This PDF file contains the front matter associated with SPIE Proceedings Volume 8706, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

Current high-resolution hyperspectral cameras attempt to correct misregistration errors in hardware. Usually, it is required that aberrations in the optical system must be controlled with precision 0.1 pixel or smaller. This severely limits other specifications of the hyperspectral camera, such as spatial resolution and light gathering capacity, and often requires very tight tolerances. If resampling is used to correct keystone in software instead of in hardware, then these stringent requirements could be lifted. Preliminary designs show that a resampling camera should be able to resolve at least 3000-5000 pixels, while at the same time collecting up to four times more light than the majority of current high spatial resolution cameras that correct keystone in hardware (HW corrected cameras). A Virtual Camera software, specifically developed for this purpose, was used to compare the performance of resampling cameras and HW corrected cameras. For the cameras where a large keystone is corrected by resampling, different resampling methods are investigated. Different criteria are suggested for quantifying performance, and the tested cameras are compared according to these criteria. The simulations showed that the performance of a resampling camera is comparable to that of a HW corrected camera with 0.1 pixel residual keystone, and that the use of a more advanced resampling method than the commonly used linear interpolation – such as for instance high-resolution cubic splines – is highly beneficial for the data quality of the resampled image. Our findings suggest that if high-resolution sensors are available, it would be better to use resampling instead of trying to correct keystone in hardware.

Image uniformity and accurate radiometric calibration are key features of state-of-the-art infrared cameras. Over the past years several non-uniformity correction and radiometric calibration techniques have been developed. In this paper we present and compare different techniques: 2-point calibration, CNUC™/multipoint’s calibration and Telops’ Real-Time Image Processing (patent-pending). For each method we assess the performances, the ease of use, the advantages and drawbacks as well as the most important operational limitations considering a broad range of exposure times, ambient and scene temperatures.

Spatial noise added to temporal noise will affect both the detection and the classification ability of staring image sensors. The spatial noise is due to non-uniform pixels and is also called fixed pattern noise (FPN), though it is not totally static but varies slowly in time, which is due to sensor drift. The sensor drift is mainly due to variability in the ambient temperature and hence the temperature of camera elements, which may be a concern in field trials and the subsequent analysis of the image data. The performance of a non-uniformity correction (NUC) depends on the characteristics of the spatial noise in the image data, in addition to the correction method. In this paper six different quality metrics are studied, aiming to quantify the non-uniformity in collected image data and to validate the performance of a set of NUC methods. The set of methods has been applied on various kinds of real image data recorded with three different imaging sensors in the infrared spectral region, where image data may be severely distorted by fixed pattern noise. Calculated image quality metrics for image data have been compared with results from a visual evaluation. A conclusion is that image quality metrics are useful tools that enable an objective rating of image quality.

Infrared imaging is commonly used for performing thermography based on field calibration that simply relates image levels to apparent temperature levels using field blackbodies. Under normal conditions, the correlation between the image levels and blackbody temperature is strong, allowing conversion of the raw data into units of blackbody-equivalent temperature without consideration of other factors. However, if certain instrument anomalies are present, a compensation procedure that involves more in-depth sensor characterization may be required. The procedure, which uses an analysis of temperature-dependent dark current, optical emissions, and detector response, is described along with results for a specific case. The procedure involves first cold soaking a thermal camera and then observing the cooldown behavior of the sensor under non-stressing conditions. Variations in environmental temperature levels are then used to observe cooler performance and dark current levels. A multi-variate linear regression is performed that allows temperature-dependent dark current, lens emission, lens transmission, and detector quantum efficiency to be fully characterized. The resulting data describe for each image pixel a relationship between the scene temperature and the observed values of image signal, detector temperature, and camera temperature. The procedure has been applied successfully to a thermal imager used to collect field data while suffering from instrument anomalies due to a faulty cooler. Using the resulting characterization data for the pixel-dependent dark current, image data collected with the thermal imager was compensated. The compensation involved using spatial filtering to determine temperature shifts caused by the faulty cooler based on the predictable pattern of pixel-to-pixel variations in dark current. The estimated temperature shift was used to compute a compensation offset for each pixel based on its known dark current coefficient. The compensated image data, while still degraded, was sufficiently corrected for the predictable effects of dark current variations to allow valid thermography to be performed.

