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Extended Hyperspectral Imaging System Modeling and Implementation for Subpixel Target Detection Bo Ding and John P. Kerekes Chester F. Carlson Center for Imaging Science Rochester Institute of Technology 54 Lomb Memorial Drive, Rochester, NY 14623 USA ABSTRACT For hyperspectral imaging system design and parameter trade-off research, an analytical model to simulate the remote sensing system has been developed and is in progress to be made available to the community. The analytical model includes scene, sensor and target characteristics, and also atmospheric features, background spectral statistics, sensor specifications and target spectral statistics. The model is being implemented as a web-based application through an RIT-hosted website. Predicting system performance has been verified by real world data collected during the RIT SHARE 2012 collection and the data shows consistency with the simulated results on calibration tarps and grass. Also, subpixel target spectral statistics are predicted by this model. Some parameter trade-off examples are given and analyzed to explain the utility of this model. Keywords: hyperspectral imaging, remote sensing system modeling, subpixel target detection, prediction model 1. INTRODUCTION In support of hyperspectral imaging system design and parameter trade-off research, an analytical end-to-end model to simulate the remote sensing system pipeline and forecast remote sensing system performance has been developed1 and extended2 . Now the model is being implemented and will be made available to the remote sensing community through an RIT-hosted website in the near future. Users will be able to forecast hyperspectral imaging system performance by defining an observational scenario along with imaging system parameters. The implemented analytical model includes scene, sensor and target characteristics as well as atmospheric features, background spectral reflectance statistics, sensor specifications and target class reflectance statistics. For target detection applications, common data processing algorithms are also implemented. Predictions of system performance have been verified by comparing the forecast results to ones obtained using real world data collected during the RIT SHARE 2012 data collection. In this paper, first the analytical model is introduced, and then the web-based application to model a remote sensing system is described and a typical application is presented. Sensor modeling has been extended to include the airborne ProspecTIR instrument. Subpixel target spectral radiance statistics are predicted by the model with given scene information, pre-measured background, target spectral reflectance statistics, and also the atmospheric radiative transfer model MODTRAN. The predicted spectral radiance statistics are transformed into a feature space and used with target detection algorithms to generate probability of detection versus false alarm curves to evaluate system performance and parameter trade-offs. The validation data from the SHARE 2012 collection show consistency with the simulated result on calibration tarps in the same system setting. Additionally, some examples of parameter trade-offs are given and analyzed to explain the utility of this model for hyperspectral imaging system design and research. Further author information: John P. Kerekes: E-mail: [email protected] Imaging Spectrometry XVIII, edited by Pantazis Mouroulis, Thomas S. Pagano, Proc. of SPIE Vol. 8870, 88700P · © 2013 SPIE · CCC code: 0277-786X/13/$18 · doi: 10.1117/12.2022688 Proc. of SPIE Vol. 8870 88700P-1 Downloaded From: http://spiedigitallibrary.org/ on 12/02/2013 Terms of Use: http://spiedl.org/terms 2. HYPERSPECTRAL IMAGING SYSTEM MODELING A novel systematic approach to analyze hyperspectral imaging system was introduced in 19913 . That work serves as a cornerstone in remote sensing system modeling research based on input statistics data. Then the forecasting and analysis of spectroradiometric system performance (FASSP) model1 was presented based on the first paper extended for subpixel target detection. A more clear and straightforward system structure was brought up and the whole process developed into separated function blocks, as is