Sparse Superpixel Unmixing of CRISM Hyperspectral Images 1 NASA / Caltech / JPL / Instrument Software and Science Data Systems Images courtesy NASA / Caltech

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

NASA / Caltech / JPL / Instrument Software and Science Data Systems 4

multispectral (survey)

hyperspectral (targeted)

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

Bayesian Unmixing

• Sparsity-inducing exponential prior on mixing coefficients

• Objective function: maximize p(coefficients|data)

• Gradient ascent [similar to Moussaui et al. 2008]


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


frt00003e12

frt00003fb9

Bayesian Unmixing


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


Sparse Superpixel Unmixing

• Problem Background

• Datasets & Preprocessing

• Sparse Unmixing

• Superpixel Segmentation

• Preliminary Results


Agenda


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


Image created with code by Mori et al., Courtesy CMU


• Felzenszwalb graph partitioning algorithm [Felzenszwalb et al. 2004]

• Compute edge weights using Euclidean distance between spectra





• Iteratively join segments when there is no evidence of a boundary between them



• Compare strongest joining edge to weakest edge of spanning trees

• Weighted with an additive bias prevents small regions




original coarse fine

Mapping Results

• Abundance measure produced by combining mixing coefficients from Olivine, Phyllosilicate library samples

• Evaluated correlation with hand-crafted summary products


Olivine detections

OLINDEX standard

Phyllosilicate detections

D2300 standard

Mapping Results

• High correlation scores for both minerals, images


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



Future Work

• Superpixel-enhanced endmember extraction


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


Mean spectrum of target region

MCMC Probabilistic Unmixing


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


Backup Slides




• Merge contiguous subregions using Euclidean distance between spectra


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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

Documents

Sparse Superpixel Unmixing of CRISM Hyperspectral Images 1 NASA / Caltech / JPL / Instrument Software and Science Data Systems Images courtesy NASA / Caltech