The market for military-use Generation 2 and Generation 3 image intensifier (I2) tubes has become truly global, with major manufacturers and customers spanning five continents. This worldwide market is becoming increasingly important to U.S. manufacturers, with the majority of U.S. Army intensifier fielding having been completed in 2012. Given this keen global competition, it is not surprising that the advertised tube performance of a given source is often discounted by competitors, and the customers have no objective "honest broker" to determine the relative accuracy of these claims. To help fill this void, the U.S. Army RDECOM CERDEC NVESD recently measured a number of domestic and foreign image intensifier tubes, using consistent test equipment/procedures with which the U.S. industry must correlate for Army tube deliveries. Data and analysis will be presented for the major tube parameters of luminance gain, equivalent background input (EBI), signal-to-noise ratio (SNR), limiting resolution, halo, and modulation transfer function (MTF). The bright-light resolution provided by various auto-gated and non-gated tubes will also be addressed, since this area has been an important factor in the international market. RDECOM CERDEC NVESD measurement data will be compared to the corresponding manufacturer specifications whenever possible.

For over 30 years, the U.S. Army Aviation and Missile Research, Development, and Engineering Center (AMRDEC) has specialized in characterizing the performance of infrared (IR) imaging systems in the laboratory and field. In the late 90’s, AMRDEC developed the Automated IR Sensor Test Facility (AISTF) which allowed efficient deployment testing of Unmanned Aerial Systems (UAS) payloads. More recently, ImageJ has been used predominately as the image processing environment of choice for analysis of laboratory, field, and simulated data. The strengths of ImageJ are that it is maintained by the U.S. National Institute of Health, it exists in the public domain, and it functions on all major operating systems. Three new tools or “plugins” have been developed at AMRDEC to enhance the accuracy and efficiency of analysis. First, a Noise Equivalent Temperature Difference (NETD) plugin was written to process Signal Transfer Function (SiTF) and 3D noise data. Another plugin was produced that measures the Modulation Transfer Function (MTF) given either an edge or slit target. Lastly, a plugin was developed to measure Focal Plane Array (FPA) defects, classify and bin the customizable defects, and report statistics. This paper will document the capabilities and practical applications of these tools as well as profile their advantages over previous methods of analysis.

SBIR is developing an enhanced blackbody for improved radiometric testing. The main feature of the blackbody is an improved coating with higher emissivity than the standard coating used. Comparative measurements of the standard and improved coatings are reported, including reflectance. The coatings were also tested with infrared imagers and a broadband emissivity estimate derived from the imagery data. In addition, a control algorithm for constant slew rate has been implemented, primarily for use in minimum resolvable temperature measurements. The system was tested over a range of slew rates from 0.05 K/min to 10 K/min and its performance reported.

Active Short Wave InfraRed (SWIR) imaging presents unique challenges to laboratory testing. It is always important to have laboratory testing that will directly relate to field performance. This paper will present the modeling and corresponding laboratory testing that was developed for these types of systems. The paper will present the modeling that was used to derive the lab metric used for verification testing of the system and provide details into the design of the lab equipment that was necessary to ensure accurate lab testing. The Noise Limited Resolution (NLR) test, first developed for low light imaging systems in the 1960s, serves as the basic lab metric for the evaluation of the active SWIR system. This test serves well for a quick test (go-no go) and is used to evaluate this system during production testing. The test derivation will be described and shown how it relates to the modeling results. The test equipment developed by Santa Barbara InfraRed (SBIR) for this application allows for accurate uniform radiance levels from an integrating sphere for both 1.06um and 1.57um imaging applications. The source has the ability to directly mimic any laser system and can provide pulsed laser source radiation from 20 nanoseconds to 500 nanoseconds resulting in levels from 0.4 to 85 nJ/cm²/sr, peak radiance levels. The light source can be triggered to replicate a laser return at any range from 100m to 100,000m. Additionally, the source provides the ability to output Mid Wave IR (MWIR) illumination through the use of a small extended area IR source in the integrating sphere. This is useful for boresighting the active SWIR sensor with other sensors such as Forward Looking IR (FLIR).

