Megacollect 2004: Hyperspectral Collection Experiment of Terrestrial Targets and Backgrounds of the RIT Megascene Megacollect 2004: Hyperspectral Collection Experiment of Terrestrial Targets and Backgrounds of the RIT Megascene N.G. Raqueno, L.E. Smith, D.W. Messinger, C. Salvaggio, R.V. Raqueno, and J.R. Schott Digital Imaging and Remote Sensing Laboratory Chester F. Carlson Center for Imaging Science Rochester Institute of Technology Rochester, New York

MegaCollect: Topics ObjectiveObjective OpportunityOpportunity Collection PlansCollection Plans –Sensors, Flightplans, Experiments, Equipment Collection ResultsCollection Results Data ManagementData Management –Mobile GIS –Spectral Library Future DirectionFuture Direction

MegaCollect : Objective Many RIT research programs are in need of data for:Many RIT research programs are in need of data for: –Algorithm development and testing –Scene modeling development and testing –High spatial & spectral resolution Data Applications include:Data Applications include: –Target detection –Material classification –In-water analysis Data must be no excuses:Data must be no excuses: –Know target location and characteristics –Known environmental conditions –Known sensor performance

MegaCollect: Opportunity *RIT’s Modular Imaging Spectrometer Instrument **RIT’s Wildfire Airborne Sensor Program *RIT’s Modular Imaging Spectrometer Instrument **RIT’s Wildfire Airborne Sensor Program * **

DIRSIG MegaScene DIRSIG Physics based model developed at RIT to simulate remotely sensed dataPhysics based model developed at RIT to simulate remotely sensed data Various platformsVarious platforms –Line scanner, framing array, pushbroom scanner MegaScene DIRSIG simulations (Tiles 1-5). Main experiment area is labeled CE. (7 sq. km)

Flight Coverage

MegaCollect: Results MegaCollect was conducted on June 7, 2004 – –weather cooperated to enable multiple collections and experiments to be conducted! – –four planes were flying simultaneously! – –required ~50 persons on the ground! – –102 GB of overhead imagery – –1 GB of spectral measurements, metadata, & documentation imagery Weather Conditions: sunny with increasing haze sunny with increasing haze and occasional clouds and occasional clouds WASP Terra Pix 7000ft MISI RGB & Thermal

RIT’s WASP Imagery Camp Eastman 3,000 ft (GDS 0.15m) SWIR : microns Terra Pix : microns MWIR : 3.0 – 5.0 microns LWIR : 3.0 – 5.0 microns

WASP June 7, ft

Experiments Target Variation ScenariosTarget Variation Scenarios –Low, medium, high contrast –Contaminated –Concealed –Illumination conditions –Variable background clutter –Thermal Target Sensor CharacterizationSensor Characterization –MTF –Noise –Spectral Smile Search/Rescue and Fire Phenomenology TargetsSearch/Rescue and Fire Phenomenology Targets Illumination Experiment - Effect of sun angle on detection algorithms detection algorithms

MegaCollect Experiments Concealed Target Experiment - Ability to detect partially obscured targets targets Contaminated Target Experiment - Impact of surface contaminants on target detection algorithms on target detection algorithms Fire Phenomenology Experiment - Detection - LWIR spectral properties

Data collected on scene – –GPS location – –Target Surround Photographs – –ASD radiance – –ASD reflectance – –D&P radiance – –ASD downwelling radiance – –Temperature » »Thermocouples » »Exergen » »Thermistors at Genesee – –Meteorological conditions Staring Radiometer Main Calibration Targets with ASD

Spectral Instruments

Data Management: Streamlining Metadata Entry Mobile GIS each subject has 30 fieldseach subject has 30 fields each measurement has (60-80) instrument dependant fieldseach measurement has (60-80) instrument dependant fields

Summary & Future Directions The Megacollect campaign proved to be a valuable exercise in planning, multi-agency coordination, measurement protocol review, and execution. Data Generated: – –102 gigabytes of overhead imagery – –1 gigabyte of spectral measurements, metadata,and imagery Apply hyperspectral algorithms Update DIRSIG spectral simulations of the Megascene Update DIRSIG sensor models Data Integration – –Data sharing through GIS – –Link the DIRS Spectral Library to an online GIS

