Tomas Chevalier

Range accuracy and resolution for laser radars

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.

Characterizing targets and backgrounds for 3D laser radars

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.

An approach to target detection in forested scenes

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.

Performance of 3D laser radar through vegetation and camouflage

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.

Simulation and modeling of laser range profiling and imaging of small surface vessels

Abstract. 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.

Active and passive short-wave infrared and near-infrared imaging for horizontal and slant paths close to ground.

This paper investigates active and passive short-wave infrared (SWIR) imaging for slant paths close to ground. The main sensor, a gated SWIR camera, was collecting both passive and active images along a 2 km long path over an airfield and also from our rooftop laboratory looking over open fields. For some investigations we also used a gated system working in the near-infrared region and thermal as well as color CCD cameras. The sensor was elevated by a lift in steps from 1.6-13.5 m or placed in a rooftop laboratory 13 m above ground. Targets were resolution charts and man targets. The turbulence was measured along the path with anemometers and scintillometers. The image performance was evaluated by measurement of the image blur and also by performing observer perception tests. The results reveal a strong dependence on the sensor height especially during daytime.

Spatial filtering for detection of partly occluded targets

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 radar modeling for simulation and performance evaluation

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.

Data collection and simulation of high range resolution laser radar for surface mine detection

Rapid and efficient detection of surface mines, IEDs (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.

Methods for recognition of natural and man-made objects using laser radar data

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.