Comprehensive hyperspectral system simulation: II. Hyperspectral sensor simulation and premliminary VNIR testing results

Bartell, Richard J.; Schwartz, Craig R.; Eismann, Michael T.; Cederquist, Jack N.; Nunez, James A.; Sanders, Lee Curtis; Ratcliff, Alan H.; Lyons, Barry W.; Ingle, Stephen D.

doi:10.1117/12.410380

Cited by 6 publications

(5 citation statements)

References 3 publications

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“…The first step is the calculation of the number of stimulated electrons (signal electrons) freeing from a detector element by equation [ 32 ]:

where D is the pupil aperture, f is the focus, A d is the area of the detector element, t is the integration time, τ is the transfer efficiency of the telescope, η d is diffraction efficiency of the Offner system, L is the input at-sensor radiance, η e is the quantum efficiency of the detector element, h is the Planck's constant, ν i is the frequency of the wavelength λ i , η is the ratio of the wavelength range of band i which lies on the element to the band width, and λ 1 and λ 2 are the wavelength boundaries which will locate in the element.…”

Section: Methodsmentioning

confidence: 99%

A Digital Sensor Simulator of the Pushbroom Offner Hyperspectral Imaging Spectrometer

Tao

Jia

Yuan

et al. 2014

Sensors

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Sensor simulators can be used in forecasting the imaging quality of a new hyperspectral imaging spectrometer, and generating simulated data for the development and validation of the data processing algorithms. This paper presents a novel digital sensor simulator for the pushbroom Offner hyperspectral imaging spectrometer, which is widely used in the hyperspectral remote sensing. Based on the imaging process, the sensor simulator consists of a spatial response module, a spectral response module, and a radiometric response module. In order to enhance the simulation accuracy, spatial interpolation-resampling, which is implemented before the spatial degradation, is developed to compromise the direction error and the extra aliasing effect. Instead of using the spectral response function (SRF), the dispersive imaging characteristics of the Offner convex grating optical system is accurately modeled by its configuration parameters. The non-uniformity characteristics, such as keystone and smile effects, are simulated in the corresponding modules. In this work, the spatial, spectral and radiometric calibration processes are simulated to provide the parameters of modulation transfer function (MTF), SRF and radiometric calibration parameters of the sensor simulator. Some uncertainty factors (the stability, band width of the monochromator for the spectral calibration, and the integrating sphere uncertainty for the radiometric calibration) are considered in the simulation of the calibration process. With the calibration parameters, several experiments were designed to validate the spatial, spectral and radiometric response of the sensor simulator, respectively. The experiment results indicate that the sensor simulator is valid.

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“…The first step is the calculation of the number of stimulated electrons (signal electrons) freeing from a detector element by equation [ 32 ]:

Section: Methodsmentioning

confidence: 99%

A Digital Sensor Simulator of the Pushbroom Offner Hyperspectral Imaging Spectrometer

Tao

Jia

Yuan

et al. 2014

Sensors

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“…Image blur due to the effects of diffraction, jitter, turbulence, drift, detector size, and wavefront error are applied as optical transfer functions (OTFs). 2,3 Figure 8 of the OTFs across 5 wavelength bands from the shortwave (1.315 μm) to longwave infrared (10 μm). In this example the shortwave infrared wavelength results are dominated by turbulence and detector size, while at the longer wavelengths diffraction effects dominate.…”

Section: Sensor Modelmentioning

confidence: 99%

Modeling bistatic spectral measurements of temporally evolving reflected and emitted energy from a distant and receding target

Cusumano

Fiorino

Bartell

et al. 2011

J. Appl. Rem. Sens.

Self Cite

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The Air Force Institute of Technology's Center for Directed Energy developed the High Energy Laser End-to-End Operational Simulation (HELEEOS) model in part to quantify the performance variability in laser propagation created by the natural environment during dynamic engagements. As such, HELEEOS includes a fast-calculating, first principles, worldwide surface-to-100 km, atmospheric propagation, and characterization package. This package enables the creation of profiles of temperature, pressure, water vapor content, optical turbulence, atmospheric particulates, and hydrometeors as they relate to line-by-line layer transmission, path, and background radiance at wavelengths from the ultraviolet to radio frequencies. In the current paper an example of a unique high fidelity simulation of a bistatic, time-varying five band multispectral remote observation of energy delivered on a distant and receding test object is presented for noncloudy conditions with aerosols. The multispectral example emphasizes atmospheric effects using HELEEOS, the interaction of the energy and the test object, the observed reflectance, and subsequent hot spot generated. A model of a sensor suite located on the surface is included to collect the diffuse reflected in-band laser radiation and the emitted radiance of the hot spot in four separate and spatially offset midwave infrared and longwave infrared bands. Particular care is taken in modeling the bidirectional reflectance distribution function of the delivered energy/target interaction to account for both the coupling of energy into the test object and the changes in reflectance as a function of temperature. The architecture supports any platform-target-observer geometry, geographic location, season, and time of day, and it provides for correct contributions of the sky-earth background. The simulation accurately models the thermal response, kinetics, turbulence, base disturbance, diffraction, and signal-to-noise ratios.

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“…The total detector noise (in electrons) is then converted back to noise equivalent spectral radiance and scaled by a user-specified noise factor before being added to the diagonal entries of the spectral covariance matrices as shown in (13) for each sensor spectral channel (13) The next noise source is relative calibration error . This error is assumed to be uncorrelated between spectral channels, with a standard deviation expressed as a percentage of the mean signal level.…”

Section: B Sensor Modelmentioning

confidence: 99%

“…A similar approach was adopted by Schwartz et al [11], to analyze multispectral sensor systems in mine detection applications. These statistical approaches contrast with image simulation models such as HySIM [12], [13] and DIRSIG [14], which produce synthetic images suitable for analysis with 0196 traditional tools. The statistical analytical models do not produce an image, but rather represent the characteristics of the scene classes by statistical models, and compute expected performance through the use of analytical equations.…”

mentioning

confidence: 99%

Spectral imaging system analytical model for subpixel object detection

Kerekes

Baum

2002

IEEE Trans. Geosci. Remote Sensing

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Abstract-Data from multispectral and hyperspectral imaging systems have been used in many applications including land cover classification, surface characterization, material identification, and spatially unresolved object detection. While these optical spectral imaging systems have provided useful data, their design and utility could be further enhanced by better understanding the sensitivities and relative roles of various system attributes; in particular, when application data product accuracy is used as a metric. To study system parameters in the context of land cover classification, an end-to-end remote sensing system modeling approach was previously developed. In this paper, we extend this model to subpixel object detection applications by including a linear mixing model for an unresolved object in a background and using object detection algorithms and probability of detection ( ) versus false alarm ( ) curves to characterize performance. Validations with results obtained from airborne hyperspectral data show good agreement between model predictions and the measured data. Examples are presented which show the utility of the modeling approach in understanding the relative importance of various system parameters and the sensitivity of versus curves to changes in the system for a subpixel road detection scenario.Index Terms-Hyperspectral imaging, multispectral imaging, remote sensing system modeling, subpixel object detection.

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Comprehensive hyperspectral system simulation: II. Hyperspectral sensor simulation and premliminary VNIR testing results

Cited by 6 publications

References 3 publications

A Digital Sensor Simulator of the Pushbroom Offner Hyperspectral Imaging Spectrometer

A Digital Sensor Simulator of the Pushbroom Offner Hyperspectral Imaging Spectrometer

Modeling bistatic spectral measurements of temporally evolving reflected and emitted energy from a distant and receding target

Spectral imaging system analytical model for subpixel object detection

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