ALERT: Awareness and Localization of Explosives-Related Threats

1. Prof. Miguel Vélez-Reyes Univ. of Puerto Rico at Mayaguez N. Santiago, V. Manian, UPRM E. Miller, Tufts D. Castañón, W.C. Karl, BU F3-D: Multimodal Pattern Recognition ALERT: Awareness and Localization of Explosives-Related Threats

2. The problem of interest IR Hyperspectral Pattern Recognition System THz and Microwave Probes X-ray Imaging • We want to • Increase detection rates • Decrease false alarms with high throughput for standoff and portal-based detection systems

3. Challenges • 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) • Integration of human in the decision process

4. Proposed Approach • Powerful Methods for Constructing Detectors and Classifiers • Kernel-based methods • Support Vector Machines (SVM) • Adaptive Boosting Techniques • AdaBoost • Dimensionality Reduction • Invariant features • Adaptation • Changing environment • Robust detection of new classes of explosives • Integration with human operator • Decision pre-processors

5. Our Expertise • Automatic target recognition and optimal sensor management • Classification and detection in high dimensional feature spaces • Hyperspectral image processing • Novel methods for wide range of problems across many application areas in image formation and segmentation from multimodal data • Geometric methods • Probabilistic modeling • Ill-posed inverse problems • Optimization and computation

6. Spectral Data Pattern Recognition as Detection Aids • Problem of Interest: Automated tools to rapidly process large volumes of data • Tailored to focus attention of human operators when more information is needed • Desired feature: • Classify with great confidence whenever possible • Identify ambiguous cases where additional information is needed (sensor management!) • Results: New theories for classifiers that determine when additional information is needed • Extensions of kernel support vector machines and adaptive boosting • Work in concert with human-in-the-loop • Applied to medical diagnosis Decision Regions with ambiguous class

7. Positive Matrix Factorization Unsupervised Unmixing: Target Clutter Separation Endmember Determination Endmember Signatures Abundance Estimation Hyperspectral Image Unsupervised Unmixing Abundance Maps

8. Alternative 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 • Preliminary Results: Implementation of Image Space Reconstruction Algorithm for abundance estimation on FPGAs and CUDA has resulted in reduction of three orders of magnitude in execution time. • Tune application to platforms.

9. Novel HSI Spatial/Spectral Processing: Geometric PDE Processing of HSI SEBASS Image • Improve Target Background Contrast • Improve Detection and Classification

10. Year 1 Work Plan • Initial projects: • Investigated kernel-based methods and adaptive boosting techniques for constructing and updating classifiers • Extension of SVM adaptive classifier developed for biomedical applications • Threats, non-threats and ambiguous objects • Unsupervised unmixing (close collaboration with F2A) • Speed up using hardware implementations • Integration of libraries, a priori information and other data sources • Fusion of multi-sensor classification for portal applications • Initial focus on luggage inspection (Collaboration with F3A) • Maintain close relationship with industrial partners via constant personnel exchange