To explore system sensitivities in hyperspectral subpixel target detection, multivariate methods are applied to output detection metrics generated from a statistical target detection model. The Forecasting and Analysis of Spectroradiometric System Performance (FASSP) statistical model generates probabilities of detection (PD) and false alarm (PFA) using spectral libraries of target and background materials. This allows for the computation of the area under the receiver operating characteristic curve (AUC). To explore sensitivities within elements (e.g. scene, atmosphere, sensor) of the remote sensing system, ensembles of model-based scenarios are generated using combinations of the aerosol visibility, solar angle, and sensor viewing angle. Output detection metrics (PD, AUC) from these scenarios were cached into a high-dimensional tensor, before utilizing multivariate methods (e.g. interpolation and regression) to explore sensitivities and correlations between system variables and detection. Inferences on limitations of detection within the system are drawn from multivariate contour regions which characterize joint parametric parameters required to exceed a desired threshold of detection. The outlined methods aim to provide an initial framework to investigate both specific and generalizable limitations of detection across various scenes (e.g. rural, urban, maritime, and desert), environmental conditions (e.g. solar angle, haze, clouds), sensor characteristics (e.g. noise, viewing angle) and processing configurations (e.g. feature selection, detector algorithm).
KEYWORDS: Target detection, Systems modeling, Atmospheric modeling, Data modeling, Sensors, Imaging systems, Signal to noise ratio, Hyperspectral target detection, Hyperspectral systems, Bidirectional reflectance transmission function
Systems modeling of hyperspectral instruments is effective for forecasting instrument performance of subpixel target detection. A new reconfiguration of the statistical model-based tool FASSP (Forecasting and Analysis of Spectroradiometric System Performance) is currently in development at the Rochester Institute of Technology, for purposes of exploring systems limitations in subpixel detection. To validate the baseline functioning of the statistical model, empirical analyses using real data were cross-examined with model predictions. The real data were collected from a field experiment using a hyperspectral sensor on-board an unmanned aerial system (UAS). To assist in model validation, a variety of novel subpixel targets, spanning a range of constant target percentages, were designed and deployed in the field. The UAS was advantageous in enabling the research team to maintain full end-to-end control of the system parameters within the experiment. This includes selecting specific flight lines, collecting ground truth spectral measurements, deploying specific targets, and processing raw data into geophysical units of surface reflectance. The study revealed close alignment between the empirical results and modeled predictions.
The CubeSat Infrared Atmospheric Sounder (CIRAS) is a remote sensing instrument under development for the demonstration and technology maturation of hyperspectral infrared sounding in a 6U CubeSat. The CIRAS instrument utilizes a 2D Focal Plane Array (FPA) of High Operating Temperature-Barrier Infrared Detectors (HOT-BIRD). The HOT-BIRD material provides improved uniformity, higher allowable operating temperatures, and lower 1/f noise. The performance of the HOT-BIRD FPA was tested inside a Integrated Dewar and Cryocooler Assembly (IDCA). Testing involved using an external Blackbody Calibrator Target (BCT) to generate numerous images of the object at varying temperatures of the scene. The data acquired was compared to a radiometric model to characterize the responsivity of the detectors. In addition, a solid angle correction was developed to improve the accuracy of the modeling. Real and synthetic images are presented in the comparative analysis to validate the expected responsivity of the detectors through calculation of the quantum efficiency.
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