Athermalizing Multispectral Infrared Imagery - A Total System Approach

1. Athermalizing Multispectral Infrared Imagery - A Total System Approach UNCLASSIFIED Randy Zywicki McKinney, Texas 972-952-6293 972-952-6267 (fax) zywicki@raytheon.com UNCLASSIFIED Information contained on this sheet is restricted under the International Traffic in Arms Regulations (ITAR) for viewing only by United States citizens or permanent legal residents. Raytheon Proprietary 1

2. Multispectral - What Is It and Why Do I Care? • Spectral imaging adds useful information content • Used to enhance spectral anomalies and suppress clutter Broadband LWIR Image Fugitive Gas Industrial Facility Multispectral Image

3. Multispectral - What Is It and Why Do I Care? Definitions Multispectral: ~2-50 spectral channels Hyperspectral: ~50-500 spectral channels Ultraspectral: ~>500 spectral channels Spectral imaging implies that information from different spectral channels is combined to enhance spectral features. Military Applications Camouflage detection Chemical Agents detection Rocket motor detection & ID Civilian Applications Forestry & land use management Geology Industrial monitoring Hazardous spill mapping & management

4. Multispectral Programs Within Raytheon ES • Air Combat & Strike • Tactical • Surveillance and Reconnaissance Systems SAFEGUARD MFSSS SRS Programs

5. So Why Does it Work? • In the thermal infrared: • Targets of interest often have spectral anomalies in their emissivity. • Most background clutter behaves as a graybody. • Signal to Clutter Ratio (SCR) can be enhanced by exploiting the spectral anomalies and suppressing the background clutter. • Multispectral and Hyperspectral sensors provide the spectral discrimination needed to exploit the spectral anomalies.

6. Example - Chemical Agent Detection 300 K Background ‘G’ Agent Spectra Multispectral Sensor Response

7. So Why Don’t We All Use It? System complexity, size, weight and cost Solutions: Recent customer focus on transitioning the technology from strategic to tactical systems. MFSSS, SAFEGUARD and JOANNA programs are examples. Historically limited by data bandwidth and required real-time processing requirements Solutions: DSP, ASIC and FPGA technology makes real time mutispectral image processing feasible. Lack of robust, automatic image processing algorithms Signals are temperature dependant Atmosphere is unpredictable Background Clutter can overwhelm the signal of interest Solutions?: Athermalized Imagery & Atmospheric Corrections This is the subject of the remainder of the presentation.

8. ‘Whole system’ functional flow model: I Thought That This Was a Systems Paper! Sensors Phenomenology Emerging Algorithms Mature Algorithms Notice that good spatial/spectral registration and radiometric calibration is required for this process to work

9. The Problem - An Unpredictable Signal • Variations in: • temperature, • emissivity and • atmosphere • mean that the signal is not predictable, which makes spectral training data hard to use for ATR/ATC algorithms 330 K Blackbody After Atmospheric Transmission 300 Kelvin 270 Kelvin

10. First - Fix The Atmospheric Effects: Approximate Atmospheric Correction • Choose graybodies from the scene areas as uncalibrated ground control points, e.g. water, grass, rooftops. • Estimate the temperature and emissivity of each control point. • Use linear regression to estimate atmospheric transfer function. Ground control point spectra at sensor Estimated ground control point spectra at ground • Method also compensates for imperfect radiometric and spectral calibration

11. Athermalization - De-Planckifying the Imagery • Estimate the background temperature and emissivity for each pixel using least squares regression • Subtract or divide by the graybody background radiance • Residual spectral image contains signal+noise with the clutter removed • This does not increase signal or decrease noise but it: • Suppresses clutter • Standardizes target spectra for better matched filtering • Whitens the noise spectra

12. Case Study - Fugitive Factory Emissions Target and Background Spectra Preprocessed Imagery Single Band SCR= 0.22 Roof Methanol Signal Clutter 2D Scattergram

13. Athermalized Imagery Athermalized Imagery Single Band SCR=2.24 10X SCR Improvement Roof Clutter Signal Methanol Target and Background Spectra 2D Scattergram

14. Athermalization Byproduct - Temperature and Emissivity Images True Temperature Image Emissivity Image

15. Image Classification Results Well discriminated methanol plume • Pixels are classified as: • Plume Class • Rooftop Class • Background Class • Very low FAR Correctly classified galvanized rooftops

16. Summary & References • Multispectral imaging is a viable technology for tactical systems • Developing robust image processing algorithms is a key challenge • Removing the temperature dependence of the imagery stabilizes classification algorithms • Radiometric calibration, approximate atmospheric corrections and athermalization algorithms work together to suppress clutter by 10X References: Eismann, M.T. et al, “Target detection in desert backgrounds: infrared hyperspectral measurements and analysis”, Proceedings of the SPIE, Vol. 2561, September 1995. Schwartz, Craig R. et al., “Thermal multispectral detection of military vehicles in vegetated and desert backgrounds”, Proceedings of the SPIE, Vol. 2742, June 1996. Kealy, Peter S and Hook, Simon J., “Separating Temperature and Emissivity in Thermal Infrared Multispectral Scanner Data: Implications for Recovering Land Surface Temperatures”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 31 No. 6, November 1993. Zywicki, R.W. “Radiometric Calibration Of An Airborne Chemical Imager”, Proceedings of the SPIE Symposium on Air Monitoring and Detection of Chemical and Biological Agents, 1998.