Digital Imaging and Remote Sensing Laboratory R.I.TR.I.TR.I.TR.I.T R.I.TR.I.TR.I.TR.I.T RIT ONR MURI Algorithm Development David Messinger LM LASS Status Meeting October 14, 2003

Digital Imaging and Remote Sensing Laboratory R.I.TR.I.TR.I.TR.I.T R.I.TR.I.TR.I.TR.I.T Scope of ONR MURI Hyperspectral imagery, down-looking geometryHyperspectral imagery, down-looking geometry Model-based algorithm developmentModel-based algorithm development –Moving away from data-driven approaches –Leveraging knowledge of physical phenomena Three year project (+ two option years)Three year project (+ two option years) –Approaching end of third year –Currently in option year review process TeamTeam –RIT, Cornell, UC Irvine

Digital Imaging and Remote Sensing Laboratory R.I.TR.I.TR.I.TR.I.T R.I.TR.I.TR.I.TR.I.T Application Areas Project considers four application areas:Project considers four application areas: –Littoral Zone Characterization –Atmospheric Parameter Retrieval –Gaseous Effluents –Material Identification Algorithms & models are physics-basedAlgorithms & models are physics-based Effort into understanding (and leveraging) the physics behind the phenomenaEffort into understanding (and leveraging) the physics behind the phenomena

Digital Imaging and Remote Sensing Laboratory R.I.TR.I.TR.I.TR.I.T R.I.TR.I.TR.I.TR.I.T Littoral Zone Characterization GoalsGoals –Inversion of hyperspectral imagery to physical quantities of interest »Chlorophyll content »Total Suspended Sediment »Colored Dissolved Organic Matter –Improve in-water radiance simulations ApproachesApproaches –Look up table generation and model matching –New model development and validation - Photon Mapping –Validation through controlled experiment on Conesus Lake –Simultaneous retrieval of atmospheric and water properties

Digital Imaging and Remote Sensing Laboratory R.I.TR.I.TR.I.TR.I.T R.I.TR.I.TR.I.TR.I.T Preliminary Mapping Results Aug 1, 2001 N CHLTSSCDOM PHILLS Imagery AVIRIS Imagery [mg/m^3]

Digital Imaging and Remote Sensing Laboratory R.I.TR.I.TR.I.TR.I.T R.I.TR.I.TR.I.TR.I.T Photon Mapping Computing the radiance field within and emergent from a large body of water is a complicated radiative transfer problemComputing the radiance field within and emergent from a large body of water is a complicated radiative transfer problem –Multiple particle species –Multiple scattering effects –Water-surface effects Current model (Hydrolight) does not include many phenomena associated with littoral zoneCurrent model (Hydrolight) does not include many phenomena associated with littoral zone Goal - improve the modelsGoal - improve the models –train algorithms better –retrieve water-constituent parameters more accurately

Digital Imaging and Remote Sensing Laboratory R.I.TR.I.TR.I.TR.I.T R.I.TR.I.TR.I.TR.I.T Photon Mapping Photon mapping is from the computer graphics communityPhoton mapping is from the computer graphics community Two-pass solution to the problemTwo-pass solution to the problem –First pass considers radiation sources and scattering to create the radiance field in the water –Second pass considers the sensor FOV and “samples” the radiance field –Computationally expensive to do spectral calculations sourcesensor scattering, absorption, emission, etc., stored in the photon map water

Digital Imaging and Remote Sensing Laboratory R.I.TR.I.TR.I.TR.I.T R.I.TR.I.TR.I.TR.I.T Preliminary Photon Mapping Test Scene Scene Model Bottom Rendering Test Bottom + Water Rendering Test Reflectance Test Transmission Test Preliminary PM Test

Digital Imaging and Remote Sensing Laboratory R.I.TR.I.TR.I.TR.I.T R.I.TR.I.TR.I.TR.I.T Atmospheric Parameter Retrieval GoalsGoals –Retrieve altitude profiles of temperature, water-vapor, & pressure –Retrieve radiometric description of the atmosphere  atm, L u, L d –Remove effects of the atmosphere from MWIR/LWIR imagery to aid in separation of temperature & emissivity, perform material id studies, gas quantification, etc. –Focus on thermal IR methods Model-Based MethodsModel-Based Methods –CCRA - Canonical Correlation Regression Analysis »Develop the correlation between image radiance and training data –AAC - Autonomous Atmospheric Compensation »Model-based correlation between training set and data-derived parameters

Digital Imaging and Remote Sensing Laboratory R.I.TR.I.TR.I.TR.I.T R.I.TR.I.TR.I.TR.I.T CCRA Temperature & Transmission Profiles  =1K  = 1.0

Digital Imaging and Remote Sensing Laboratory R.I.TR.I.TR.I.TR.I.T R.I.TR.I.TR.I.TR.I.T AAC Compensated Image SEBASS Uncompensated Image AAC Compensated Image R: 8.21  m G:  m B:  m

Digital Imaging and Remote Sensing Laboratory R.I.TR.I.TR.I.TR.I.T R.I.TR.I.TR.I.TR.I.T AAC Atmospheric Parameters mean value and +/- 1  shown ~ 40 sub-images in cube

