A small, lightweight, and inexpensive hyperspectral camera based on a linear variable filter close to the focal plane array (FPA) is described. The use of a full-frame sensor allows large coverage with high spatial resolution at moderate spectral resolution. The spatial resolution has been maintained using a tilt/shift lens for chromatic focusing corrections. The trade-offs of positioning the filter relative to the FPA and varying the f-number have been studied. Calibration can correct for artifacts such as spectral filter variability. Reference spectra can be obtained using the same camera system by imaging targets over homogeneous areas. For textured surfaces, the different materials can be separated by using statistical methods. Accurate reconstruction of the sparse spectral image data is demonstrated.
An infrared image contains spatial and radiative information about objects in a scene. Two challenges are to classify pixels in a cluttered environment and to detect partly obscured or buried objects like mines and IEDs. Infrared image sequences provide additional temporal information, which can be utilized for a more robust object detection and an improved classification of object pixels. A manual evaluation of multi-dimensional data is generally time consuming and inefficient and therefore various algorithms are used. By a principal component analysis (PCA) most of the information is retained in a new, reduced system with fewer dimensions. The principal component coefficients (loadings) are here used both for classifying detected object pixels and for reducing the number of images in the analysis by computing of score vectors. For the datasets studied, the number of required images can be reduced significantly without loss of detection and classification ability. This allows for a more sparse sampling and scanning of larger areas when using a UAV, for example.
This paper briefly describes a field trial designed to give a realistic data set on a road section containing areas with
disturbed soil due to buried IEDs. During a time-span of a couple of weeks, the road was repeatedly imaged using a
multi-band sensor system with spectral coverage from visual to LWIR. The field trial was conducted to support a long
term research initiative aiming at using EO sensors and sensor fusion to detect areas of disturbed soil.
Samples from the collected data set is presented in the paper and shown together with an investigation on basic statistical
properties of the data. We conclude that upon visual inspection, it is fully possible to discover areas that have been
disturbed, either by using visual and/or IR sensors. Reviewing the statistical analysis made, we also conclude that
samples taken from both disturbed and undisturbed soil have well definable statistical distributions for all spectral bands.
We explore statistical tests to discriminate between different samples showing positive indications that discrimination
between disturbed and undisturbed soil is potentially possible using statistical methods.
An improvised explosive device (IED) is a bomb constructed and deployed in a non-standard manor. Improvised means that the bomb maker took whatever he could get his hands on, making it very hard to predict and detect. Nevertheless, the matters in which the IED’s are deployed and used, for example as roadside bombs, follow certain patterns. One possible approach for early warning is to record the surroundings when it is safe and use this as reference data for change detection. In this paper a LADAR-based system for IED detection is presented. The idea is to measure the area in front of the vehicle when driving and comparing this to the previously recorded reference data. By detecting new, missing or changed objects the system can make the driver aware of probable threats.
In the electro-optical sensors and processing in urban operations (ESUO) study we pave the way for the European Defence Agency (EDA) group of Electro-Optics experts (IAP03) for a common understanding of the optimal distribution of processing functions between the different platforms. Combinations of local, distributed and centralized processing are proposed. In this way one can match processing functionality to the required power, and available communication systems data rates, to obtain the desired reaction times. In the study, three priority scenarios were defined. For these scenarios, present-day and future sensors and signal processing technologies were studied. The priority scenarios were camp protection, patrol and house search. A method for analyzing information quality in single and multi-sensor systems has been applied. A method for estimating reaction times for transmission of data through the chain of command has been proposed and used. These methods are documented and can be used to modify scenarios, or be applied to other scenarios. Present day data processing is organized mainly locally. Very limited exchange of information with other platforms is present; this is performed mainly at a high information level. Main issues that arose from the analysis of present-day systems and methodology are the slow reaction time due to the limited field of view of present-day sensors and the lack of robust automated processing. Efficient handover schemes between wide and narrow field of view sensors may however reduce the delay times. The main effort in the study was in forecasting the signal processing of EO-sensors in the next ten to twenty years. Distributed processing is proposed between hand-held and vehicle based sensors. This can be accompanied by cloud processing on board several vehicles. Additionally, to perform sensor fusion on sensor data originating from different platforms, and making full use of UAV imagery, a combination of distributed and centralized processing is essential. There is a central role for sensor fusion of heterogeneous sensors in future processing. The changes that occur in the urban operations of the future due to the application of these new technologies will be the improved quality of information, with shorter reaction time, and with lower operator load.