Dome-input pyroelectric radiometers with different black coatings were developed to extend the spectral responsivity scale from near infrared (NIR) to 20 μm. The reflective dome with shiny gold-coating has been known to be an efficient light trap to enhance the detector absorptance and to minimize spectral responsivity variation. The enhancement of spectral responsivity using reflective dome relies on optical characterization of black coating on detector, reflectance of dome reflector, and input aperture dimension, etc. We report a comparison of spectral responsivity of dome-input pyroelectric radiometers measured with/without dome-trap from 2.4 μm to 14 μm using the Infrared Spectral Comparator Facility (IRSCF) at NIST. The results show 4 % to 8 % gain of responsivity for two dome-input pyroelectric detectors, with reduced structure of spectral responsivity. The uncertainty of dome-input pyroelectric radiometer calibrations is approximately 2 % (k = 2).

Human task performance with imaging sensors is characterized by perception experiments involving ensembles of observers viewing an ensemble of task relevant images from real sensors. Summary statistics from perception experiments are used, along with detailed descriptions of the sensors and early human vision processes to build predictive models such as NV-IPM. Use of these models typically requires knowledge of more than 100 specific parameters regarding the sensor, the viewing conditions, and the task. In this research we seek to do a blind prediction of task performance using task relevant image ensembles and image processing operations that produce statistically similar outputs to those obtained in real human perception experiments. We restrict our investigation to the task of identifying tracked vehicles. The data we seek to replicate through image processing are similarity matrices derived from the confusion matrices of actual perception experiments. This paper updates our work to date examining primarily the correspondence between several image processing approaches and perception data.

In this paper the mean time to acquire a stationary target by n stationary imaging sensors is computed using probability theory by making use of the well established result that the detection time for a single imaging sensor is a random variable from an exponential probability density function. Each imaging sensor is characterized by a separate P∞ value which describes the probability an observer using that sensor will eventually acquire the target and a separate t value which describes the mean time to acquire the target using that sensor. There is no restriction on the wavelength band used by the imaging sensor. There are no empirical constants in the model presented here and the results are in agreement with and generalize previously published equations. The newly developed equations have been verified by numerical simulations and also yield the expected mean detection time for all limiting values of the input parameters. The code used in the numerical simulations is exhibited. For any given scenario, the separate τ observer-sensor-target parameters P∞ and t can be estimated using the NV-IPM model or measured in perception experiments. Thus the input parameters needed by the model are generally available. Comparing results presented here with results from war game simulations such as OneSAF may improve the quality of both products.

Night vision equipment is crucial in order to accomplish supremacy and safety of the troops on the battlefield. Evidently, system integrators, MODs and end-users need access to reliable quantitative characterization of the expected field performance when using night vision equipment. The Image Intensifier tube is one of the most important engines driving the performance for night vision equipment. As a major tube manufacturer, PHOTONIS has investigated the link between its products physical design parameters and the actual end-user field performance. The developments include 1) an end-to-end performance measurement method and test facility, 2) an image-based night vision simulation and 3) a range estimation model. The purpose is twofold: i) being able to support the need of the integrators and end users, and ii) further systematic improvement of night vision equipment design. For the end-to-end test, PHOTONIS and TNO cooperated in the implementation of the TOD (Triangle Orientation Discrimination) test for night vision equipment. This test provides a clear and rigorous ranking of the products with respect to their target acquisition performance level. With respect to the image-based simulation, PHOTONIS performs physical and performance comparisons between artificial and real imagery, promising exciting further development of a model based on the merging of the different approaches of night vision evaluation and modelling. In this paper, we present the PHOTONIS night vision test laboratory, provide TOD results for a set of night vision devices and show range prediction examples.

