Characterization techniques for incorporating backgrounds into …dirsig.org/docs/maps.pdf · 2015. 2. 23. · Material Maps: Approach • An image is used to vary the material ID

Digital Imaging and Remote Sensing LaboratoryR.I.TRR..II..TT

Characterization techniques forCharacterization techniques forincorporating backgrounds intoincorporating backgrounds into

DIRSIGDIRSIG

Scott D. BrownScott D. BrownJohn R. SchottJohn R. Schott

Digital Imaging and Remote Sensing LabDigital Imaging and Remote Sensing LabChester F. Carlson Center for Imaging ScienceChester F. Carlson Center for Imaging Science

Rochester Institute of TechnologyRochester Institute of Technology


DIRSIG:DIRSIG:Model HighlightsModel Highlights

•• First principles based modelingFirst principles based modeling–– Single expression used for solar and emissive regionsSingle expression used for solar and emissive regions

•• Spectral TreatmentSpectral Treatment–– All radiometric computations done on a spectral basisAll radiometric computations done on a spectral basis–– Transmissive objects (vegetation, nets, gas plumes, etc.)Transmissive objects (vegetation, nets, gas plumes, etc.)–– All atmospheric contributions are a function of path lengthAll atmospheric contributions are a function of path length

and geometry (MODTRAN and FASCODE)and geometry (MODTRAN and FASCODE)–– Approaches to introduce spatial and spectral variations.Approaches to introduce spatial and spectral variations.

•• Complex temperature predictionComplex temperature prediction–– Passive slab model using meteorological history (THERM)Passive slab model using meteorological history (THERM)–– Aware of subtle environmental variationsAware of subtle environmental variations–– Per-pixel (sub-facet) temperature predictionPer-pixel (sub-facet) temperature prediction–– Mechanisms to import external thermal modelsMechanisms to import external thermal models


DIRSIG:DIRSIG:Model HighlightsModel Highlights

•• Complex Sensor ModelComplex Sensor Model–– Capable of modeling an arbitrary number of discrete orCapable of modeling an arbitrary number of discrete or

continuous (spectrometer) bands/channels.continuous (spectrometer) bands/channels.–– Can reproduce geometric distortions found scannerCan reproduce geometric distortions found scanner

platforms.platforms.–– Can model Imaging FTS (IFTS) platforms (unreleased)Can model Imaging FTS (IFTS) platforms (unreleased)

•• Rigorous Surface Reflectance ModelingRigorous Surface Reflectance Modeling–– Rigorous modeling of incident radiation onto any surface.Rigorous modeling of incident radiation onto any surface.–– Currently supporting BRDF-type measurements.Currently supporting BRDF-type measurements.

•• Availability of per-pixel Truth mapsAvailability of per-pixel Truth maps–– Can report useful “truth” information including material class,Can report useful “truth” information including material class,

surface reflectance, surface temperature, atmosphericsurface reflectance, surface temperature, atmospherictransmission, etc. for transmission, etc. for each pixeleach pixel in the image. in the image.


DIRSIG:DIRSIG:Application AreasApplication Areas

•• Sensor PrototypingSensor Prototyping–– Construct and test a new sensor in a virtual environmentConstruct and test a new sensor in a virtual environment–– Evaluate design tradesEvaluate design trades–– Produce example products for customersProduce example products for customers

•• Algorithm TestingAlgorithm Testing–– Decrease the number of expensive field collectionsDecrease the number of expensive field collections–– Control all the image formation variablesControl all the image formation variables–– Per-pixel truth allows for better evaluation of the algorithmPer-pixel truth allows for better evaluation of the algorithm

•• Analyst TrainingAnalyst Training–– Create custom training examplesCreate custom training examples–– Test the sensitivity of the analyst to specific phenomenologyTest the sensitivity of the analyst to specific phenomenology–– Provide a tool to hypothesis about the nature ofProvide a tool to hypothesis about the nature of

phenomenologyphenomenology


Scene Construction:Scene Construction:ChallengesChallenges

•• Significant effort is invested in target modelsSignificant effort is invested in target models–– Complex geometry, surface attributing, etc.Complex geometry, surface attributing, etc.

