszwalb et al. 2004] Merge contiguous subregions using Euclidean distance between spectra NASA / Calech / JPL / Instrument Software and Science Data Systems 24 ?

25. Superpixel Segmentation Felzenszwalb graph partitioning algorithm [Felzenszwalb et al. 2004] Merge contiguous subregions using Euclidean distance between spectra NASA / Calech / JPL / Instrument Software and Science Data Systems 25 ?

26. Superpixel Segmentation Felzenszwalb graph partitioning algorithm [Felzenszwalb et al. 2004] Merge contiguous subregions using Euclidean distance between spectra NASA / Calech / JPL / Instrument Software and Science Data Systems 26

27. 1. Sparse unmixing discovers constituents from an overcomplete source library 1. Draft mineralogical maps Motivation NASA / Caltech / JPL / Instrument Software and Science Data Systems 27 Reconstruction Constituents Phyllosilicate Mafics Phyllosilicate detections