ACKNOWLEDGEMENTS The flight crews of the COMPASS and SEBASS sensors Landcare Aviation for RIT flight support Tim Gallagher and Jason Faulring for operating the MISI and WASP sensors Monroe County Executive Maggie Brooks Mike Spang of the Irondequoit Town Parks Ray Nary of Camp Eastman Carolyn Kennedy and Rulon Simmons of ITT RIT ROTC DIRS Spectral Measurements Team lead by Lon Smith DIRS Faculty, Staff, & Students

Nina Raqueno (585)

REFERENCES 1. J. R. Schott, Remote Sensing: The Image Chain Approach, Oxford University Press, New York, NY, J. R. Ballard, Jr. and J. A. Smith, “A multi-wavelength thermal infrared and reflectance scene simulation model,” in Proceedings of the International Geophysics and Remote Sensing Symposium, (Toronto, Ontario), June E. J. Ientilucci and S. D. Brown, “Advances in wide area hyperspectral image simulation,” in Proceedings of the SPIE conference on Targets and Backgrounds IX: Characterization and Representation, 5075, (Orlando, FL), April R. V. Raque˜no, N. G. Raque˜no, A. D. Weidemann, S. W. Eer, M. Perkins, A. Vodacek, J. R. Schott, W. D. Philpot, and M. Kim, “Megacollect 2004: Hyperspectral collection experiment over the waters of the Rochester Embayment,” in Proceedings of the SPIE conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, (Orlando, FL), March C. Simi, E. Winter, M. Williams, and D. Driscoll, “Compact Airborne Spectral Sensor (COMPASS),” in Proceedings of SPIE: Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII, 4381, pp. 129–136, J. A. Hackwell, D. W. Warren, R. P. Bongiovi, S. J. Hansel, T. L. Hayhurst, D. J. Mabry, M. G. Sivjee, and J.W. Skinner, “LWIR/MWIR imaging hyperspectral sensor for airborne and ground-based remote sensing,” in Proceedings of SPIE: Imaging Spectrometry II, 2819, pp. 102–107, J. R. Schott, T. W. Gallagher, B. L. Nordgren, L. C. Sanders, and J. A. Barsi, “Radiometric calibration procedures and performance for the Modular Imaging Spectrometer Instrument (MISI),” in Proceedings of the Earth International Airborne Remote Sensing Conference, ERIM, J. R. Schott, S. D. Brown, R. V. Raque˜no, H. N. Gross, and G. Robinson, “An advanced synthetic image generation model and its application to multi/hyperspectral algorithm development,” Canadian Journal of Remote Sensing 25, pp. 99–111, June A. Vodacek, R. L. Kremens, A. J. Fordham, S. C. VanGorden, D. Luisi, J. R. Schott, and D. J. Latham, “Remote optical detection of biomass burning using a potassium emission signature,” International Journal of Remote Sensing 23, pp. 2721–2726, NSEC, “Spectral data services: Spectral library data structure specification version 1.1,” Tech. Rep. ITT f, National Air Intelligence Center, Feb

Wildfire Airborne Sensor Program (WASP) System in development at RITSystem in development at RIT Four separate camera systemsFour separate camera systems –Terra Pix : microns –SWIR: microns –MWIR: microns –LWIR: microns CharacterizationCharacterization –equipment specifications –actual lab scenes and measurements –Bayer pattern artifacts WASP

WASP Modeling Criterion DIRSIG needs Terra Pix NIRMWIRLWIR Focal length m m m m # x pixels (Cross-track) # y pixels (Along-track) X length m m Y length m m Spectral response SpecsSpecsSpecsSpecs Gain & bias Color strips target CI blackbody PSF Plywood target CI blackbody W/ foil edge CI blackbody W/ foil edge Noise Color strips target CI blackbody

Instrumentation......