Digital Imaging and Remote Sensing Laboratory R.I.TR.I.TR.I.TR.I.T R.I.TR.I.TR.I.TR.I.T Gaseous Effluent Characterization Given a MWIR/LWIR image thought to contain a gaseous plumeGiven a MWIR/LWIR image thought to contain a gaseous plume –where is the plume –what are the species –what are their relative abundances –what are the absolute abundances and temperatures DetectionDetection IdentificationIdentification QuantificationQuantification Source process estimationSource process estimation

Digital Imaging and Remote Sensing Laboratory R.I.TR.I.TR.I.TR.I.T R.I.TR.I.TR.I.TR.I.T DetectionDetection –Study to identify “best” algorithm from standard VIS/NIR hard- target detection schemes –Quantifiable measure using synthetic imagery IdentificationIdentification –Application of the Invariant Hyperspectral Algorithm –Linear, Matrix and Stepwise Regression QuantificationQuantification –Application of a non-linear radiance model –Solves for gas concentration and temperature directly per-pixel Algorithm Path

Digital Imaging and Remote Sensing Laboratory R.I.TR.I.TR.I.TR.I.T R.I.TR.I.TR.I.TR.I.T Gas Signature Subspace Variability NH 3 gasNH 3 gas MLS atmosphereMLS atmosphere T s = 290 KT s = 290 K T gas = 260 KT gas = 260 K NH 3 gasNH 3 gas MLS atmosphereMLS atmosphere T s = 290 KT s = 290 K c = 100 ppm-mc = 100 ppm-m

Digital Imaging and Remote Sensing Laboratory R.I.TR.I.TR.I.TR.I.T R.I.TR.I.TR.I.TR.I.T Gas Detection by Principal Components and Projection Pursuit PCA component #15 Projection Pursuit projection #2 SEBASS imagerySEBASS imagery NH 3 plumeNH 3 plume Urban, refinery sceneUrban, refinery scene Similar results from both methodsSimilar results from both methods Significant differences “downwind”Significant differences “downwind” Different false alarm patternDifferent false alarm pattern Want to quantify these differences using DIRSIG plume simulationWant to quantify these differences using DIRSIG plume simulation  m10.44  m

Digital Imaging and Remote Sensing Laboratory R.I.TR.I.TR.I.TR.I.T R.I.TR.I.TR.I.TR.I.T Synthetic Plume Image Part of MegaScene, tile 1Part of MegaScene, tile 1 LWIR rendering with plume addedLWIR rendering with plume added Plume model is EPA/JPL Gaussian plumePlume model is EPA/JPL Gaussian plume NH 3 release, ~ 10 g/sNH 3 release, ~ 10 g/s

Digital Imaging and Remote Sensing Laboratory R.I.TR.I.TR.I.TR.I.T R.I.TR.I.TR.I.TR.I.T Synthetic Plume Spectra Plume features are weak, but spectrally aligned with the laboratory spectrum

Digital Imaging and Remote Sensing Laboratory R.I.TR.I.TR.I.TR.I.T R.I.TR.I.TR.I.TR.I.T Material Identification Exploring “space” in which the target spectra potentially lieExploring “space” in which the target spectra potentially lie –Invariant Algorithm - accounts for (almost) all possible illumination and atmospheric conditions –Basis Vector Selection - given the description of either the background or the target space, how do we best extract the basis vectors from this space? Considering both resolved and sub-pixel targetsConsidering both resolved and sub-pixel targets Developing methods for concealed & contaminated targetsDeveloping methods for concealed & contaminated targets Moving toward a hybrid method combining both geometric and statistical descriptions of the target / background spaceMoving toward a hybrid method combining both geometric and statistical descriptions of the target / background space

Digital Imaging and Remote Sensing Laboratory R.I.TR.I.TR.I.TR.I.T R.I.TR.I.TR.I.TR.I.T Physics-Based Model: Concealment E s ( ) L s ( )       F tL d ( ) (1-F t )L bd ( ) Direct Solar Terms DownwelledBackground Describes the radiance interacting with the targetDescribes the radiance interacting with the target Summation over i = target (t), concealing (  ) object propertiesSummation over i = target (t), concealing (  ) object properties Need to add in:Need to add in: -downwelling term -target / concealment weighting factor (  )

Digital Imaging and Remote Sensing Laboratory R.I.TR.I.TR.I.TR.I.T R.I.TR.I.TR.I.TR.I.T Physics-Based Model: Contamination Due to Target Due to Shadowing and Tree Shine Due to Atmosphere Example Target Space r s ( ) : sample reflectivity r c ( ) : contaminant reflectivity Can control the amount of contaminantCan control the amount of contaminant Contaminated target reflectivity

Digital Imaging and Remote Sensing Laboratory R.I.TR.I.TR.I.TR.I.T R.I.TR.I.TR.I.TR.I.T Summary The RIT ONR MURI is an algorithm development program, considering model-based algorithmsThe RIT ONR MURI is an algorithm development program, considering model-based algorithms Four applications areasFour applications areas –Littoral Zone Characterization –Atmospheric Parameter Retrieval –Gaseous Effluent Detection, Identification, and Quantification –Material Identification Considerable progress has been made in each areaConsiderable progress has been made in each area Currently undergoing option year funding reviewCurrently undergoing option year funding review

Digital Imaging and Remote Sensing Laboratory R.I.TR.I.TR.I.TR.I.T R.I.TR.I.TR.I.TR.I.T Questions?