KEYWORDS: Electro optical modeling, Sensors, Data modeling, Monte Carlo methods, Atmospheric modeling, RGB color model, Reflectivity, Solid modeling, Computer aided design, Temperature metrology
Computer programs for prediction of optical signatures of targets and backgrounds are valuable tools for signature assessment and signature management. Simulations make it possible to study optical signatures from targets and backgrounds under conditions where measured signatures are missing or incomplete. Several applications may be identified: Increase understanding, Design and assessment of low signature concepts, Assessment of tactics, Design and assessment of sensor systems, Duel simulations of EW, and Signature awareness. FOI (the Swedish Defence Research Agency) study several methods and modeling programs for detailed physically based prediction of the optical signature of targets in backgrounds. The most important commercial optical signature prediction programs available at FOI are CAMEO-SIM, RadThermIR, and McCavity. The main tasks of the work have been: Assembly of a database of input data, Gain experience of different computer programs, In-house development of complementary algorithms and programs, and Validation and assessment of the simulation results. This paper summarizes the activities and the results obtained. Some application examples will be given as well as results from validations. The test object chosen is the MTLB which is a tracked armored vehicle. It has been used previously at FOI for research purposes and therefore measurement data is available.
When using prediction programs for optical signatures, it is necessary to include validations to find estimates of the uncertainties and define the regions of validity. In this paper we present two paths of development of validation methods: The objective of the first path is to analyze and validate the differences between simulated and measured images, through image features such as edge concentration and different energy measures. In particular, aspects that are important for detection, classification and identification of targets are considered. The second path concerns development of methods for quantifying the propagation of input data uncertainties to output parameters in computational predictions. Two commercial codes have been used for the modeling: RadThermIR for thermal predictions of the targets and CAMEO-SIM for the radiometry and rendering. A recently developed interface between the two codes has been utilized. For the validation of spatial statistics, several feature values have been computed for a measured image and for the corresponding simulated image. It was found that the agreement was quite good. The work on propagation of uncertainties in computational predictions has resulted in a number of proposed methods. In this paper we present two different methods: one based on linear error propagation and one based on the Monte Carlo method. The results are according to expectations for both types of methods and show that a large part of the uncertainty in predicted temperature emanates from input parameter uncertainties for the considered test case.
KEYWORDS: Mid-IR, Long wavelength infrared, Data modeling, Temperature metrology, Databases, Calibration, Infrared imaging, Sun, Defense and security, FT-IR spectroscopy
The Swedish Defence Research Agency (FOI) has performed systematic measurements on a background scene over a period of one year, and established an IR-background database. It has, and will, be used for a wide range of applications and provide a basis for the modeling of IR-background properties of Swedish terrain. The database consists of traditional IR images and hyperspectral IR images, as well as weather data from the measurements. The major part of this paper describes the analysis of the hyperspectral data that was collected with the FTIR based ScanSpec system. In this paper the degree of greybody behavior from objects in the background have been studied in the LWIR and MWIR spectral regions. The analysis has been performed for seven different surfaces of the scene for different times of the day and different days of the year. The paper describes the method of the analysis, some results and attempts to find explanations to the observed phenomena. It is found that the background mainly behaves as greybodies and that the most important reason for deviations is the reflected sunlight. Among the studied surfaces asphalt shows the most obvious deviation from greybody behavior.
The Swedish Defence Research Agency (FOI) has recently performed systematic measurements in order to establish an IR-background database. It will be used for a wide range of applications and provide a basis for the modeling of IR-background properties of Swedish terrain. Experimental data like this is also necessary for the validation of methods and programs for synthetic IR-scene simulation. The data was collected from a varied background scene at the FOI site in Linkoeping. Several sensor systems were employed and the most important was a Thermovision 900 that measured for a 24-hour period once every month during a year. Simultaneously, registrations were made with one visual and one near-IR camera. For one day every season hyperspectral images were collected with ScanSpec - an imaging spectrometer designed at FOI. A weather station collected data during all the IR-measurements. The paper describes the data acquisition process, the instrumentation and the contents of the database. Some preliminary statistical analysis of the data is shown as well as an initial validation of a SensorVision IR-scene simulation.
The demand for hyperspectral imagers for research has increased in order to match the performance of new sensors for military applications. These work in several spectral bands and targets and backgrounds need to be characterized both spatially and spectrally to enable efficient signature analysis. Another task for a hyperspectral research imager is to acquire hyperspectral data to be able to study new hyperspectral signal processing techniques to detect, classify, and identify targets. This paper describes how a hyperspectral IR imager was developed based on an FTIR spectrometer at the Defence Research Establishment (FOA) in Linkoping, Sweden. The system, called ScanSpec, consists of a fast FTIR spectrometer from Bomem (MR254), an image-scanning mirror device with controlling electronics, and software for data collection and image forming. The spectrometer itself has not been modified. The paper also contains a performance evaluation with NESR NEDT, and MRTD analysis. Finally, some examples of hyperspectral results from field trials are presented: maritime background and remote gas detection.
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