One of the main threats for armed forces in conflict areas are attacks by improvised explosive devices (IED). After
an IED attack a forensic investigation of the site is undertaken. In many ways military forensic work is similar to the
civilian counterpart. There are the same needs to acquire evidence in the crime scene, such as fingerprints, DNA, and
samples of the remains of the IED. Photos have to be taken and the geometry of the location shall be measured,
preferably in 3D. A main difference between the military and the civilian forensic work is the time slot available for
the scene investigation. The military must work under the threat of fire assault, e.g. snipers. The short time slot puts
great demands on the forensic team and the equipment they use. We have done performance measurements of the
Mantis-Vision F5 sensor and evaluated the usefulness in military forensic applications. This paper will describe
some applications and show possibilities and also limitations of using a handheld laser imaging sensor for military
The European Defence Agency (EDA) launched the Active Imaging (ACTIM) study to investigate the potential of active
imaging, especially that of spectral laser imaging. The work included a literature survey, the identification of promising
military applications, system analyses, a roadmap and recommendations.
Passive multi- and hyper-spectral imaging allows discriminating between materials. But the measured radiance in the
sensor is difficult to relate to spectral reflectance due to the dependence on e.g. solar angle, clouds, shadows... In turn,
active spectral imaging offers a complete control of the illumination, thus eliminating these effects. In addition it allows
observing details at long ranges, seeing through degraded atmospheric conditions, penetrating obscurants (foliage,
camouflage...) or retrieving polarization information. When 3D, it is suited to producing numerical terrain models and to
performing geometry-based identification. Hence fusing the knowledge of ladar and passive spectral imaging will result
in new capabilities.
We have identified three main application areas for active imaging, and for spectral active imaging in particular: (1) long
range observation for identification, (2) mid-range mapping for reconnaissance, (3) shorter range perception for threat
detection. We present the system analyses that have been performed for confirming the interests, limitations and
requirements of spectral active imaging in these three prioritized applications.
This paper will describe ongoing work from an EDA initiated study on Active Imaging with emphasis of using multi or
broadband spectral lasers and receivers. Present laser based imaging and mapping systems are mostly based on a fixed
frequency lasers. On the other hand great progress has recently occurred in passive multi- and hyperspectral imaging
with applications ranging from environmental monitoring and geology to mapping, military surveillance, and
reconnaissance. Data bases on spectral signatures allow the possibility to discriminate between different materials in the
scene. Present multi- and hyperspectral sensors mainly operate in the visible and short wavelength region (0.4-2.5 μm)
and rely on the solar radiation giving shortcoming due to shadows, clouds, illumination angles and lack of night
operation. Active spectral imaging however will largely overcome these difficulties by a complete control of the
illumination. Active illumination enables spectral night and low-light operation beside a robust way of obtaining
polarization and high resolution 2D/3D information.
Recent development of broadband lasers and advanced imaging 3D focal plane arrays has led to new opportunities for
advanced spectral and polarization imaging with high range resolution. Fusing the knowledge of ladar and passive
spectral imaging will result in new capabilities in the field of
EO-sensing to be shown in the study. We will present an
overview of technology, systems and applications for active spectral imaging and propose future activities in connection
with some prioritized applications.
This paper describes the development of a high resolution waveform recording laser scanner and presents results
obtained with the system. When collecting 3-D data on small objects, high range and transverse resolution is needed. In
particular, if the objects are partly occluded by sparse materials such as vegetation, multiple returns from a single laser
pulse may limit the image quality. The ability to resolve multiple echoes depends mainly on the laser pulse width and the
receiver bandwidth. With the purpose to achieve high range resolution for multiple returns, we have developed a high
performance 3-D LIDAR, called HiPer, with a short pulse fibre laser (500 ps), fast detectors (70 ps rise time) and a 20
GS/s oscilloscope for fast sampling. HiPer can acquire the full waveform, which can be used for off-line processing. This
paper will describe the LIDAR system and present some image examples. The signal processing will also be described,
with some examples from the off-line processing and the benefit of using the complete waveform.
