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Polarimetric Modeling and Visualization

Description:

TECHNOLOGY AREA(S): Electronics

OBJECTIVE: Develop models and visualization tools for multi-modal polarimetric imagery to enhance detection, recognition and identification.

DESCRIPTION: Polarization imaging provides improvements in Detection, Recognition, and Identification (DRI) of objects through contrast enhancements and clutter suppression.The polarimetric data sets are multi-modal and include polarization magnitude, orientation, and conventional intensity information.The physics behind polarimetric signatures is complex and is dependent on the material properties of the scene elements, the shape and the surface characteristics of the objects in the scene, the look angle of the sensor, the relative position of any illumination sources, the relative temperatures of the objects in the scene and the background, and background conditions including weather, cloud cover and the presence and temperature of objects in line of sight of the imaged field of view.Although a first principle model that includes all of these considerations would provide a means to analyze situations and mission parameters in which the enhancement from polarization sensing is optimized, an accurate physics-based model is not practicable.Instead, we seek a hybrid empirical/physical model based on physical modeling and enough testing to develop empirical detection models that can be used to set the mission parameters.Visualization tools are needed to assist a human observer in rapidly detecting and identifying threats in a scene.What is needed is the modeling to predict what should be happening and the visualization tools to achieve successful DRI.The proposer shall collect or acquire infrared polarimetric data (short-wave, mid-wave, or long-wave) under a variety of conditions and use this data for model development.Detection performance must be quantified through established published metrics.The empirical detection model would ultimately be used for some or all elements of the DRI process and may be implemented into a mission planning tool. The proposer shall develop visualization tools by exploiting the multi-modal nature of polarimetric imaging and thus improve DRI.

PHASE I: Develop and design empirical/physical process for optimizing multi-modal polarimetric image collection with a limited set of parameters.A complete set of parameters will be identified and the subset for Phase I development delineated.Analysis to demonstrate the efficacy of the model is sufficient for a Phase I demonstration.A path for algorithm improvement in Phase II will be established.

PHASE II: Execute the plan developed in Phase I and continue algorithm development. The complete set of mission parameters will be included in the model.A user interface will be developed to facilitate easy input of the parameters.Sufficiently large data sets for development and test will be collected.The algorithms will be implemented on a real-time computing platform and demonstrated.

PHASE III: Provide a validated approach for predicting polarimetric camera performance for a variety of military applications and programs.For military applications, this technology will contribute to route clearance, countermine operations, drone-based ISR, and missile seekers.

KEYWORDS: infrared, polarimetric imaging, image enhancement, machine learning, artificial intelligence

References:

1. Collin Bright et al., “Long Wave Infrared (LWIR) Polarization with Reflective Band Camera for Enhanced Detection and Identification of Surface Hazards in Cluttered Scenes,” Global EOD Symposium, Aug 2019; 2. Felton, M., Gurton, K. P., Pezzaniti, J. L., Chenault, D. B., and Roth, L. E., “Measured comparison of the crossover periods for mid- and long-wave IR (MWIR and LWIR) polarimetric and conventional thermal imagery,” Opt. Exp., Vol. 18. Issue 15, pp. 15704-15713 (2010).; 3. T. J. Rogne, F. G. Smith, and J. E. Rice, "Passive Target Detection using Polarized Components of Infrared Signatures," Proc. SPIE 1317, Polarimetry: Radar, Infrared, Visible, Ultraviolet, and X-Ray, R. A. Chipman ed. 242-251 (1990).; 4. Tyo, J. Scott, Goldstein, D. H., Chenault, D. B., and Shaw, J. H., "Review of passive imaging polarimetry for remote sensing applications," Appl. Opt. 45, 5453-5469 (2006).; 5. S. Tyo, B.M. Ratliff, J. Boger, W. Black, D. Bowers, M. Fetrow, “The effects of thermal equilibrium and contrast in LWIR polarimetric images”, Opt. Express, vol. 15, no. 23 Nov. (2007).; 6. K.P. Gurton, M. Felton, “Detection of disturbed earth using passive LWIR polarimetric imaging” Proc SPIE Optics and Photonics Conference, San Diego, Ca. August 2-6, (2009).; 7. “Summary of the 2018 department of defense artificial intelligence strategy”, Accessible from https://media.defense.gov/2019/Feb/12/2002088963/-1/-1/1/SUMMARY-OF-DOD-AI-STRATEGY.PDF, February 2019.; 8. U.S. Department of Homeland Security, “Automatic Identification System Overview”, United States Coast Guard. 17 November 2018 https://www.navcen.uscg.gov/?pageName=aismain.; 9. Bishop, Christopher.Pattern Recognition and Machine Learning.New York, Springer-Verlag, 2006 https://www.springer.com/us/book/9780387310732.

Additional Guidance A20-121

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