This paper will focus on the effect of atmospheric conditions on EO sensor performance using computer models. We have shown the importance of accurately modeling atmospheric effects for predicting the performance of an EO sensor. A simple example has demonstrated how real conditions for several US sites will significantly change the Probability of Detection, and hence Recognition and Identification. The current state-of-the-art model for computing atmospheric transmission and radiance is, MODTRAN® 5, developed by the US Air Force Research Laboratory and Spectral Science, Inc. Research by the US Air Force, Navy and Army resulted in the public release of LOWTRAN 2 in the early 1970’s. Subsequent releases of LOWTRAN and MODTRAN® have continued until the present. The corresponding state-of-the art in sensor models, is the NVESD’s NVThermIP and soon to be released, NV-IPM. These models have also undergone an evolutionary process starting with the FLIR and ACQUIRE models to the present. NVThermIP, versions 2002 through 2009. NVTherm-IP has the capability to interface with 3rd party MODTRAN® software, along with a Beers law and table inputs. The replacement, NV-IPM, uses a different approach. It continues to use Beers law and table, however, its primary interface is via a library of pre-calculations. The capability to interface to a complete set of MODTRAN® inputs no longer exists. It is recommended that the full capabilities of MODTRAN® be used when modeling atmospheric transmission and radiance, and that this capability be added to the new NVESD NV-IPM.

In a broadband imaging system, the spectral information from the scene can be as important as the spatial information for the task of discriminating an object or feature of interest (the target) from a backdrop of other objects or features of lesser interest (the background) in the image of the scene. A useful measure of the ability to discriminate a target from its background is the apparent contrast between it and the background. The more diverse the scene is, the greater the apparent contrast can be between objects within the spectral passband of the imager. In broadband imaging, the net spectrally and spatially integrated radiances from a target and its background determine the apparent contrast, which in the reflective bands is a function of the spectra of the target and background reflectivities and the scene illumination. The impact of the scene illumination spectrum on apparent contrast can be significant to the point that a given target will be highly visible against a given background under one illumination source and yet hardly visible under a different source of illumination. This paper examines the impact of the spectral natures of the target, background, and the illumination on intrinsic Michelson contrast and target discrimination with notional reflective broadband imaging systems.

The Triangle Orientation Discrimination (TOD) test method was applied to characterize thermal and visual range performance of the Gatekeeper Electro Optical Security System. Gatekeeper developed by Thales Nederland BV, is currently in use with the Royal Netherlands Navy. The system houses uncooled infrared and colour TV cameras providing up to 360° view in azimuth. The images displayed to the operator are automatically optimized based on the scene intensity distribution. Because of this built-in scene-based optimization, proper measurement of the system requires careful surround illumination of the TOD setup over a large part of the camera Field Of View. The tests provided very accurate threshold estimates with relatively small observer differences. The resulting TOD curves that characterize the sensor system in terms of acuity and contrast sensitivity can be used as input to a Target Acquisition model to predict range performance for operational scenarios.

A clear and absolute method for discriminating between image fusion algorithm performances is presented. This method can effectively be used to assist in the design and modeling of image fusion systems. Specifically, it is postulated that quantifying human task performance using image fusion should be benchmarked to whether the fusion algorithm, at a minimum, retained the performance benefit achievable by each independent spectral band being fused. The established benchmark would then clearly represent the threshold that a fusion system should surpass to be considered beneficial to a particular task. A genetic algorithm is employed to characterize the fused system parameters using a Matlab® implementation of NVThermIP as the objective function. By setting the problem up as a mixed-integer constraint optimization problem, one can effectively look backwards through the image acquisition process: optimizing fused system parameters by minimizing the difference between modeled task difficulty measure and the benchmark task difficulty measure. The results of an identification perception experiment are presented, where human observers were asked to identify a standard set of military targets, and used to demonstrate the effectiveness of the benchmarking process.