The multi-optical mine detection system (MOMS) is a research project focused on the detection of surface laid mines. In
the sensor suite, both passive and active sensors are included, such as IR as well as hyper- and multispectral cameras,
and 3-D laser radar.
Extensive field experiments have been conducted to collect data under various environmental conditions. Three seasons
have been covered during the field campaigns: Spring, summer, and autumn. Furthermore, the mines have been arranged
in three different types of vegetation scenarios. Also, a long term data collection effort has been conducted to collect
diurnal and seasonal signature variations.
Among the signal processing techniques considered, anomaly detection emerges as a key component in a system
concept. The method is based on detecting small differences between the mine-sized object and a local background. The
spectral features of the detected anomalies are further analyzed with respect to general commonalities in the scene and
known spectral properties of mine-like objects. In this paper we present some of the results from the project.
The objective of this paper is to present the Swedish land mine and UXO detection project named "Multi Optical Mine Detection System", MOMS with emphasis on system concept development. The aim of the project is to define and evaluate some system concepts, one of which may results in further development. Research and investigations carried out within the MOMS project during the first 3.5 years (of 5 in total) will shortly be described. Activities have mainly been focused on basic principles, scenarios, phenomena, and experimental studies using different passive and active EO sensors including signal processing. Based on these results a number of system concepts are suggested involving both airborne and ground vehicles as well as different sensor combinations. The different concepts will be discussed in relation to overall performance such as coverage rate, ROC (receiver operating characteristics) and complexity. Areas of uncertainties will be identified and suggestions for further investigations will be proposed.
Results from an experimental polarimetric investigation of 7 different types of land mines and 3 types of plants
with the aim to explore the possibility in discriminating surface land mines from natural backgrounds are presented.
The samples Mueller matrices at both specular and non-specular angles during 405 nm 1570 nm laser
illumination were collected. Also included in this study is reflection spectra from the mines taken from 400 to
2500 nm as well as actual images of surface land mines hidden in a natural environment during different weather
conditions. The mines had a reflection coefficient between 5-15 % with peak values around 510 nm due to the
embedded green pigment. The mines were found to be less reflective in wet compared to dry conditions. The
polarimetric study revealed that the samples had similar retardance and diattenuation values for small incident
angles, but that discrimination between the samples could be made by monitoring the depolarization of the incident
light for several incident angles, as a function of the angular distribution of scattered light. The land mines
generally experience less depolarization than the investigated plants, specifically for specular angles around 1570
nm where the mines act as a non-depolarizing sample with depolarization index close to 1.0. The depolarization
index is significantly smaller for specular angles from the plants, becoming 0.4 or below. Both plants and mines
experience more depolarization for non-specular angles. A non-specular angular scan with a constant bi-static
angle resulted in a Lorentzian shaped depolarization index curve, with characteristic differences in the fitted
line-shape parameters. Remote laser based polarimetry might thus be a promising supplementary technique
in recognizing surface mines or other related man-made objects from a natural background. Conclusively, the
depolarization index as a function of angular distribution of scattered light along with its wavelength dependence
is a metric that produce significant differences in the polarimetric signatures.
This paper presents the Swedish land mine and UXO detection project "Multi Optical Mine Detection System," MOMS, and the research carried out so far. The goal for MOMS is to provide knowledge and competence for fast detection of mines, especially surface laid mines, by the use of both active and passive optical sensors. A main activity was to collect information and gain knowledge about phenomenology; i.e. features or characteristics that can give a detectable signature or contrast between object and background, and to carry out a phenomenology assessment. A large effort has also been put into a scene description to support phenomenology assessment and provide a framework for further experimental campaigns. Also, some preliminary experimental results are presented and discussed.