•• Backgrounds are just as complexBackgrounds are just as complex–– More variation in materials, larger coverage, etc.More variation in materials, larger coverage, etc.–– Using target building techniques is time and resourceUsing target building techniques is time and resource

prohibitive.prohibitive.–– Desirable to easily create very-large area scenes.Desirable to easily create very-large area scenes.

•• Employing other techniquesEmploying other techniques–– Construct simplified geometries and apply the variationsConstruct simplified geometries and apply the variations

in materials and material properties using “mapping”in materials and material properties using “mapping”approaches.approaches.

–– Utilize “maps” that can be derived from real world dataUtilize “maps” that can be derived from real world datausing standard exploitation tools (ENVI, Imagine, etc.)using standard exploitation tools (ENVI, Imagine, etc.)


MappingsMappingsWhat do they do?What do they do?

•• Allow the user to “map” other properties to a facetAllow the user to “map” other properties to a facetor set of facets in the scene.or set of facets in the scene.–– Change material propertiesChange material properties–– Change reflectance propertiesChange reflectance properties–– Directly supply reflectance spectraDirectly supply reflectance spectra–– Directly supply surface temperaturesDirectly supply surface temperatures

•• The alternative …The alternative …–– Create smaller individual facets with the desiredCreate smaller individual facets with the desired

attributes.attributes.–– More facets affect the run-time performance of the ray-More facets affect the run-time performance of the ray-

tracer.tracer.


Mappings:Mappings:Logical FlowLogical Flow


Geometry Maps:Geometry Maps:Importing geometric featuresImporting geometric features

•• Utilize widely available triangular irregular network (TIN) toolsUtilize widely available triangular irregular network (TIN) toolsto facetize terrains.to facetize terrains.

–– Efficient for terrains with large areas with near-constant slope.Efficient for terrains with large areas with near-constant slope.•• Directly render the terrain dataDirectly render the terrain data

–– Needed for terrains that do not have areas with near-constantNeeded for terrains that do not have areas with near-constantslope.slope.

–– Implement a “height map” ray-tracing facility.Implement a “height map” ray-tracing facility.•• Both approaches allow for multi-resolution renderingBoth approaches allow for multi-resolution rendering

–– Change facet budget for TIN algorithmChange facet budget for TIN algorithm–– Degrade height image for direct rendering approachDegrade height image for direct rendering approach


Facetized Terrain:Facetized Terrain:Using TIN ToolsUsing TIN Tools

The utility for generating facetized terrain from DEMs for use with DIRSIG isThe utility for generating facetized terrain from DEMs for use with DIRSIG isbuilt around a data dependent triangular irregular network (TIN) algorithmbuilt around a data dependent triangular irregular network (TIN) algorithm

(Scape) implemented by Micheal Garland at CMU. This example uses only(Scape) implemented by Micheal Garland at CMU. This example uses only20,000 facets.20,000 facets.


Geometry Maps:Geometry Maps:Direct RenderingDirect Rendering

Box Entry PointBox Entry PointBox Exit PointBox Exit Point

Search-and-Intersect ApproachSearch-and-Intersect Approach


Material Maps:Material Maps:ApproachApproach

•• An image is used to vary the material ID based onAn image is used to vary the material ID based onthe image digital countthe image digital count–– Material look-up table is required that translates theMaterial look-up table is required that translates the

image gray values into material IDs.image gray values into material IDs.•• Normally, a facet will have one material assigned toNormally, a facet will have one material assigned to

it.it.–– A material map allows the user to create materialA material map allows the user to create material

transitions at sub-facet resolutions.transitions at sub-facet resolutions.•• Useful for attributing facets generated by tools for aUseful for attributing facets generated by tools for a

terrain.terrain.