A well-known phenomena that diminishes the recognition range in infrared imagery is atmospheric turbulence. In literature many methods are described that try to compensate for the distortions caused by atmospheric turbulence. Most of these methods use a global processing approach in which they assume a global shift and a uniform blurring in all frames. Because the effects of atmospheric turbulence are often spatial and temporal varying, we presented previous year a turbulence compensation method that performs local processing leading to excellent results. In this paper an improvement of this method is presented which uses a temporal moving reference frame in order to be capable of processing imagery containing moving objects as well as blur estimation to obtain adaptive deconvolution. Furthermore our method is evaluated in a quantitative way, which will give a good insight in which components of our method contribute to the obtained visual improvements.

This paper is the first of three papers associated with the military benefits of SWIR imaging. This paper describes the benefits associated with passive daytime operations with comparisons of SWIR, NIR, and VIS bands and sensors. This paper includes quantitative findings from previously published papers, analysis of open source data, summaries of various expert analyses, and calculations of notional system performance. We did not accept anecdotal findings as acceptable benefits. Topics include haze and fog penetration, atmospheric transmission, cloud and smoke penetration, target and background contrasts, spectral discrimination, turbulence degradation, and long range target identification. The second and third papers will address passive night imaging and active night imaging.

A model is being developed to evaluate the capabilities of various LWIR sensors and combinations of sensors to provide Local Area Awareness for satellites in low-Earth and geostationary orbit. The model being developed will be used to evaluate the system performance of LWIR detectors mounted at various locations on the satellite against multiple observation scenarios with multiple debris configurations. LWIR sensors have been chosen as the detector technology for the initial phase of research because of their ability to operate with the sun in their field of view (FOV) while imaging nearby debris in the long-wave infrared band without the need for additive components such as baffles or solar occluders. This report describes progress on the development of this model. Preliminary results demonstrate the modeling of debris and its LWIR signature for each simulated orbital path. Results are presented in terms of radiant flux of the tracked debris. Radiant flux results are shown for all times the observed debris can be seen by the observing satellite or sensor platform. These results are evaluated for each face, or side, of the observed debris, as well as a composite of all faces. It is shown that intensity-based detection and characterization techniques may be quantified from this research, based on the different emissivities and temperatures of certain space debris materials. The results presented in this report are of simulated debris in the local are of a GEO based sensing platform.

Our research deals with developing metrics for quantizing the contrast of target to background in color images, and investigating the degree such contrast affects the detection of targets by human observers. When dealing with a gray scale image, the only parameter affecting the detection of targets is the luminance contrast; in color images, there are several parameters. This research examines these parameters and defines the importance of each one of them. Our system parameters quantifying color images are based on CIELAB space (brightness-color). We examine how each axis in this space affects the contrast of the targets.

Recognition and identification ranges are limited to the quality of the images. Both the received contrast and the spatial resolution determine if objects are recognizable. Several aspects affect the image quality. First of all the sensor itself. The image quality depends on the size of the infrared detector array and the sensitivity. Second, also the intervening atmosphere, in particular over longer ranges, has an impact on the image quality. It degrades the contrast, due to transmission effects, as well as it influences the resolution, due to turbulence blur, of the image. We present studies in the field of infrared image enhancement. Several techniques are described: noise reduction, super resolution, turbulence compensation, contrast enhancement, stabilization. These techniques operate in real-time on COTS/MOTS platforms. They are especially effective in the army theatre, where long horizontal paths, and short line-of-sight limited urban operations are both present. Application of these techniques on observation masts, such as on military camp sites, and on UAVs and moving ground vehicles are discussed. Examples will be presented from several trials in which these techniques were demonstrated, including the presentation of test results.

Electro-Optic (EO) image sensors exhibit the properties of high resolution and low noise level, but they cannot reflect information about the temperature of objects and do not work in dark environments. On the other hand, infrared (IR) image sensors exhibit the properties of low resolution and high noise level, but IR images can reflect information about the temperature of objects all the time. Therefore, in this paper, we propose a novel framework to enhance the resolution of EO images using the information (e.g., temperature) from IR images, which helps distinguish temperature variation of objects in the daytime via high-resolution EO images. The proposed novel framework involves four main steps: (1) select target objects with temperature variation in original IR images; (2) fuse original RGB color (EO) images and IR images based on image fusion algorithms; (3) blend the fused images of target objects in proportion with original gray-scale EO images; (4) superimpose the target objects’ temperature information, onto original EO images via the modified NTSC color space transformation. Therein, the image fusion step will be conducted by qualitative (frame pipeline) approach. Revealing temperature information in EO images for the first time is the most significant contribution of this paper. Simulation results will show the transformed EO images with the targets’ temperature information.

