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The role of physical modeling and scene simulation in support of space based remote sensing

The role of physical modeling and scene simulation in support of space based remote sensing. Dr. John Schott, Scott Brown, & Mike Richardson Digital Imaging and Remote Sensing Laboratory Rochester Institute of Technology Center for Imaging Science 54 Lomb Memorial Drive Rochester, NY 14623

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The role of physical modeling and scene simulation in support of space based remote sensing

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  1. The role of physical modeling and scene simulation in support of space based remote sensing Dr. John Schott, Scott Brown, & Mike Richardson Digital Imaging and Remote Sensing Laboratory Rochester Institute of Technology Center for Imaging Science 54 Lomb Memorial Drive Rochester, NY 14623 schott@cis.rit.edu (716) 475-5170

  2. Users - Applications SIG modeling is designed to support • New sensor design • design trades • expected performance evaluation • provide sample sensor data in support of early algorithm & data processing development

  3. Users - Applications • Tasking aid • sensor selection • acquisition conditions

  4. Users - Applications • Training • end-to-end phenomenology • sensors & algorithm training wind speed emissivity

  5. Users - Applications • Analyst aid • hypothesis test-compare real vs. synthetic • model-based exploitation tools SIG Case 1 Real Image SIG Case 2

  6. Users - Applications • Algorithm development • provide more robust data sets • better evaluate performance when “truth” is known • provide good metrics for tuning complex algorithms

  7. Sensor - System Design Spatial/Spectral/Radiometric Design Trades High resolution, low SNR, monochromatic SNR Moderate resolution, high SNR, monochromatic 1/x Moderate resolution, high SNR, 3 spectral bands 1/ Low resolution, moderate SNR, 30 spectral bands

  8. chemical structure absorption features Phenomenology Studies • Extension of the image chain from the chemical and physical characteristics of targets or components of targets all the way through the final image record-signature.

  9. Phenomenology Studies surface structure BRDF features energy matter interaction model apparent reflectance (linear and nonlinear)

  10. thermodynamic model temperature N Phenomenology Studies

  11. Modeled Signature Observed Signature Error Phenomenology Studies • Use error as an indicator of our lack of knowledge along the phenomenology chain. Use our ability to sample along the chain to identify sources of errors and improve phenomenology understanding. 

  12. Phenomenology Studies atmospheric propagation spectral radiance and target background at sensor interaction model 8 7 6 5 4 3 2 1 0 Altitude (km) -25 -20 -15 -10 -5 0 5 10 15 20 25 Temperature (°C)

  13. Phenomenology Studies sensor and observed platform models (recorded image)

  14. Modeling Tools • Scene Simulators:DIRSIG, Genesis, Creation, GTSIG, CameoSim, IRMA • Atmospheric Radiative Transfer: MODTRAN/FASCODE • Thermal Models • Therm: slab - high environmental coupling • HeatEx: lateral conduction • MUSES: full internal 3D propagation

  15. Modeling Tools • Scene Simulators: • Energy Mater Interaction • Reflectance: Lambertion Full BRDF • Transmission: None Planar Volumetric Insert BRDF GRAPHIC CJRS

  16. Modeling Tools • Scene Simulators: • Scene Construction • DEMS Facets • Object Models Viewpoint, ???????? • Scene Construction from imagery • Backgrounds: Genesis • 3D objects: HARRIS???, SocketSet

  17. Modeling Tools • Sensor Models • Spectral response: single band per band per detector element • Spatial sampling: GSD PSF per scene, PSF per band, PSF per detector • opto-mechanical: projection notional detector element on focal plane • temporal: instantaneous linked to platform model linked to platform and scene clock • noise: Gaussian structural per band per detector • calibration • Platform Model (x,y,z, w, f, k) • Fixed time varying spatial time varying spatial and angular • full orbital dynamics, jitter

  18. Modeling Tools • System Models • Scene • Sensor • Platform geometry, ?? , communications control • Communications com link time, tasking, downlink, compression • Ground processing • Performance Models:FASSP, SPECTRA, SCITAC MODEL??? • Algorithm • Metric

  19. Meteorological Conditions Direct Insolation Sky Exposure Diffuse Insolation Diffuse Insolation Object Geometry Material Properties Scene Geometry Themodynamic Optical Properties Ray Tracer Plume Description Thermal Model Weather Database DIRSIG Executive Plume Model Reflectance Model Radiometry Model Focal Plane Description Atmospheric Database Sensor Model Platform Description MODTRAN SERTRAN FASCODE Broadband, multi, hyper or ultraspectral imagery Synthetic Image Generation DIRSIG

  20. DIRSIG

  21. Future Directions Phenomenology • Improvements • Many - many incremental • Cross fertilization • Target - Background interaction (i.e. wind shadows) • More rigorous backgrounds (i.e. treat background like targets) • Spatial and Spectral variability (correlation) • Within material type • Transition regions • False alarms (variability - objects) • New Phenomena • Polarization • LIDAR • In water radiative transfer

  22. Future Directions Scene construction, scene rendering, & model interface • Use of GIS and image data to drive SIG • Terrain • Image derived material maps & 3D objects • Use of ancillary data • Computing • Engineering code -> Modern code • Code designed for speed • Parallel processing • Information extraction from existing imagery • Background classes • 3D objects • Texturing • Spectral and Spatial databases (using atmospheric inversion)

  23. Future Directions • Scene construction, scene rendering, & model interface • Large area SIG databases for many scenarios • Users make only small changes • User Interface • Multi-level: sensor engineer, analyst, phenomenologist • Error checking: consistency, range limits … • Preset defaults: scenario, sensor, mission …

  24. Future Directions • Multilevel Rendering & Visualization from common databases • Image maps & icons: command level planning • Visual fly through: training • Full up radiometry: algorithm development & test • End to end system simulations from concept design to on orbit lifetime trades • Scene simulators • Sensor models • Orbit: bus, power, duty cycles • Tasking • Communications: com links, compression • Processing: calibration algorithms • Product generation • Dissemination • Performance prediction • Life cycle

  25. Future Directions Algorithm Development • Use of SIG models to control (threshold) parameters in conventional algorithms • Incorporation of physics into algorithms and models • Use of SIG tools to train ATR algorithms

  26. Simulation Modeling is Coming of Age

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