Material Maps:Material Maps:Logical FlowLogical Flow

Material MapMaterial MapImageImage

Mat ID #4Mat ID #4

Mat ID #6Mat ID #6

Mat ID #5Mat ID #5

DC->IDDC->IDLook-up TableLook-up Table

GrassGrass

ConcreteConcrete

AsphaltAsphalt

MaterialMaterialPropertiesProperties


Material Property Maps:Material Property Maps:ApproachApproach

•• Utilize an image to introduce spatial variations inUtilize an image to introduce spatial variations inmaterial propertiesmaterial properties–– Change surface reflectance/emissivity or temperature.Change surface reflectance/emissivity or temperature.

•• Allow the scene builder to introduce naturalAllow the scene builder to introduce naturalvariations within a material classvariations within a material class–– Variation in reflectance do to impurities, etc.Variation in reflectance do to impurities, etc.

•• Allow the user to include spatial variations thatAllow the user to include spatial variations thatcannot be simulated.cannot be simulated.


Texture Maps:Texture Maps:Logical FlowLogical Flow


Reproducing Spatial and Spectral CorrelationReproducing Spatial and Spectral Correlation

GreenGreenBandBand

Near-IRNear-IRBandBand

Images of the same patch of grass in the Green andImages of the same patch of grass in the Green andNear-IR bands. The contrast inversion demonstratedNear-IR bands. The contrast inversion demonstrated

with these two bands is characteristic of stressedwith these two bands is characteristic of stressedvegetation.vegetation.


Desert Scenario:Desert Scenario:Terrain, Material and TextureTerrain, Material and Texture

2 km2 km

Activity AreaActivity AreaTerrain data, classmaps andTerrain data, classmaps andvehicle models are combinedvehicle models are combinedto create large scenes.to create large scenes.


Forest Scenario:Forest Scenario:Material and Texture MapsMaterial and Texture Maps

Image after sensor modelImage after sensor modelOriginal oversampled imageOriginal oversampled image


Mixture Maps:Mixture Maps:ApproachApproach

•• Utilize outputs from off-the-shelf processing tools.Utilize outputs from off-the-shelf processing tools.–– Material fraction images and respective spectralMaterial fraction images and respective spectral

reflectancesreflectances..•• Directly mix material (Directly mix material (endmemberendmember) spectral) spectral

reflectances reflectances at each pixel.at each pixel.–– Creates mixed pixel Creates mixed pixel reflectances reflectances without spatialwithout spatial

oversamplingoversampling..•• CaveatsCaveats

–– Accuracy dependent on Accuracy dependent on unmixing unmixing performance.performance.


Mixture Maps:Mixture Maps:Logical FlowLogical Flow


Reflectance Maps:Reflectance Maps:ApproachApproach

•• Spectral reflectance cube is used to specify the reflectance ofSpectral reflectance cube is used to specify the reflectance ofthe surface.the surface.

•• Overrides the values in the reflectance (emissivity) databaseOverrides the values in the reflectance (emissivity) databasefiles.files.

•• Useful for inserting calibrated truth imagery as a background.Useful for inserting calibrated truth imagery as a background.•• Extract reflectances behave just as if they came from anExtract reflectances behave just as if they came from an

emissivity file.emissivity file.–– Still results in the optical variations passed to the thermalStill results in the optical variations passed to the thermal

model.model.


Reflectance Maps:Reflectance Maps:Target EmbedingTarget Embeding

Zoom of vehicles afterZoom of vehicles afterapplication of a sensor model.application of a sensor model.


Temperature Maps:Temperature Maps:ApproachApproach

•• Image pixel values are used as the surface temperature ratherImage pixel values are used as the surface temperature ratherthan one predicted by THERM.than one predicted by THERM.

•• Useful for inserting calibrated truth imagery as a background.Useful for inserting calibrated truth imagery as a background.•• Shadows are not formed on the inserted imagery.Shadows are not formed on the inserted imagery.•• Emissivity is still used to compute the surface leavingEmissivity is still used to compute the surface leaving

radiance.radiance.


