Prof. Miguel V lez-Reyes Lab. for Appl. Remote Sensing and Image Proc. Univ. of Puerto Rico at Mayaguez S. Rosario-Torr - PowerPoint PPT Presentation

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Prof. Miguel V lez-Reyes Lab. for Appl. Remote Sensing and Image Proc. Univ. of Puerto Rico at Mayaguez S. Rosario-Torr

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    1. Prof. Miguel Vlez-Reyes Lab. for Appl. Remote Sensing and Image Proc. Univ. of Puerto Rico at Mayaguez S. Rosario-Torres, J. Goodman, V. Manian Hyperspectral Image Exploitation for Ship Detection

    2. HSI is a Key Technology Environmental monitoring NASA Flora CHRIS (Compact High Resolution Imaging Spectrometer) Proba (ESA), HERO (Canadian), SPECTRA (ESA), and EnMAP (German) missions. DoD Situational Awareness AFRL/Raytheon TacSat 3 ARTEMIS Space Exploration NASA MRO Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) NASA Moon Mineral Mapper (M3) mission

    3. The problem of interest

    4. Challenges How to combine different modalities to optimize information extraction Dynamic High dimensionality Variability introduced by Changes in atmospheric conditions Differences in illumination, orientation, etc Variable unstructured clutter in standoff applications Mixed signatures (clutter and threat)

    5. Proposed Approach Powerful Methods for Constructing Detectors and Classifiers Kernel-based methods Support Vector Machines (SVM) Adaptive Boosting Techniques AdaBoost Dimensionality Reduction and Feature Extraction Invariant features Optimize sensor combinations Adaptation and Nonlinear Learning Changing environment Robust detection of new classes of targets Optimize sensor combinations Explotion of new and powerful methods. Learn from the data differences between threat and non threat situations. Novel methods for dimensionality reduction. Techniques that reduce the amount of data to transmit over sensor networks. Compresed sensing has shown that the information required to separate object classes with significant variability induced by chages in pose, illumination, etc.. Is summarized in few dimensions with techniques that are object indepentent. Another challeging direction is the detection of new classes of explosives that appear as annomalous signature and the automated re-training of the classifier.Explotion of new and powerful methods. Learn from the data differences between threat and non threat situations. Novel methods for dimensionality reduction. Techniques that reduce the amount of data to transmit over sensor networks. Compresed sensing has shown that the information required to separate object classes with significant variability induced by chages in pose, illumination, etc.. Is summarized in few dimensions with techniques that are object indepentent. Another challeging direction is the detection of new classes of explosives that appear as annomalous signature and the automated re-training of the classifier.

    6. Our Expertise Hyperspectral image processing Vector/Multichannel image processing Classification and detection in high dimensional feature spaces Nonlinear Signal Processing Machine learning Automatic target recognition

    7. Geometric PDE Processing of HSI: Object Oriente Approaches Improve Target Background Contrast Improve Detection and Classification

    8. Unsupervised Unmixing: Target Clutter Separation

    9. Algorithm Implementation: Solutionware

    12. Alternative Computational Platforms for Hyperspectral Image Processing Problem of Interest: Study alternative platforms where hyperspectral algorithms may be mapped efficiently, Algorithm Unsupervised unmixing Platforms Massively parallel processors CUDA GPGPUs Field programmable gate arrays - FPGAs Features: Embarrasingly parallel structure Tune application to platforms.

    18. 2007 Puerto Rico Hyperspectral Mission*

    19. Space Information Laboratory Provides Satellite Reception Capabilities: Investment of approximately $1.3M in Infrastructure. Only university under the U.S. flag licensed to receive LANDSAT 7. Trains students in station operations, programming, image processing, satellite tracking, state of the art high data rate communications, and more. S band station receives NOAA telemetry (12, 14, 15, 16, 17) and SeaWiFS data. X-band station receives LANDSAT 7, RADARSAT 1 and MODIS (Terra and Aqua Satellite). NASA FUSE Ground Control Station SIL provides imagery for the other TCESS components.

    20. Initial Focus Geometric PDEs for spectral/spatial integration for image segmentation Hyperspectral Target Detection Spectral Libraries (Collaboration with UH) High spatial resolution HSI Sub-pixel Target Detection Analysis of MODIS/AVHRR Imagery over the Caribbean

    21. Synergy with CIMES Collaborators UH: Design and construction on hyperspectral airborne imagers Hawaii Space Flight Lab access to space UAF Real time processing Builds on UPRMs expertise: Over 10 years of work in the area Over 100 peer reviewed publications 2 Book chapters Research sponsored by: NSF, DoD, DHS AFRL, NOAA, NASA, NGA

    22. Synergy with Existing Centers: DHS, NSF