shown in Fig. 1 . Sensor Parameter Files Reflectance Statistics Library Scene Model Spectral Radiance Statistics Sensor Model Processing Algorithm Description Spectral Signal Statistics Processing Model Performance Metrics User Inputs Scene Description Figure 1. FASSP framework model2 This model is statistical data based, which means the inputs are mean and covariance statistics of the reflectance data. This system analysis method may not be as intuitive as a physics-based image simulation such as the DIRSIG system,4 which uses a 3D CAD model input to generate predicted image output, but it reduces the computational cost and sometimes reduces the unnecessary system modeling complexity for specific applications. In a subpixel target detection scenario with multiple background scenes, the background scene reflectance is modeled by a weighted combination of different backgrounds. The subpixel target is similar, which is a weighted combination of the target itself and the background it resides in5 . Using the modeled statistics, MODerate resolution atmospheric TRANsmission(MODTRAN)6 is applied to generate sensor reaching radiance. For the sensor modeling, the channel response modeling is first applied to map the input dimension to the sensor dimension, and then different kinds of noises and data link errors are calculated to fully model the sensor behavior.2 The processing model first converts the radiance data back to reflectance by the empirical line method(ELM)7 . Then the result is usually transformed into some specified feature space, like simply choosing spectral band or performing PCA to generate data for the target detection algorithm. Then a ROC curve can be generated to evaluate the system performance and target detectability. 3. MODEL IMPLEMENTATION Based on the analytical model described above, the end-to-end model is implemented and will be made available to the hyperspectral imaging research community through the RIT-CIS website working as a web application. Proc. of SPIE Vol. 8870 88700P-2 Downloaded From: http://spiedigitallibrary.org/ on 12/02/2013 Terms of Use: http://spiedl.org/terms Hyperspectral Imaging System Prediction Model HTML JavaScript CSS /startpage/ /customize/ /paraselection/ Users select sensor, target, background pre-fill text blocks Initialize selectables /error/ /result/ Users make changes to parameters Users redraw plots FRONT END BACK END urls.py, forms.py Y main.py ->startpage() main.py ->customize() Y main.py -> paraselection() “main”goes wrong? executable file “main” Radiance SNR ROC text file N executable file “initialize” user Customizing? N ModTran pathconfig.ini main.py ->results() Python C++ Figure 2. System Working Flow It will allow users to select different sensors, targets and backgrounds for system modeling or choose some predefined scenarios with fixed settings. Also, for different modules in the model, the user could also adjust some parameters to meet their specific needs, including the scene, sensor, and target parameters. The end-to-end modeling system is divided into two parts in the implementation: the front end, which is the user interface providing an interaction with the system parameters; and the back end, which is the algorithm computational core doing all the calculation and data recording. Sensor selection: Hymap Target selection: Green BMW Background Selection: Background to Choose: TREE Old GRASS ROAD Avon Grass White Tarp Black Tarp Bushes Reflectance 10% Reflectance 30% Reflectance 50% Background Chosen: A Soil 1 Snil m l77 [ add all ) Submit I Figure 3. Sensor, Background and Target Selection Proc. of SPIE Vol. 8870 88700P-3 Downloaded From: http://spiedigitallibrary.org/ on 12/02/2013 Terms of Use: http://spiedl.org/terms For the application, the user can choose predefined scenarios to start with, or choose a customized scenario to specify every parameter manually. Fig.3 shows the sensor, background and target selection user interface after choosing ”customized scenario”. The background selection freedom is broad, and any combination is available. Fig.4 shows the parameter selection interface. The pre-filled values in parameter selection forms are dynamically generated based on the sensor, background and target selection. Hyperspectral Imaging System