Hyperspectral remote sensing based on unmanned airborne vehicles is a field increasing in importance. The combined functionality of simultaneous hyperspectral and geometric modeling is less developed. A configuration has been developed that enables the reconstruction of the hyperspectral three-dimensional (3D) environment. The hyperspectral camera is based on a linear variable filter and a high frame rate, high resolution camera enabling point-to-point matching and 3D reconstruction. This allows the information to be combined into a single and complete 3D hyperspectral model. In this paper, we describe the camera and illustrate capabilities and difficulties through real-world experiments.
The detection and classification of small surface targets at long ranges is a growing need for naval security. Simulations of a laser radar at 1.5 μm aimed for search, detect, and recognition of small maritime targets will be discussed. The data for the laser radar system will be based on present and realistic future technology. The simulated data generate signal waveforms for every pixel in the sensor field-of-view. From these we can also generate two-dimensional (2-D) and three-dimensional (3-D) range and intensity images. The simulations will incorporate typical target movements at different sea states, vessel courses, effects of the atmospheric turbulence and also include different beam jitter. The laser pulse energy, repetition rate as well as the receiver and detector parameters have been the same during the simulations. We have also used a high resolution (sub centimeter) laser radar based on time correlated single photon counting to acquire examples of range profiles from different small model ships. The collected waveforms are compared with simulated wave forms based on 3-D models of the ships. A discussion of the classification potential based on information in 1-D, 2-D, and 3-D data separately and in combination is made versus different environmental conditions and system parameters.
The detection and classification of small surface targets at long ranges is a growing need for naval security. This paper
will discuss simulations of a laser radar at 1.5 μm aimed for search, detect and recognition of small maritime targets.
The data for the laser radar system will be based on present and realistic future technology.
The simulations will incorporate typical target movements at different sea states, vessel courses, effects of the
atmosphere and for given laser system parameters also include different beam jitter. The laser pulse energy, repetition
rate as well as the receiver and detector parameters have not been changed during the simulations.
A discussion of the classification potential based on information in 1D, 2D and 3D data separately and in combination
will be made vs. different environmental conditions and system parameters. System issues when combining the laser
radar with IR/TV and a range-Doppler radar will also be commented.
The detection and classification of small surface targets at long ranges is a growing need for naval security. Laser range profiling offers a new capability for detecting and classifying such targets even if they appear as point (transversally unresolved) targets in radar or passive/active imaging EO sensors. Modifying a conventional laser range finder to have a higher range resolution can this increase it’s value as a sensor. Laser range profiles will reveal basic reflecting structures on the ship. The best information is obtained for profiles along the ship. Several range profiles from different aspects will increase the classification performance. If many aspects angles are possible a tomographic reconstruction of the ship may be done. We have used high resolution (sub cm) laser radar based on time correlated single photon counting (TCSPC) to acquire range profiles from different small model ships. The collected waveforms are compared with simulated wave forms based on 3 D models of the ships. A discussion of the classification accuracy based on the number of waveforms from different aspect angles is done as well as the influence of the reflectivity from different parts of the ship is made. The results are discussed with respect to the potential performance of modified laser range finder measuring on real ships and the combination with active imaging.
The identification of human targets including their activities and handheld objects at range is a prime military and
security capability. We have investigated this capability using active and passive imaging for video cameras, and sensors operating in the NIR and SWIR regions. For a limited data set we also compare sensor imagery from visible, NIR and SWIR sensors with that from a thermal imaging camera. The target recognition performance is studied vs. the gate position relative to the target, target range, turbulence conditions and target movement. A resolution chart is also included in the scene. The performance results from observer tests are compared with models and discussed from a system perspective.
This paper will report experiments and analysis of slant path imaging using 1.5 μm and 0.8 μm gated imaging. The
investigation is a follow up on the measurement reported last year at the laser radar conference at SPIE Orlando.
