CENTER FORSUBSURFACE SENSING AND IMAGING SYSTEMS

CENTER FORSUBSURFACE SENSING AND IMAGING SYSTEMS

April 19, 2007

2007 CenSSIS Site Visit

April 19, 2007

2007 CenSSIS Site Visit

Miguel Vélez-ReyesR2C Sub-thrust Leader

Miguel Vélez-ReyesR2C Sub-thrust Leader

Multi-SpectralDiscrimination

(MSD)

Probe

Multi-BandDetectors

Spectral Sensing and Imaging @ CenSSISSpectral Sensing and Imaging @ CenSSIS

Detectors at different

wavelengths, Yi

Detectors at different

wavelengths, Yi

object

MediumClutter

BroadbandProbe, P

BroadbandProbe, P

Remote Sensing

iiiii λ,wγ,S,λβα,Τλ,Y rrr

Elastic-Scattering Spectroscopy

Cosmic Rays

Spectrograph

Optical System

Laser beamCCD

nvnvns RC

s

maxminminmin ,)2(,...,,| Nnnn

Raman spectroscopy system and signal model

Cosmic Rays

Spectrograph

Optical System

Laser beamCCD

nvnvns RC

s

maxminminmin ,)2(,...,,| Nnnn

Cosmic Rays

Spectrograph

Optical System

Laser beamCCD

nvnvns RC

s

Cosmic Rays

Spectrograph

Optical System

Laser beamCCD

nvnvns RC

s

Cosmic Rays

Spectrograph

Optical System

Laser beamCCD

nvnvns RC

s

maxminminmin ,)2(,...,,| Nnnn

Raman spectroscopy system and signal model

Raman Imaging Spectroscopy

Spectral Sampling

Goals of Spectral Sensing & Imaging (R2C)Estimation, Detection, Classification, or Understanding

Goals of Spectral Sensing & Imaging (R2C)Estimation, Detection, Classification, or Understanding

o Crop health o Chemical composition, pH, CO2

o Metabolic information o Ion concentrationo Physiological changes (e.g., oxygenation)o Extrinsic markers (dyes, chemical tags)

Examples of

Detect: presence of a target characterized by its spectral features or Classify: objects based on features exhibited in or

Understand: object information, e.g., shape or other features based on or . Integrating spatial and spectral domains.

Or

Estimate: probed spectral signature { (x,y,)}

physical parameter to be estimated {(x,y,)}

M

MSSI Research Across ThrustsMSSI Research Across Thrusts

R2: MultispectralPhysics-Based Signal ProcessingFundamental

ScienceFundamentalScience

ValidatingTestBEDsValidatingTestBEDs

L1L1

L2L2

L3L3S4

Bio -Med Enviro -Civil

R3: AlgorithmImplementation

Benthic HabitatMapping

R1: Multispectral Imaging

S1Microscopy,Celular Imaging

Posters

• R2C– R2C p1: Tianchen Shi, Charles DiMarzio (NU), Multi-Spectral Reflectance

Confocal Microscopy on Skin– R2C p6: Sol Cruz-Rivera, Vidya Manian (UPRM), Charles DiMarzio (NU),

Component Extraction from CRM Skin Images– R2C p2: Melissa Romeo, Max Diem (NU), Vibrational Multispectral Imaging of

Cells and Tissue: Monitoring Disease and Cellular Activity– R2C p3: Luis A. Quintero, Shawn Hunt (UPRM), Max Diem (NU), Denoising of

Raman Spectroscopy Signals– R2C p4: Julio Martin Duarte-Carvajalino, Miguel Velez-Reyes (UPRM), Guillermo

Sapiro (UM) Fast Multi-Scale Regularization and Segmentation of Hyperspectral Imagery via Anisotropic Diffusion and Algebraic Multigrid Solvers

– R2C p5: Enid M. Alvira, Miguel Velez-Reyes, Samuel Rosario (UPRM) A Study on the Effect of Spectral Signature Enhancement in Hyperspectral Image Unmixing

• SeaBED– Sea p1: James Goodman, SeaBED: A Controlled Laboratory and Field Test

Environment for the Validation of Coastal Hyperspectral Image Analysis Algorithms

– Sea p2: Carmen Zayas, Spectral Libraries of Submerged Biotoped for Benthic Mapping in Southwestern Puerto Rico

Denoising of Raman Spectroscopy Signals: L. Quintero, S. Hunt, M. Diem

Impulsive Noise Filter

Savitzky-Golay Filter (Smoothing) nx̂

ns1̂ ny

Median Filter7 point window

Low pass Filter

Cosmic Spike Classification

|y[n]-u[n]|>thr

Missing Point Filter

+_ ny nu thr indx nx̂Cosmic Spikes Detection

Wavelet Denoising ns2ˆ++ ++ ns

nx

nR nC

Figure 1. Signal processing system: Impulsive noise filter and two alternatives to reduce the remaining noise (νR[n])

100 200 300 400 500 600 700 800 900 1000

100

120

140

160

180

200

SamplesC

ount

s

s[n]

s[n]+R[n]