Prediction Model Leave your parameters PARAMETER HINT, \\I-LEN MODIFYING Scene Sensor Target Atmospheric Haze: Noise Factor: Target file Name: 2 1.0 Ground Altitude: vgrnbmw. ref Gain Factor: Target Scale: Rltv Calibration Error( %): Solar Angle: Atmospheric Model: Integration Time(s): 2 Target Percentage(%): 950824_05_Tree1 _R. ref 950824_05 Grassl_R.rf urban road.ref Target Inwhichbkg: urban_road.ref 0.0001 Bkg Scale: 1.00 25 1.0 LO Bkg file Name: 1.00 1.0 0 Background Bkg Percentage(%): 50,40,10 Sensor Altitude(km): Cloud Index: 1.0 0 Meteorological Range: 10 Wavelength To Choose: 453.8 467.4 481.9 496.9 511.7 526.5 541.6 556.5 571.2 585.9 600.7 Wavelength Chosen: A F1g add all Figure 4. Parameter Selection 4. MODEL VERIFICATION AND APPLICATION For model verification, real world data collected from the RIT SHARE 2012 data collection campaign8 is used. In order to validate the new implemented model for subpixel target detection, first the system is tested in the scenario of a large uniform target and predicted spectral radiance output is compared with the collected radiance. The pre-measured spectral reflectance data of targets are used as the input for the prediction model, and the atmospheric parameters are adjusted according to the real collection. Fig.5(a) and Fig.5(b) show consistency Proc. of SPIE Vol. 8870 88700P-4 Downloaded From: http://spiedigitallibrary.org/ on 12/02/2013 Terms of Use: http://spiedl.org/terms Average Collected Radiance of Black Tarp Predicted Radiance of Black Tarp Average Collected Radlance of White Tarp Predicted Radiance of White Tarp (a) White Tarp (b) Black Tarp Figure 5. Tarp Validation 12 14 Predicted Radiance for Grass Collected Radiance for Grass 12 10 Ê y Collected Grass Sample Eigenvalues Predicted Grass Sample Eigenvalues 10 8 E 3 8 E 6 N 4 2 2 0 400 600 800 1000 1200 1400 1600 Wavelength(nm) 1800 2000 2200 2400 (a) Grass Radiance 2 3 4 5 Eigenvalue Rank 6 7 8 (b) Grass Covariance Matrix Eigen Values Figure 6. Grass Validation between collected and predicted data on white and black tarps. It is worth mentioning the noticeable difference of the black tarp in the near-infrared band is as a result of the vegetation in the adjacent part of the scene, which contributes a lot to the near-infrared region, especially since the total radiance is low for the black tarp. Then the grass class is also used to validate the model. Fig.6(a) shows very little difference between collected and predicted radiance data for grass, which means the prediction model works well. Also, Fig.6(b) shows consistency between the principle eigenvalues of covariance matrices predicted by the model and estimated by the collected data. So the model is developed and implemented well, and the result is reliable. 5. SUBPIXEL TARGET DETECTION TRADEOFF ANALYSIS In addition to the calibration target validation, it is very important for end-to-end model validation to be investigated at the output, which in a subpixel target detection scenario is to predict the detection probability at a specified false alarm rate. The receiver operating characteristic (ROC) curve characterizes the tradeoff between the detection probability and false alarm rate. The detection probability is related to target pixel fill Proc. of SPIE Vol. 8870 88700P-5 Downloaded From: http://spiedigitallibrary.org/ on 12/02/2013 Terms of Use: http://spiedl.org/terms factor, meteorological range, sensor relative calibration error, specified false alarm rate, etc. This prediction model could be used to analyze the tradeoff between those parameters. A typical scenario is defined in Table 1. This is a multi-background scene with a real subpixel target and it is pre-defined in our implemented model. In this setting, several parameters are variables to analyze the trade-off between these and probability of detection. Table 1. Parameter Table Parameter Value Atmospheric Haze Rural Hazy Ground Altitude 0km Solar Angle 60 Atmospheric Model Mid Latitude Summer Meteorological Range 5km - 30km Sensor HyMap Sensor Intergration Time 0.001s Sensor Altitude 1km Sensor Channels 126 Sensor Relative Calibration Error 1% - 4% Target Profile Green BMW Car Target Percentage 5% - 30% Target in Background Road Background File Tree, Grass, Road Background Percentage 