The sensor, a SWIR camera was collecting both passive and active images along a 2 km long path over an airfield. The
sensor was elevated by a lift in steps from 1.6-13.5 meters. Targets were resolution charts and also human targets. The
human target was holding various items and also performing certain tasks some of high of relevance in defence and
security. One of the main purposes with this investigation was to compare the recognition of these human targets and
their activities with the resolution information obtained from conventional resolution charts. The data collection of
human targets was also made from out roof top laboratory at about 13 m height above ground.
The turbulence was measured along the path with anemometers and scintillometers. The camera was collecting both
passive and active images in the SWIR region. We also included the Obzerv camera working at 0.8 μm in some tests.
The paper will present images for both passive and active modes obtained at different elevations and discuss the results
from both technical and system perspectives.
The new generation of laser-based imaging sensors enables collection of range images at video rate at the expense of
somewhat low spatial and range resolution. Combining several successive range images, instead of having to analyze
each image separately, is a way to improve the performance of feature extraction and target classification. In the robotics
community, occupancy grids are commonly used as a framework for combining sensor readings into a representation
that indicates passable (free) and non-passable (occupied) parts of the environment. In this paper we demonstrate how
3D occupancy grids can be used for outlier removal, registration quality assessment and measuring the degree of
unexplored space around a target, which may improve target detection and classification. Examples using data from a
maritime scene, acquired with a 3D FLASH sensor, are shown.
This paper will report experiments, analysis and simulations of slant path imaging using 1.5 μm gated imaging. The
measurements were taking place at a former airfield along a 2 km path. The sensor was elevated by a lift in steps from
2-12.5 meters. Targets were resolution charts. The turbulence was measured along the path with a scintillometer.
Turbulence information was also obtained at various path positions including the elevated cage using anemometers. The
camera was collecting both passive and active images in the SWIR region. In the passive mode (using solar illumination)
the noise due to speckles are eliminated and the influence by scintillation limited. In the active mode on the other hand
these noise sources are present to a varying degree depending on stabilized frame averaging and on the sensor elevation.
A trend is that the image quality is improved for elevated sensor positions. Two light sources in the camera FOV (head
lights from a car) gave independent turbulence level estimates.
The paper will present evaluated images for both passive and active modes obtained at different elevations and the result
will be compared with theory including image simulation.
A Bayesian approach for data reduction based on spatial filtering is proposed that enables detection of targets partly occluded by natural forest. The framework aims at creating a synergy between terrain mapping and target detection. It is demonstrates how spatial features can be extracted and combined in order to detect target samples in cluttered environments. In particular, it is illustrated how a priori scene information and assumptions about targets can be translated into algorithms for feature extraction. We also analyze the coupling between features and assumptions because it gives knowledge about which features are general enough to be useful in other environments and which are tailored for a specific situation. Two types of features are identified, nontarget indicators and target indicators. The filtering approach is based on a combination of several features. A theoretical framework for combining the features into a maximum likelihood classification scheme is presented. The approach is evaluated using data collected with a laser-based 3-D sensor in various forest environments with vehicles as targets. Over 70% of the target points are detected at a false-alarm rate of <1%. We also demonstrate how selecting different feature subsets influence the results.
Laser radars have the unique capability to give both intensity and full 3-D images of an object or a scene. The latest
addition to these capabilities is the possibility to acquire motion. These systems have many civilian and military
applications such as terrain modeling, depth sounding, object detection, classification and positioning as well as object
tracking. In order to fully understand the performance of laser radars vs experimental data a computer simulation model
is of high value. We have developed a modularized computer model capable of modeling performance of a variety of
laser radar systems. In order to derive the returned signal waveform from the object one has to account for the laser pulse
time characteristics, media effects such as the atmospheric attenuation and scattering as well as object characteristics like
shape, BRDF, surface roughness and others. The noise from target speckle and scintillations has to be coupled with
detector noise generated by the inherent noise in the detectors and subsequent amplifiers and read out circuitry as well as
the noise induced from the optical background. The result can be of help when designing and using new laser radar
systems, as well as extending real system parameters and evaluate performance. We will give examples of simulated
sensor data in different applications/scenarios together with some real measured data.