A key input to any thermal infrared signature model is the environment, more specifically the model inputs specific to the thermal infrared background model. This paper describes a new method of analysing the climatic data for input to ShipIR. Historical hourly data from a stationary marine buoy are used to select a small number of data points (N=100) to adequately cover the range of statistics (CDF, PDF) displayed by the original data set (S=46,072). The method uses a coarse bin (1/3) to subdivide the variable space (3⁵=243 bins), and a single-point ranking system to select individual points so that uniform coverage (1/N = 0.01) is obtained for each variable. The selected data points are used in Vaitekunas and Kim (2013) to demonstrate how the new methodology is used to provide a more rigorous and comprehensive analysis of platform IR susceptibility based on the statistics of IR detection.

We find for infrared wavelengths there are clear particle size ranges and indices representative of fog and rain where the use of circular polarization imaging can penetrate to larger optical depths than linear polarization. Using polarization tracking Monte Carlo simulations for varying particle size, wavelength, and index systematically, we show that for specific scene parameters circular polarization vastly outperforms linear polarization in maintaining degree of polarization for large optical depths in transmission and reflection. This enhancement in circular polarization can be exploited to improve imaging in obscurant environments that are important in many critical imaging applications. Specifically, circular polarization performs better than linear for radiation fog in the SWIR and MWIR regime, advection fog in the LWIR regime, and small sized particles of Sahara dust in the MWIR regime.

A 3D simulation of the reflection of a Gaussian shaped laser beam on the dynamic sea surface is presented. The simulation is suitable for the pre-calculation of images for cameras operating in different spectral wavebands (visible, short wave infrared) for a bistatic configuration of laser source and receiver for different atmospheric conditions. In the visible waveband the calculated detected total power of reflected laser light from a 660nm laser source is compared with data collected in a field trial. Our computer simulation comprises the 3D simulation of a maritime scene (open sea/clear sky) and the simulation of laser beam reflected at the sea surface. The basic sea surface geometry is modeled by a composition of smooth wind driven gravity waves. To predict the view of a camera the sea surface radiance must be calculated for the specific waveband. Additionally, the radiances of laser light specularly reflected at the wind-roughened sea surface are modeled considering an analytical statistical sea surface BRDF (bidirectional reflectance distribution function). Validation of simulation results is prerequisite before applying the computer simulation to maritime laser applications. For validation purposes data (images and meteorological data) were selected from field measurements, using a 660nm cw-laser diode to produce laser beam reflection at the water surface and recording images by a TV camera. The validation is done by numerical comparison of measured total laser power extracted from recorded images with the corresponding simulation results. The results of the comparison are presented for different incident (zenith/azimuth) angles of the laser beam.

Reliable and accurate infrared characterization is necessary to measure the specific spectral signatures of aircrafts and associated infrared counter-measures protections (i.e. flares). Infrared characterization is essential to improve counter measures efficiency, improve friend-foe identification and reduce the risk of friendly fire. Typical infrared characterization measurement setups include a variety of panchromatic cameras and spectroradiometers. Each instrument brings essential information; cameras measure the spatial distribution of targets and spectroradiometers provide the spectral distribution of the emitted energy. However, the combination of separate instruments brings out possible radiometric errors and uncertainties that can be reduced with Hyperspectral imagers. These instruments combine both spectral and spatial information into the same data. These instruments measure both the spectral and spatial distribution of the energy at the same time ensuring the temporal and spatial cohesion of collected information. This paper presents a quantitative analysis of the main contributors of radiometric uncertainties and shows how a hyperspectral imager can reduce these uncertainties.