The objective of this paper is to present the Swedish land mine and UXO detection project "Multi Optical Mine Detection System", MOMS. The goal for MOMS is to provide knowledge and competence for fast detection of mines, especially surface laid mines. The first phase, with duration 2005-2009, is essentially a feasibility study which focuses on the possibilities and limitations of a multi-sensor system with both active and passive EO-sensors. Sensor concepts used, in different combinations or single, includes 3-D imaging, retro reflection detection, multi-spectral imaging, thermal imaging, polarization and fluorescence. The aim of the MOMS project is presented and research and investigations carried out during the first years will be described.
As a part of the Swedish mine detection project MOMS, an initial field trial was conducted at the Swedish EOD and
Demining Centre (SWEDEC). The purpose was to collect data on surface-laid mines, UXO, submunitions, IED's, and
background with a variety of optical sensors, for further use in the project. Three terrain types were covered: forest,
gravel road, and an area which had recovered after total removal of all vegetation some years before. The sensors used in
the field trial included UV, VIS, and NIR sensors as well as thermal, multi-spectral, and hyper-spectral sensors, 3-D laser
radar and polarization sensors. Some of the sensors were mounted on an aerial work platform, while others were placed
on tripods on the ground. This paper describes the field trial and the presents some initial results obtained from the
The objective of this paper is to present the Swedish land mine and UXO detection project named "Multi Optical Mine Detection System," MOMS. Research and investigations carried out within the MOMS project during the first year will be described. Activities have mainly been focused on basic principles, phenomena, acquisition of knowledge and literature studies. The paper introduces the reader with the aim of the project and then the initial and future work is
One of the more exciting capabilities foreseen for future 3-D imaging laser radars is to see through vegetation and camouflage nettings. We have used ground based and airborne scanning laser radars to collect data of various types of terrain and vegetation. On some occasions reference targets were used to collect data on reflectivity and to evaluate penetration. The data contains reflectivity and range distributions and were collected at 1.5 and 1.06 μm wavelength with range accuracies in the 1-10 cm range. The seasonal variations for different types of vegetation have been studied. A preliminary evaluation of part of the data set was recently presented at another SPIE conference. Since then the data have been analyzed in more detail with emphasis on testing algorithms and future system performance by simulation of different sensors and scenarios. Evaluation methods will be discussed and some examples of data sets will be presented.
This paper wil give an overview of 3D laser sensing and related activities at the Swedish Defence Research Agency (FOI) in the view of system needs and applications. Our activites include data collection of laser signatures for target and backgrounds at various wavelengths. We will give examples of such measurements. The results are used in building sythetic environments, modellin of laser radar systems and as training sets for development of algorithms for target recognition and weapon applications. Present work on rapid environment assessment includes the use of data from airborne laser for terrain mapping and depth sounding. Methods for automatic target detection and object classification (buildings, trees, man-made objects etc.) have been developed together with techniques for visualisation. This will be described in more detail in a separate paper. The ability to find and correctly identify "difficult" targets, being either at very long ranges, hidden in the vegetation, behind windows or under camouflage, is one of the top priorities for any military force. Example of such work will be given using range gated imagery and 3D scanning laser radars. Different kinds of signal processing approaches have been studied and will be presented more in two separate papers. We have also developed modeling tools for both 2D and 3D laser imaging. Finally we will discuss the use of 3D laser radars in some system applications in the light of new component technology, processing needs and sensor fusion.
Target recognition is an important issue on the military battlefield. Vibration signatures are robust and independent on target orientation. Hence, they are interesting to use for classification and identification of targets. Various sensors can be used to measure signatures induced by target vibrations, such as laser vibrometry and acoustic sensors. The output from both sensors can be presented as a frequency spectrum that represents the target vibrations.
A field trial was conducted where some military targets were investigated. Simultaneous data were taken with two sensors: i) a laser vibrometry system consisting of a 1.55 μm eye-safe coherent laser radar; and ii) an acoustic data logging system with Bruel & Kjaer free field microphone and amplifier as the sensor part. The range to the targets was between 25 and 100 meters. Results from the field trial are reported and a comparison of the data from the sensors is presented.