Laser-based 3D sensors measure range with high accuracy and allow for detection of several reflecting surfaces for each
emitted laser pulse. This makes them particularly suitable for sensing objects behind various types of occlusion, e.g.
camouflage nets and tree canopies. Nevertheless, automatic detection and recognition of targets in forested areas is a
challenging research problem, especially since foreground objects often cause targets to appear as fragmented.
In this paper we propose a sequential approach for detection and recognition of man-made objects in natural forest
environments using data from laser-based 3D sensors. First, ground samples and samples too far above the ground (that
cannot possibly originate from a target) are identified and removed from further processing. This step typically results in
a dramatic data reduction. Possible target samples are then detected using a local flatness criterion, based on the
assumption that targets are among the most structured objects in the remaining data. The set of samples is reduced
further through shadow analysis, where any possible target locations are found by identifying regions that are occluded
by foreground objects. Since we anticipate that targets appear as fragmented, the remaining samples are grouped into a
set of larger segments, based on general target characteristics such as maximal dimensions and generic shape. Finally,
the segments, each of which corresponds to a target hypothesis, undergo automatic target recognition in order to find the
best match from a model library. The approach is evaluated in terms of ROC on real data from scenes in forested areas.
In object/target reconstruction and recognition based on laser radar data, the range value's accuracy is important. The range data accuracy depends on the accuracy in the laser radar's detector, especially the algorithm used for time-of-flight estimation. In this paper, a general direct-detection laser radar system applicable for hard-target measurements is modeled. The time- and range-dependent laser radar cross sections are derived for some simple geometric shapes (plane, cone, sphere, and paraboloid). The cross-section models are used, in simulations, to find the proper statistical distribution of uncertainties in time-of-flight range estimations. Three time-of-flight estimation algorithms are analyzed: peak detection, constant-fraction detection, and matched filter. The detection performance for various shape conditions and signal-to-noise ratios is analyzed. Two simple shape reconstruction examples are shown, and the detectors' performance is compared with the Cramér-Rao lower bound. The performance of the peak detection and the constant-fraction detection is more dependent on the shape and noise level than that of the matched filter. For line fitting the matched filter performs close to the Cramér-Rao lower bound.
Range-gated or burst illumination systems have recently drawn a great deal of attention concerning the use for target classification. The development of eye-safe lasers and detectors will make these systems ideal to be combined with thermal imagers for long range targeting at night but also for short range security applications. This presentation will describe performance modelling and simulation of range-gated systems and discuss these together with experimental data.
One of the major advantages with laser sensors compared to passive optronic sensors, is the capability to penetrate
sparse vegetation. Therefore, the most limiting performance issue is the portion of laser "shots" being absorbed by the
foliage. This issue is the main focus in this paper and an analysis of the effect of forest vegetation of Nordic type is
presented. The conclusions are based on laser scanner measurement as well as photos. While the analysis covers several
elevation angles, the evaluation focuses on ground-to-ground measurements.
This article describes measurements of snow reflection using laser radar. There seem to be few publications on this subject. This article reports measurements on reflection from different kinds of snow, including the angular reflection properties. Reflectance information obtained from two commercial scanning laser radars working at the wavelengths 0.9 and 1.5 µm is shown and discussed. Data are mainly be presented at the eye-safe wavelength 1.5 µm, but some measurements were also performed at 0.9 µm. We have measured snow reflection during a part of a winter season that gave us opportunities to investigate different types of snow and different meteorological conditions. The reflection values tend to decrease during the first couple of hours after a snowfall. The structure of the snow seems to be more important for the reflection than its age. In general the reflection at 1.5 µm is rather low; the reflectivity can vary between 0.5% and 10% for oblique incidence, depending on the structure of the snow, which in turn depends on its age, the air temperature, the humidity, etc. The reflectivity at the 0.9-µm wavelength is much higher: more than 50% for fresh snow. Images of snow-covered scenes are shown together with reflection data, including bidirectional reflectance distribution functions.
