This paper reports how Electro-Optics (EO) technologies such as thermal and hyperspectral [1-3] imaging methods can
be used for the detection of stress remotely. Emotional or physical stresses induce a surge of adrenaline in the blood
stream under the command of the sympathetic nerve system, which, cannot be suppressed by training. The onset of this
alleviated level of adrenaline triggers a number of physiological chain reactions in the body, such as dilation of pupil and
an increased feed of blood to muscles etc. The capture of physiological responses, specifically the increase of blood
volume to pupil, have been reported by Pavlidis's pioneer thermal imaging work [4-7] who has shown a remarkable
increase of skin temperature in the periorbital region at the onset of stress. Our data has shown that other areas such as
the forehead, neck and cheek also exhibit alleviated skin temperatures dependent on the types of stressors. Our result has
also observed very similar thermal patterns due to physical exercising, to the one that induced by other physical stressors,
apparently in contradiction to Pavlidis's work [8]. Furthermore, we have found patches of alleviated temperature regions
in the forehead forming patterns characteristic to the types of stressors, dependent on whether they are physical or
emotional in origin. These stress induced thermal patterns have been seen to be quite distinct to the one resulting from
having high fever.
Emotional or physical stresses induce a surge of adrenaline in the blood stream under the command of the sympathetic
nerve system, which, cannot be suppressed by training. The onset of this alleviated level of adrenaline triggers a number
of physiological chain reactions in the body, such as dilation of pupil and an increased feed of blood to muscles etc. This
paper reports for the first time how Electro-Optics (EO) technologies such as hyperspectral [1,2] and thermal imaging[3]
methods can be used for the detection of stress remotely. Preliminary result using hyperspectral imaging technique has
shown a positive identification of stress through an elevation of haemoglobin oxygenation saturation level in the facial
region, and the effect is seen more prominently for the physical stressor than the emotional one. However, all results
presented so far in this work have been interpreted together with the base line information as the reference point, and that
really has limited the overall usefulness of the developing technology. The present result has highlighted this drawback
and it prompts for the need of a quantitative assessment of the oxygenation saturation and to correlate it directly with the
stress level as the top priority of the next stage of research.
Conventional air-to-ground target acquisition processes treat the image stream in isolation from external data sources.
This ignores information that may be available through modern mission management systems which could be fused into
the detection process in order to provide enhanced performance. By way of an example relating to target detection, this
paper explores the use of a-priori knowledge and other sensor information in an adaptive architecture with the aim of
enhancing performance in decision making. The approach taken here is to use knowledge of target size, terrain elevation,
sensor geometry, solar geometry and atmospheric conditions to characterise the expected spatial and radiometric
characteristics of a target in terms of probability density functions. An important consideration in the construction of the
target probability density functions are the known errors in the a-priori knowledge. Potential targets are identified in the
imagery and their spatial and expected radiometric characteristics are used to compute the target likelihood. The adaptive
architecture is evaluated alongside a conventional non-adaptive algorithm using synthetic imagery representative of an
air-to-ground target acquisition scenario. Lastly, future enhancements to the adaptive scheme are discussed as well as
strategies for managing poor quality or absent a-priori information.
The degrading effect of the atmosphere on hyperspectral imagery has long been recognised as a major issue in applying
techniques such as spectrally-matched filters to hyperspectral data. There are a number of algorithms available in the
literature for the correction of hyperspectral data. However most of these approaches rely either on identifying objects
within a scene (e.g. water whose spectral characteristics are known) or by measuring the relative effects of certain
absorption features and using this to construct a model of the atmosphere which can then be used to correct the image. In
the work presented here, we propose an alternative approach which makes use of the fact that the effective number of
degrees of freedom in the atmosphere (transmission, path radiance and downwelling radiance with respect to
wavelength) is often substantially less than the number of degrees of freedom in the spectra of interest. This allows the
definition of a fixed set of invariant features (which may be linear or non-linear) from which reflectance spectra can be
approximately reconstructed irrespective of the particular atmosphere. The technique is demonstrated on a range of data
across the visible to near infra-red, mid-wave and long-wave infra-red regions, where its performance is quantified.
There are many reconnaissance tasks which involve an image analyst viewing data from hyperspectral imaging systems and attempting to interpret it. Hyperspectral image data is intrinsically hard to understand, even when armed with mathematical knowledge and a range of current processing algorithms. This research is motivated by the search for new ways to convey information about the spectral content of imagery to people. In order to develop and assess the novel algorithms proposed, we have developed a tool for transforming different aspects of spectral imagery into sounds that an analyst can hear. Trials have been conducted which show that the use of these sonic mappings can assist a user in tasks such as rejecting false alarms generated by automatic detection algorithms. This paper describes some of the techniques used and reports on the results of user trials.