A methodology for analysing the infrared (IR) signature and susceptibility of naval platforms using ShipIR/NTCS was presented by Vaitekunas (2010). This paper provides three key improvements: use of a larger climatic data set (N=100), a new target sub-image algorithm eliminating false detections from pixel-aliasing at the horizon, and a new seeker model interfacing with a line-by-line background clutter model. Existing commercial stealth technologies (exhaust stack suppression, low solar absorptive paints, extended hull film-cooling) are re-analysed using the new models and methods to produce a more rigorous and comprehensive analysis of their effectiveness based on the statistics of reduction in IR susceptibility. These methods and results combined with the cost of each stealth option should allow platform managers to select an appropriate level of infrared suppression and establish the design criteria for a new ship.

We describe a method to simultaneously measure both thickness profile and refractive index distribution of a silicon wafer based on a lateral scanning of the wafer itself. By using dispersive interferometer principle based on a broadband source, which is a femtosecond pulse laser with 100 nm spectral bandwidth, both thickness profile and refractive index distribution can be measured at the same time using a single scanning operation along a lateral direction. The proposed measurement system was tested using an approximately 90 mm range with a 0.2 mm step along the center-line, except for the rim area in a ϕ100 silicon wafer. As a result, the thickness profile was determined to have a wedge-like shape with an approximately 2 μm difference at an averaged thickness of 478.03 μm. Also, the mean value of the refractive index distribution was 3.603, with an rms value of about 0.001. In addition, the measurement uncertainty of the thickness profile was evaluated by considering two uncertainty components that are related to the scanning operation, like the yaw motion of the motorized stage and the long-term stability of an optical path difference in an air path. The measurement reliability of both the thickness profile and refractive index distribution can be increased through several methods such as an analysis of the correlation between the thickness profile and the refractive index distribution and a comparative measurement using a contact-type method; these potential methods are the subject of our future work.

The uncertainties of measuring the geometrical thickness and refractive index of silicon wafers were evaluated. Both quantities of the geometrical thickness and refractive index were obtained using the previously proposed method based on spectral domain interferometry using the optical comb of a femtosecond pulse laser. The primary uncertainty factor was derived from the determination process of the optical path differences (OPDs) including the phase calculation, measurement repeatability, refractive index of air, and wavelength variation. The uncertainty for the phase calculation contains a Fourier transform in order to obtain the dominant periodic signal as well as an inverse Fourier transform with windowed filtering in order to calculate the phase value of the interference signal. The uncertainty for the measurement repeatability was estimated using the standard deviation of the measured optical path differences. During the experiments, the uncertainty of the refractive index of air should be considered for wavelengths in air because light travels through air. Because the optical path difference was determined based on the wavelength in use, the variation of the wavelength could also contribute to the overall measurement uncertainty. In addition, the uncertainty of the wavelength depends on the wavelength measurement accuracy of the sampling device, i.e. the optical spectrum analyzer. In this paper, the details on the uncertainty components are discussed, and future research for improving the performance of the measurement system is also proposed based on the uncertainty evaluation.

There are some difficulties in the development of uncooled focal plane array (FPA) detectors due to the absence of full simulation model which reflects the characterization of FPA detectors by variations of various parameters. In this paper we propose the simulator for the both readout integrated circuit (ROIC) and bolometer FPA which is based on a thermal equivalence equation of bolometer and mathematical modeling of optical and electrical part in infrared sensor system. The simulator shows the characteristics and the behaviors of individual components of infrared sensor system in the transient-state and steady-state. We present here the simulation results for output characteristics of detectors owing to variations of parameters induced non-uniformity in FPA detectors and find the dominant parameter to be the leading source non-uniformity in FPA detectors. We also present the simulation results for some typical ROICs to cancel the bias-heating which wastes most of the dynamic range of infrared sensor system. These show the effectiveness of compensation for the bias-heating according to variations of parameters. Using the proposed simulator we can expect the quantitative amount of non-uniformity due to the statistical variations in various processing steps and design of ROIC components. It can be used for the systematic design of infrared sensor system which cannot be performed in fabrication procedure.