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.
In this paper, we present techniques related to registration and change detection using 3D laser radar data. First, an experimental evaluation of a number of registration techniques based on the Iterative Closest Point algorithm is presented. As an extension, an approach for removing noisy points prior to the registration process by keypoint detection is also proposed. Since the success of accurate registration is typically dependent on a satisfactorily accurate starting estimate, coarse registration is an important functionality. We address this problem by proposing an approach for coarse 2D registration, which is based on detecting vertical structures (e.g. trees) in the point sets and then finding the transformation that gives the best alignment. Furthermore, a change detection approach based on voxelization of the registered data sets is presented. The 3D space is partitioned into a cell grid and a number of features for each cell are computed. Cells for which features have changed significantly (statistical outliers) then correspond to significant changes.
Rapid and efficient detection of surface mines, IED's (Improvised Explosive Devices) and UXO (Unexploded Ordnance) is of high priority in military conflicts. High range resolution laser radars combined with passive hyper/multispectral sensors offer an interesting concept to help solving this problem. This paper reports on laser radar data collection of various surface mines in different types of terrain.
In order to evaluate the capability of 3D imaging for detecting and classifying the objects of interest a scanning laser radar was used to scan mines and surrounding terrain with high angular and range resolution. These data were then fed into a laser radar model capable of generating range waveforms for a variety of system parameters and combinations of different targets and backgrounds. We can thus simulate a potential system by down sampling to relevant pixel sizes and laser/receiver characteristics. Data, simulations and examples will be presented.
Laser radars offer a potential for high range accuracy and range resolution due to short pulses and high bandwidth receivers. For angular non-resolved targets (1 D profiling) the analysis of the waveform offers the possibility of target recognition due to range profiling. For 2 D and 3 D imaging the angular and range resolution are the critical parameters for target recognition while in other applications such as in lidar mapping the range accuracy plays an important role for the performance. The development of the next generation laser radars including 3 D sensing focal plane arrays (FPAs) enable a full range and intensity image to be captured in one laser shot. Moreover, gated viewing systems also give a viable solution for providing 3 D target information. This paper uses simulation to illustrate the limits for accuracy and range resolution in waveform processing due to the laser pulse shape, detector noise, target shape and reflectivity as well as turbulence.
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 will describe measurements of snow reflection using laser radar. There seems to be a rather limited number
of publications on snow reflection related to laser radar, which is why we decided to investigate a little more details of
snow reflection including that from different kinds of snow as well as the angular reflection properties.
We will discuss reflectance information obtained by two commercial scanning laser radars using the wavelengths
0.9 μm and 1.5 μm. Data will mainly be presented at the eye safe wavelength 1.5 μm but some measurements were also
performed for the wavelength 0.9 μm. We have measured snow reflection during a part of a winter season which gave
us opportunities to investigate different types of snow and different meteorological conditions.
The reflection values tend to decrease during the first couple of hours after a snowfall. The snow structure seems to be
more important for the reflection than the snow age. In general the snow reflection at 1.5 μm is rather low and the
reflectivity values can vary between 0.5 and 10 % for oblique incidence depending on snow structure which in turn
depends on age, air temperature, humidity etc. The snow reflectivity at the 0.9 μm laser wavelength is much higher,
more than 50 % for fresh snow. Images of snow covered scenes will be shown together with reflection data including
Exciting development is taking place in 3 D sensing laser radars. Scanning systems are well established for mapping from airborne and ground sensors. 3 D sensing focal plane arrays (FPAs) enable a full range and intensity image can be captured in one laser shot. Gated viewing systems also produces 3 D target information. Many applications for 3 D laser radars are found in robotics, rapid terrain visualization, augmented vision, reconnaissance and target recognition, weapon guidance including aim point selection and others. The net centric warfare will demand high resolution geo-data for a common description of the environment. At FOI we have a measurement program to collect data relevant for 3 D laser radars using airborne and tripod mounted equipment for data collection. Data collection spans from single pixel waveform collection (1 D) over 2 D using range gated imaging to full 3 D imaging using scanning systems. This paper will describe 3 D laser data from different campaigns with emphasis on range distribution and reflections properties for targets and background during different seasonal conditions. Example of the use of the data for system modeling, performance prediction and algorithm development will be given. Different metrics to characterize the data set will also be discussed.
