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CENTER FOR SUBSURFACE SENSING AND IMAGING SYSTEMS

CENTER FOR SUBSURFACE SENSING AND IMAGING SYSTEMS. Probe. Miguel Vélez-Reyes R2C Sub-thrust Leader. Multi-Band Detectors. Multi-Spectral Discrimination (MSD). April 19, 2007 2007 CenSSIS Site Visit. Detectors at different wavelengths, Y i. Broadband Probe, P.

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CENTER FOR SUBSURFACE SENSING AND IMAGING SYSTEMS

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  1. CENTER FORSUBSURFACE SENSING AND IMAGING SYSTEMS Probe Miguel Vélez-ReyesR2C Sub-thrust Leader Multi-Band Detectors Multi-Spectral Discrimination (MSD) April 19, 2007 2007 CenSSIS Site Visit

  2. Detectors at different wavelengths, Yi Broadband Probe, P Elastic-Scattering Spectroscopy Remote Sensing Medium Clutter object Spectral Sensing and Imaging @ CenSSIS Raman Imaging Spectroscopy

  3. Spectral Sampling

  4. Goals of Spectral Sensing & Imaging (R2C)Estimation, Detection, Classification, or Understanding Estimate:probed spectral signature{ (x,y,)} physical parameter to be estimated {(x,y,)}   M • Crop health • Chemical composition, pH, CO2 • Metabolic information • Ion concentration • Physiological changes (e.g., oxygenation) • 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

  5. S1 R1: Multispectral Imaging MSSI Research Across Thrusts Bio - Med Enviro - Civil Microscopy, Celular Imaging L3 L3 Benthic Habitat Mapping S4 Validating Validating L2 L2 TestBEDs TestBEDs R2: Multispectral Physics-Based Signal Processing Fundamental Fundamental L1 L1 Science Science R3: Algorithm Implementation

  6. 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

  7. Savitzky-Golay Filter (Smoothing) Impulsive Noise Filter + + + + Wavelet Denoising Cosmic Spikes Detection Missing Point Filter Cosmic Spike Classification |y[n]-u[n]|>thr indx thr _ + Median Filter 7 point window Low pass Filter Denoising of Raman Spectroscopy Signals: L. Quintero, S. Hunt, M. Diem Figure 1. Signal processing system: Impulsive noise filter and two alternatives to reduce the remaining noise (νR[n]) 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

  8. 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.

  9. Component Extraction from CRM ImagesS.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

  10. Fast Multi-Scale Regularization and Segmentation of Hyperspectral Imagery via Anisotropic Diffusion and Algebraic Multigrid Solvers Grid S V-cycle . . . Grid S, Solve: Grid s Prolongation . . . Restriction Grid s Relax Relax Prolongation Restriction Grid 0 Relax Relax Grid 0 E : error, R: residual, V: approximated solution • Julio M. Duarte (UPRM) • Miguel Velez-Reyes (UPRM) • Guillermo Sapiro (UMN)

  11. A Study on the Effect of Spectral Signature Enhancement in Hyperspectral Image Unmixing Resolution Enhancement Unmixing PCA Filter • Enid M. Alvira • Miguel Vélez-Reyes • Samuel Rosario

  12. Hyperspectral Image Data Surface Measurements Water Column Measurements Benthic Measurements UPRM Researchers: J. Goodman, M. Vélez-Reyes, F. Gilbes, S. Hunt, R. Armstrong 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

  13. SeaBED: Image Collection Campaign in Preparation, Sea p1

  14. SeaBED: 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.

  15. 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

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