The subject of this article is implementation of terahertz remote sensing for detection and imaging of concealed
objects from distances of several metres. Many materials used for packaging and clothing are partially transparent in the
spectral range 0.1 - 10 THz. The transparency property can be utilised to detect objects concealed by the materials, which
are often opaque in other spectral regions. This can be achieved by detecting the radiation from these objects through the
use of an appropriate detector, which is sensitive at THz frequencies. The radiation from the concealed objects can be
either self-emitted or reflected.
The use of THz remote sensing is being pursued in IARD by both theoretical and practical approaches. The
article contains a short review on the detectors, sources and components, which can be used for remote sensing systems
operating at THz frequencies, and describes energy calculations and system design considerations. Characteristic and
exemplar performance of the components, which are being used in IARD, is presented. The article then describes
prototypes of a passive THz radiometer and an active THz system, which were built in IARD. Performance
characteristics of both systems are described. The measurement results of the optical properties of various materials are
presented as well as examples of images obtained by the active THz system.
The characterization of separation between object spectral distributions by the use of any divergence-evolved
method, such as Informational Difference is problematic due to the relative sparsity of said
distributions. The existence of zero-probability points renders the calculation result irrelevant as the
separation is either infinite or undefined. A method to surmount this problem using available
experimental data is proposed.
We consider the statistical nature of measurement for all available visual data, e.g. pixel values, and
model the spectral distributions of these pixels as a congregate of Gaussian statistic measurements. The
inherent nature of Gaussian distributions smoothes over the zero-probability points of the original
discrete distribution, solving the divergence problem. The parameters of the Gaussian smoothing are experimentally determined.
The present paper addresses the issue of extraction, processing, and recognition of information from multi-spectral observations of our surroundings. A new method of dealing with multispectral recognition problems is developed, in which a physical thermodynamic model is used to describe the properties of the object classes in a multispectral image of a certain scene. According to the model, different groups of objects in the image are canonical populations that are in thermodynamic equilibrium with each other and with their surroundings. Between the objects act forces that result from a potential field. Various thermodynamic properties of the populations are calculated. The difference between two populations is evaluated by first bringing them to a common temperature and then using the informational difference as a difference measure.
The approach was implemented for a problem of combined formal and spectral classification of trees in a natural environment. The common temperature of two similar populations was varied until the separation between the populations reached a maximal value. A six-fold increase in the separation between the populations was achieved. In the future, we propose to use the Helmholtz free energy function as a quantity which attains a local minimum within each class of objects. An optimal classification scheme is one that minimizes the total free energy of the system.
A simple model for the spectral radiance of various objects in the field was developed at IARD. The model was verified in a field experiment in which the spectral radiances from three types of natural objects, in the 8-12 micron wavelength band, were measured. The results were compared with spectra that were calculated according to the model. At first, a rather poor agreement between the results was observed. Calculating the in-situ spectral emissivity values of the objects by application of a temperature-emissivity method alleviated this problem considerably. Further analysis has shown that most of the residual disagreement between the results was due to incorrect predictions of the path radiance that were made by the MODTRAN 4 code. Good agreement between the calculated and the measured radiance values was achieved after recalculating the path radiance by a physically based method. Another interesting result was that the influence of the sky radiation was not negligible and had to be dealt with properly.
A comparison of the spectral bands recommended through employment of different data separation measures and the reliability and robustness of these measures was performed on artificially generated target and background IR radiance data sets. The Mahalanobis distance, Signal to Clutter Ratio, Bhattacharya distance and Informational Difference criteria were employed in order to obtain the best single and paired spectral bands for data separation between two data classes of 'targets' and 'backgrounds' in day and night conditions. The results show that for conditions in which there is a distinct temperature difference between the two data classes, all the criteria perform similarly, with only small differences in the recommended spectral bands and general performance. However, in daylight conditions with multiple types of backgrounds and targets, criteria based on the assumption of concentrated data classes (SCR, Mahalanobis) tend to provide contradictory results, while those based on general statistical principles (Bhattacharya, Informational Difference) produce unequivocal results that are relatively unaffected by data set complexity.
The Informational Difference (InDiff) is a measure of the difference between two image sets. Previous work has shown that it can be used to explain result of important target recognition experiments involving human observers. In this paper we present the results of investigations on the suitability of using the InDiff as a measure of target conspicuity. First, the InDiff is defined and adapted to measuring the difference between two images, one containing a target on a background and the other - containing only the background. Second, we present results of two experiments involving human observers: In one experiment, gray level images of complex scenes were presented to the observers; the second experiment involved color images. The response times for detecting and of recognizing targets in these images were measured and the InDiff values for the images were calculated. Correlation coefficients of 0.60 - 0.85 were found between the InDiff values and the following quantities: Detection speed in both experiments, recognition speed in the gray-level experiment. Significant relations were found between the probability of correct detection or recognition and a quantity based on the InDiff, in the gray-level experiment. Finally we discuss possible applications of these findings and suggest extension to the formalism.
A model for analyzing issues involving monospectral target recognition is presented. These issues include modeling target detection, recognition and identification thresholds, and predicting the functional parametric dependencies of the results of observation experiments by human observers. The model makes extensive use of concepts used in Information Theory. An image of a certain scene is treated as a sample of an entire set of images of that particular scene. A difference measure, called the Informational Difference (InDif) between two image sets is defined. The main assertion is that accomplishing target recognition tasks is equivalent to setting thresholds for the InDif. The applicability of the InDif to the performance of the Human Visual System (HVS) is shown both analytically, in very simple situations, and in computer calculations involving noisy images. Finally, a single framework for dealing with the HVS and Artificial Intelligence systems is target recognition applications is shown to result naturally from the InDif formalism.
A procedure for calibration of a color video camera has been developed at EORD. The RGB values of standard samples, together with the spectral radiance values of the samples, are used to calculate a transformation matrix between the RGB and CIEXYZ color spaces. The transformation matrix is then used to calculate the XYZ color coordinates of distant objects imaged in the field. These, in turn, are used in order to calculate the CIELAB color coordinates of the objects. Good agreement between the calculated coordinates and those obtained from spectroradiometric data is achieved. Processing of the RGB values of pixels in the digital image of a scene using the CAMDET software package which was developed at EORD, results in `Painting Maps' in which the true apparent CIELAB color coordinates are used. The paper discusses the calibration procedure, its advantages and shortcomings and suggests a definition for the visible signature of objects. The Camdet software package is described and some examples are given.
Knowledge of background properties is essential for various applications such as systems engineering and evaluation (e.g. electro-optical sensors or for camouflage design), operational planning and development of ATR algorithms. A series of field tests was conducted in the NEGEV desert in Israel, as a joint effort of the FGAN-FfO (Germany) and EORD (Israel) for characterizing properties of backgrounds in arid climatic regions. Diurnal cycles of background surface temperatures were measured during summer and winter periods in several sites in the NEGEV. The measurement equipment consisted of imaging cameras, most of them calibrated, covering the spectral region from the visible up to the thermal infrared. This paper presents the measurement set- up, the measurement techniques that were used, and some of the first analysis results.
The factors affecting the spectral composition of radiation reaching a distant observer from a natural object, and thus determining its apparent color, are experimentally studied. A method to calculate the apparent color is examined in which the spectral radiance of a distant object is first measured at zero distance and variations in the apparent radiance are then studied as a function of the distance. Sample results are given.