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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
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