100 200 300 400 500 600 700 800 900 1000

100

120

140

160

180

200

Samples

Cou

nts

s[n]s1[n]

s2[n]

500100015002000250030003500230

235

240

245

250

255

260

265

270

275

280

Wavenumber (cm-1)

Cou

nts

Original spectrum

500100015002000250030003500230

235

240

245

250

255

260

265

270

275

280

Wavenumber (cm-1)

Cou

nts

Filtered spectrum

Samples

Cou

nts

Figure 2. Real spectra in blue and filtered signal in red using the impulsive noise filter

Figure 3. Synthetic spectrum with Poisson noise. Estimations of s[n] using the Savitzky-Golay algorithm and Wavelets Shrinkage Estimators

Multi-Spectral Reflectance Confocal Microscopy on Skin: T. Shi, C. DiMarzio

A new multi-spectral reflectance confocal microscopy to achieve sub-celluar functional imaging in skin by utilizing our unique Keck multi-modality microscope is presented. Ex-vivo and phantom experimental results are presented. Further development of this new modality may lead to future clinical applications.

Component Extraction from CRM ImagesComponent Extraction from CRM ImagesS.M. Cruz-Rivera, V. Manian, C. DiMarzioS.M. Cruz-Rivera, V. Manian, C. DiMarzio

Statistical techniques have been applied to extract components (endmembers) from CRM images of the skin.The results are compared with N-FINDR method of pure pixel extraction.Figure below shows the first 4 components from the ICA algorithm for wavelenght of 810nm.

One image from the Original 4-D

matrix ICA Results for CRM data for w = 810 nm

Future work will include, spatial processing for extracting regional features and semi-supervised methods will be implemented to perform endmember extraction

Fast Multi-Scale Regularization and Segmentation of Fast Multi-Scale Regularization and Segmentation of Hyperspectral Imagery via Anisotropic Diffusion and Algebraic Hyperspectral Imagery via Anisotropic Diffusion and Algebraic Multigrid SolversMultigrid Solvers

Grid 0

Grid S

Grid s

.

.

.

.

.

.

V-cycle

1000 , nn UVUVA

Grid 0 Relax Relax

Relax Relax

Grid S, Solve:

Restriction

Restriction Prolongation

Prolongation

Grid s

SSS REA

000 EVV

E : error, R: residual, V: approximated solution

• Julio M. Duarte (UPRM)• Miguel Velez-Reyes (UPRM)• Guillermo Sapiro (UMN)

A Study on the Effect of Spectral Signature A Study on the Effect of Spectral Signature Enhancement in Hyperspectral Image UnmixingEnhancement in Hyperspectral Image Unmixing

• Enid M. Alvira• Miguel Vélez-Reyes• Samuel Rosario

Resolution Enhancement

PCA Filter

Unmixing

SeaBED: Sea p1

• CONCEPT: Assemble a multi-level array of optical measurements, field observations and remote sensing imagery describing a natural reef system

• OBJECTIVE: Provide researchers with data from a fully-characterized test environment for the development and validation of subsurface aquatic remote sensing algorithms

• LEGACY: Utilize scientific publications and web-based distribution to establish Enrique Reef and its associated data as a lasting standard for algorithm assessment

Benthic Measurements

Water Column Measurements

Surface Measurements

Hyperspectral Image Data

UPRM Researchers: J. Goodman, M. Vélez-Reyes, F. Gilbes, S. Hunt, R. Armstrong

SeaBED: Image Collection Campaign in Preparation, Sea p1SeaBED: Image Collection Campaign in Preparation, Sea p1

SeaBED: Spectral Library for Algorithm Validation Sea p2SeaBED: Spectral Library for Algorithm Validation Sea p2

New instrumentation and sampling techniques are being used for the development of spectral libraries required for hyperspectral subsurface unmixing algorithms.

Related Posters

• R1A– R1A p1: D. Goode, B. Saleh, A. Sergienko, M. Teich, Quantum Optical

Coherence Tomography– R1A p2: A. Stern, O. Minaeva, N. Mohan, A. Sergienko, B. Saleh, M. Teich,

Superconducting Single-Photon Dectectors (SSPDs) for OCT and QOCT– R1A p7: M. Dogan, J. Dupuis, A. Swan, Selim Unlu, B. Goldberg, Probing DNA

on Surfaces Using Optical Interference Techniques• R2B

– R2B p3: Amit Mukherjee, Badri Roysam, Interest-points for Hyperspectral Images

• R2D– R2D p8: Karin Griffis, Maja Bystrom, Automatic Object-Level Change

Interpretation for Multispectral Remote Sensing Imagery• R3A

– R3A p5: Carolina Gerardino, Wilson Rivera, James Goodman, Utilizing High-Performance Computing to Investigate Performance and Sensitivity of an Inversion Model for Hyperspectral Remote Sensing of Shallow Coral Ecosystems

• R3B– R3B p6: Samuel Rosario-Torres, Miguel Velez-Reyes, New Developments and

Application of the MATLAB Hyperspectral Image Analysis Toolbox