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.
Over the years imaging laser radar systems have been developed for both military and civilian (topographic) applications. Among the applications, 3D data is used for environment modeling and object reconstruction and recognition. The data processing methods are mainly developed separately for military or topographic applications, seldom both application areas are in mind. In this paper, an overview of methods from both areas is presented. First, some of the work on ground surface estimation and classification of natural objects, for example trees, is described. Once natural objects have been detected and classified, we review some of the extensive work on reconstruction and recognition of man-made objects. Primarily we address the reconstruction of buildings and recognition of vehicles. Further, some methods for evaluation of measurement systems and algorithms are described. Models of some types of laser radar systems are reviewed, based on both physical and statistical approaches, for analysis and evaluation of measurement systems and algorithms. The combination of methods for reconstruction of natural and man-made objects is also discussed. By combining methods originating from civilian and military applications, we believe that the tools to analyze a whole scene become available. In this paper we show examples where methods from both application fields are used to analyze a scene.
This paper presents our ongoing research activities on target recognition from data generated by 3-D imaging laser radar. In particular, we focus on future full flash imaging 3-D sensors. Several techniques for laser range imaging are applied for modelling and simulation of data from this kind of 3-D sensor systems. Firstly, data from an experimental gated viewing system is used. Processed data from this system is useful in assisting an operator in the target recognition task. Our recent work on target identification at long ranges, using range data from the gated viewing system, provides techniques to handle turbulence, platform motion and illumination variances from scintillation and speckle noise. Moreover, the range data is expanded into 3-D by using a gating technique that provides reconstruction of the target surface structure. This is shown at distances out to 7 km. Secondly, 3-D target data is achieved at short ranges by using different scanning laser radar systems. This provides high-resolution 3-D data from scanning a target from one single view. However, several scans from multiple viewing angles can also quite easily be merged for more detailed target representations. This is, for example, very useful for recognizing targets in vegetation. Hereby, we achieve simulated 3-D sensor data from both short and long
ranges (100 meters out to 7 km) at various spatial resolutions. Thirdly, real data from the 3-D flash imaging system by US Air Force Research Lab (AFRL/SNJM), Wright Patterson Air Force Base, has recently been made available to FOI and also used as input in the development of aided target recognition methods. High-resolution 3-D target models are used in the identification process and compared to the 3-D target data (point cloud) from the various laser radar systems. Finally, we give some examples from our work that clearly show that future 3-D laser radar systems in cooperation with signal- and image analysis techniques have a great potential in the non-cooperative target recognition task and will provide several new and interesting capabilities, for example, to reveal targets hidden in vegetation.
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.
Laser radars have the unique capability to give intensity and full 3-D images of an object. Doppler lidars can give velocity and vibration characteristics of an objects. These systems have many civilian and military applications such as terrain modelling, depth sounding, object detection and classification as well as object positioning. In order to derive the signal waveform from the object one has to account for the laser pulse time characteristics, media effects such as the atmospheric attenuation and turbulence effects or scattering properties, the target shape and reflection (BRDF), speckle noise together with the receiver and background noise. Finally the type of waveform processing (peak detection, leading edge etc.) is needed to model the sensor output to be compared with observations. We have developed a computer model which models performance of a 3-D laser radar. We will give examples of signal waveforms generated from model different targets calculated by integrating the laser beam profile in space and time over the target including reflection characteristics during different speckle and turbulence conditions. The result will be of help when designing and using new laser radar systems. The importance of different type of signal processing of the waveform in order to fulfil performance goals will be shown.