Gated viewing using short pulse lasers and fast cameras offers many new possibilities in imaging compared with passive EO imaging. Among these we note ranging capability, large target-to-background contrast also in low visibility, good penetration capability trough obscurants and vegetation as well as through shadows in buildings, cars, etc. We also note that short wavelength laser systems have better angular resolution than long-wave infrared systems of the same aperture size. This gives an interesting potential of combined IR and laser systems for target detection and classification. Beside military applications civilian applications of gated viewing for search and rescue as well as vehicle enhanced vision and other applications are in progress. This presentation investigates the performance for gated viewing systems during different atmospheric conditions, including obscurants and gives examples of experimental data. The paper also deals with signal processing of gated viewing images for target detection. This is performed in two steps. First, image frames containing information of interest are found. In a second step those frames are investigated further to evaluate if man-made objects are present. In this step a sequence of images (video frames) are set up as a 3-D volume to incorporate spatial information. The object will then be detected using a set of quadrature filters operating on the volume.
Recently a number of airborne nadir scanning laser radars have been developed for both military and civilian applications. These have range resolutions on the order of 10 cm but relatively moderate area coverage rates, in the range 1000 - 10,000 m2/s (3.6 - 36 km2/h) when operating in a high resolution mode with 0.25 m spot distance. Technology development in laser sources, scanning techniques and signal processing will probably improve the area coverage substantially and lead to compact systems suitable for new applications, including the use in UAV:s. Present nadir capability could be combined with a forward looking capability for guidance and obstacle avoidance in autonomous or semi-autonomous systems. The paper will investigate the potential performance of such combined systems using state-of-the-art lasers and receiver technology. Among the applications for both military and civilian users we note the collection of 3-D data for terrain modeling and object recognition. For these functions signal processing using multiple echo and intensity information is of great value as well as adding passive senor information. Full wave form processing will further improve the information for example to characterize trees. The use of high resolution 3-D data in synthetic environments is obvious and will be discussed. Experimental data collected with a commercial laser system, TopEye, developed by Saab Dynamics, will be shown and some image examples will be discussed in relation to different applications.
A CO2-ladar system is used for measurements. The signal from the system is a sinusoidal FM-modulated multi-component signal. To extract the modulating frequencies time-frequency representations, e.g., the Wigner-Ville distribution and the Choi-Williams distribution are used. The estimation method is applied both to simulated and real data. Estimation of the vibration frequencies is shown to be feasible even for low SNR, e.g., -4 db.
A coherent laser radar system based on semiconductor laser technology has been designed and built. The compact design and the absence of adjustments makes the system mechanically robust and easy to use. The present system has an output power of 50 mW and a line width of 280 kHz (HWHM). The laser radar system has been used in vibrometry measurements. For vibrometry of moving objects, adaptive signal processing is required in order to obtain the vibration signature. Especially for unresolved objects, interference between different vibrating parts will complicate the analysis. Model based estimation techniques are used to obtain the parameters which determine the dynamics of the reflecting object.
Segmentation is a first step towards successful tracking and object recognition in 2-D pictures. Mostly the pictures are segmented with respect to quantities as range, intensity, etc. Here a method is presented for segmentation of 2-D laser range pictures with respect to both range and variance simultaneously. This is very useful since man-made objects differ from the background in the terrain by their smoothness. The approach is based on modeling horizontal scans of the terrain as piecewise constant functions. Since the environment has a complicated and irregular structure we use multiple models for modeling different segments in the laser range image. The switching between different models, i.e., ranges belonging to different segments in a horizontal scan, are modeled by a hidden Markov model. The method is of relatively low computational complexity and the maximal complexity can be controlled by the user. Real data is used for illustration of the method.
An approach to segmentation of laser radar range images is presented which is based on a terrain variation model. Topics discussed include the laser radar system with low and high drop-out probabilities, statistical methods for target/nontarget classification and terrain segmentation, and the resulting segmentation with respect to range and drop-out probabilities.