50% Tree, 40% Grass, 10% Road The scenario in the table above defines a typical scene of vehicle detection when the vehicle is on the road in an open vegetation area. Fig.7(a) shows the relationship among pixel fill factor, probability of detection and meteorological range at a fixed false alarm rate, which is 10−5 in this scene, and 1% relative calibration error. Meteorological range will affect the radiance signal even though the system includes an atmospheric compensation step. The figure shows better detection performance with larger meteorological range and larger pixel fill factor. When the meteorological range is larger than 10km, it shows only minor effect to the system performance. Fig.7(b) shows the relationship among pixel fill factor, probability of detection and relative calibration error at the fixed 10−5 false alarm rate and 20km meteorological range. This calibration error may come from many sources in a real remote sensing system, such as non-uniformity correction or errors in the atmospheric compensation. The figure shows a relatively uniform relationship between this error and the system performance. 6. SUMMARY This paper presents a hyperspectral imaging system analytical model including its implementation, validation and its application to subpixel target detection. The sensor model has been extended to include the airborne ProspecTIR instrument, and the data from this instrument have been used to validate the system. The model implementation is presented by introducing its workflow and interface. Then the system is verified by comparing the collected real world data from the RIT SHARE 2012 with the model predicted data. The predicted radiance data of relatively simple scenario such as single background calibration tarps and grass show good consistency with the collected data from ProspecTIR instrument. Subpixel target spectral radiance statistics are predicted Proc. of SPIE Vol. 8870 88700P-6 Downloaded From: http://spiedigitallibrary.org/ on 12/02/2013 Terms of Use: http://spiedl.org/terms 1 1 0.9 - 0.9 - 0.8 - 0.8 - = 0.7 -0 = 0.7 -0 d 0.6 - d d 0.6 0 ó 0.5 d 0 ó 0.5 11 0.4 0.4 0 0.3- 0.3- 0.2 - 0.2 - 0.1 - 0.1 - 0 0 0 5 10 15 20 25 30 10 Pixel Fill Factor( %) 15 20 25 30 Pixel Fill Factor( %) (a) Sensitivity of Detection to Meteorological Range (b) Sensitivity of Detection to Relative Calibration Error Figure 7. Trade-off Analysis by this model with pre-measured background, target spectral reflectance statistics, and pre-defined varying scene parameters. The example of parameter trade-offs such as meteorological range, relative calibration and pixel fill factor with system detection performance are given and analyzed to explain the utility of this model for hyperspectral imaging system design and research. ACKNOWLEDGMENTS The authors gratefully acknowledge the work and support from Robert Krzaczek in establishing the website hosting our implemented model and the support and advice from Dr. David Messinger, Director of Digital Imaging Remote Sensing Laboratory, RIT, Dr. Jeff Pelz, Director of Visual Perception Lab, RIT, also the other graduate students’ help in the research. REFERENCES [1] Kerekes, J. P., Baum, J. E., and Farrar, K. E., “Analytical model of hyperspectral system performance,” in [Infrared Imaging Systems: Design, Analysis, Modeling and Testing X ], Proc. 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M., and et al, “MODTRAN4: radiative transfer modeling for remote sensing,” in [Optics in Atmospheric Propagation and Adaptive Systems III], Proc. SPIE, 3866(2), 2–10 (1999). [7] Smith, G. M. and Milton, E. J., “The use of the empirical line method to calibrate remotely sensed data to reflectance,” International Journal of Remote Sensing 20(13), 2653–2662 (1999). [8] Giannandrea, A., Raqueno, N., Messinger, D. W., Faulring, J., Kerekes, J. P., van Aardt, J., Canham, K., Hagstrom, S., Ontiveros, E., Gerace, A., Kaufman, J., Vongsy, K. M., Griffith, H., Bartlett, B. D., Ientilucci, E., Meola, J., Scarff, L., and Daniel, B., “The SHARE 2012 data campaign,” in [Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX], Proc. SPIE, 8743(15), 1–15 (2013). Proc. of SPIE Vol. 8870 88700P-7 Downloaded From: http://spiedigitallibrary.org/ on 12/02/2013 Terms of Use: http://spiedl.org/terms