Sparse Superpixel Unmixing of CRISM Hyperspectral Images
1NASA / 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 ([email protected])
Martha S. Gilmore, Wesleyan UniversityBecky Castaño, JPL
Sparse Superpixel Unmixing
• Problem Background
• Sparse Unmixing
• Superpixel Segmentation
• Preliminary Results
2NASA / Calech / JPL / Instrument Software and Science Data Systems
Agenda
MRO (Courtesy NASA/JPL/Caltech)
Motivation
3NASA / Caltech / JPL / Instrument Software and Science Data Systems
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
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multispectral (survey)
hyperspectral (targeted)
Sparse unmixing
• Unmixing with an overcomplete source library
• Linear mixing model
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Mixing coefficients
Overcomplete library of source signals
Gaussian noise
Reconstruction
Constituents
Phyllosilicate
Mafics
Bayesian Unmixing
• Sparsity-inducing exponential prior on mixing coefficients
• Objective function: maximize p(coefficients|data)
• Gradient ascent [similar to Moussaui et al. 2008]
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Controls sparsity
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
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frt00003e12
frt00003fb9
Bayesian Unmixing
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Constituents
Site B reconstruction
Constituents
Mafics
Site A reconstruction
Phyllosilicate
Mafics
MCMC Probabilistic Unmixing
• Gibbs sampler for mixing coefficients, proposal distributions based on multivariate Gaussian
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Sparse Superpixel Unmixing
• Problem Background
• Datasets & Preprocessing
• Sparse Unmixing
• Superpixel Segmentation
• Preliminary Results
10NASA / Calech / JPL / Instrument Software and Science Data Systems
Agenda
MRO (Courtesy NASA/JPL/Caltech)
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
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Image created with code by Mori et al., Courtesy CMU
Superpixel Segmentation
• Felzenszwalb graph partitioning algorithm [Felzenszwalb et al. 2004]
• Compute edge weights using Euclidean distance between spectra
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Superpixel Segmentation
• Felzenszwalb graph partitioning algorithm [Felzenszwalb et al. 2004]
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• Iteratively join segments when there is no evidence of a boundary between them
• 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
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15NASA / Calech / JPL / Instrument Software and Science Data Systems
Superpixel Segmentation
original coarse fine
Mapping Results
• Abundance measure produced by combining mixing coefficients from Olivine, Phyllosilicate library samples
• Evaluated correlation with hand-crafted summary products
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Olivine detections
OLINDEX standard
Phyllosilicate detections
D2300 standard
Mapping Results
• High correlation scores for both minerals, images
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Image Index Segment-ation
Corr. Precis. Recall
3e12 OLIND Coarse 0.87 0.89 0.91
Fine 0.91 0.92 0.83
D2300 Coarse 0.67 0.76 0.55
Fine 0.73 0.80 0.53
3fb8 OLIND Coarse 0.87 0.91 0.86
Fine 0.92 0.94 0.87
Conclusions
• Superpixel segmentation has utility for fast summary data products
• Demonstration of gradient ascent unmixing with sparsity-inducing priors
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MRO (Courtesy NASA/JPL/Caltech)
Future Work
• Superpixel-enhanced endmember extraction
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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
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Future Work
• Superpixel-enhanced endmember extraction
• Endmember superpixels serve as regions of interest for automated feature detection
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Mean spectrum of target region
MCMC Probabilistic Unmixing
21NASA / Calech / JPL / Instrument Software and Science Data Systems
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
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Backup Slides
23NASA / Calech / JPL / Instrument Software and Science Data Systems
Superpixel Segmentation
• Felzenszwalb graph partitioning algorithm [Felzenszwalb et al. 2004]
• Merge contiguous subregions using Euclidean distance between spectra
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?
Superpixel Segmentation
• Felzenszwalb graph partitioning algorithm [Felzenszwalb et al. 2004]
• Merge contiguous subregions using Euclidean distance between spectra
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?
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
1. Sparse unmixing discovers constituents from an overcomplete source library
1. Draft mineralogical maps
Motivation
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Reconstruction
Constituents
Phyllosilicate
Mafics
Phyllosilicate detections
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