Hybrid Modeling:Hybrid Modeling:GENESSIS and DIRSIGGENESSIS and DIRSIGMaterial MapsMaterial Maps

Mixture MapsMixture MapsAltitude MapAltitude Map

Material DatabaseMaterial Database

GENESSISGENESSIS

DIRSIGDIRSIG

Surface Leaving Radiance Surface Leaving Radiance Image CubeImage Cube

SensorSensorModelModel

OversampledOversampledSensor Reaching Radiance Sensor Reaching Radiance

Image CubeImage Cube

AtmosphericDatabase

Target FacetDatabase

SensorGeometry


Hybrid Modeling:Hybrid Modeling:ExampleExample


Mapping Approaches:Mapping Approaches:Strengths and WeaknessesStrengths and Weaknesses

•• Texture MapsTexture Maps–– Can only guarantee spatial statistics in the wavelength regionCan only guarantee spatial statistics in the wavelength region

that texture was provided.that texture was provided.–– Additional texture images at other wavelengths would helpAdditional texture images at other wavelengths would help

constrain the spectral variation.constrain the spectral variation.•• Mixture MapsMixture Maps

–– Better chance of reproducing all spatial/spectral variability.Better chance of reproducing all spatial/spectral variability.–– Unmixing algorithms simultaneous constrain the spatial andUnmixing algorithms simultaneous constrain the spatial and

spectral variances.spectral variances.•• Reflectance MapsReflectance Maps

–– Directly introduces measured spatial/spectral variability.Directly introduces measured spatial/spectral variability.–– Requires measured imagery.Requires measured imagery.


Mapping Approaches:Mapping Approaches:Strengths and WeaknessesStrengths and Weaknesses

•• Temperature MapsTemperature Maps–– Useful for introducing effects that cannot be modeledUseful for introducing effects that cannot be modeled

yet.yet.–– Temperatures are tied to a specific time and conditionTemperatures are tied to a specific time and condition

which limits flexibility.which limits flexibility.•• Radiance MapsRadiance Maps

–– Useful for importing radiances for targets that cannot beUseful for importing radiances for targets that cannot bemodeled yet.modeled yet.

»» Import radiances for a lake from an external Littoral/Water model.Import radiances for a lake from an external Littoral/Water model.–– Model flexibility tied to imported data.Model flexibility tied to imported data.


DIRSIG:DIRSIG:Model AvailabilityModel Availability

•• Restricted UseRestricted Use–– Available to all U.S. Government agencies and contractorsAvailable to all U.S. Government agencies and contractors–– RIT licensing agreementRIT licensing agreement–– Users are required to attend a 3 day training sessionUsers are required to attend a 3 day training session

•• Supported PlatformsSupported Platforms–– Sun Sparc and UltraSparc running Solaris 2.6 or betterSun Sparc and UltraSparc running Solaris 2.6 or better–– SGI Servers/Workstations running IRIX 6.XSGI Servers/Workstations running IRIX 6.X–– DEC Alpha running Digital Unix 4.X or RedHat Linux 6.XDEC Alpha running Digital Unix 4.X or RedHat Linux 6.X–– Intel Pentium (I, II, and III) running RedHat Linux 6.XIntel Pentium (I, II, and III) running RedHat Linux 6.X–– SGI Onyx/Origin Multiple Processor machines (MPI)SGI Onyx/Origin Multiple Processor machines (MPI)

•• Future PlatformsFuture Platforms–– MS Windows (98 and NT)MS Windows (98 and NT)


DIRSIG:DIRSIG:Contact InformationContact Information

DIRSIG Web Page:DIRSIG Web Page:http://www.cis.rit.edu/~dirsighttp://www.cis.rit.edu/~dirsig

[email protected]@cis.rit.edu

Scott D. BrownScott D. Brownbrown@[email protected]

John R.John R. Schott Schottschottschott@@ciscis..ritrit..eduedu

Documents

Characterization techniques for incorporating backgrounds into …dirsig.org/docs/maps.pdf · 2015. 2. 23. · Material Maps: Approach • An image is used to vary the material ID