This paper explores three related themes: the statistical nature of hyperspectral background clutter; why should it be like this; and how to exploit it in algorithms. We begin by reviewing the evidence for the non-Gaussian and in particular fat-tailed nature of hyperspectral background distributions. Following this we develop a simple statistical model that gives some insight into why the observed fat tails occur. We demonstrate that this model fits the background data for some hyperspectral data sets. Finally we make use of the model to develop hyperspectral detection algorithms and compare them to traditional algorithms on some real world data sets.
Hyperspectral imaging provides the potential to derive sub-pixel material abundances. This has significant utility in the detection of sub-pixel targets or targets concealed under canopy. The linear mixture model describes spectral data in terms of a basis set of pure material spectra or endmembers. The success of such a model is dependent on the choice and number of endmembers used and the unmixing process. Endmember spectra may come from field or laboratory measurements, however, differences between sensors and changes in environmental conditions may mean that the measurement is not representative of the material as found in the scene. Alternatively, a number of algorithms exist to select spectra from the data directly, but these assume pure examples of the complete set of materials exist within the imagery. In either case, the chosen set of endmembers may not optimally describe the data in a linear mixing sense. In this paper some new methods for endmember selection are presented. These are evaluated on hyperspectral imagery and the results compared with those of a well-known automatic selection technique. Finally, an improved unmixing architecture is proposed which is self-consistent in terms of endmember selection and the unmixing process.
KEYWORDS: Detection and tracking algorithms, Statistical analysis, Target detection, Hyperspectral imaging, Statistical modeling, Data modeling, Data analysis, Image processing, Monte Carlo methods, Sensors
Anomaly detection in hyperspectral imagery is a potentially powerful approach for detecting objects of military interest because it does not require atmospheric compensation or target signature libraries. A number of methods have been proposed in the literature, most of these require a parametric model of the background probability distribution to be estimated from the data. There are two potential difficulties with this. First a parametric model must be postulated which is capable of describing the background statistics to an adequate approximation. Most work has made use of the multivariate normal distribution. Secondly the parameters must be estimated sufficiently accurately - this can be problematic for the covariance matrix of high dimensional hyperspectral data. In this paper we present an alternative view and investigate the capabilities of anomaly detection algorithms starting from a minimal set of assumptions. In particular we only require the background pixels to be samples from an independent and identically distributed (iid) process, but do not require the construction of a model for this distribution. We investigate a number of simple measures of the 'strangeness' of a given pixel spectra with respect to the observed background. An algorithm is proposed for detecting anomalies in a self-consistent way. The effectiveness of the algorithms is compared with a well-known anomaly detection algorithm from the literature on real hyperspectral data sets.
The problem of the automatic detection and identification of military vehicles in hyperspectral imagery has many possible solutions. The availability and utility of library spectra and the ability to atmospherically correct image data has great influence on the choice of approach. This paper concentrates on providing a robust solution in the event that library spectra are unavailable or unreliable due to differing atmospheric conditions between the data and reference. The development of a number of techniques for the detection and identification of unknown objects in a scene has continued apace over the past few years. A number of these techniques have been integrated into a "Full System Model" (FSM) to provide an automatic and robust system drawing upon the advantages of each. The FSM makes use of novel anomaly detectors and spatial processing to extract objects of interest in the scene which are then identified by a pre-trained classifier, typically a multi-class support vector machine. From this point onwards adaptive feedback is used to control the processing of the system. Stages of the processing chain may be augmented by spectral matching and linear unmixing algorithms in an effort to achieve optimum results depending upon the type of data. The Full System Model is described and the boost in performance over each individual stage is demonstrated and discussed.
This paper investigates how the targeting capability of a distributed data fusion system can be improved though the use of intelligent sensor management. The research reported here builds upon previous results from QinetiQ's air-to-ground fusion programme and sensor management research. QinetiQ's previously reported software test-bed for developing and evaluating data fusion algorithms has been enhanced to include intelligent sensor management functions and weapon fly-out models. In this paper details of the enhancements are provided together with a review of the sensor management algorithms employed. These include flight path optimization of airborne sensors to minimize target state estimation error, sensor activation control and sightline management of individual sensors for optimal targeting performance. Initial results from investigative studies are presented and conclusions are drawn.
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