This PDF file contains the front matter associated with SPIE Proceedings Volume 8731, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

The work presented in this paper is based on a dataset recorded with an airborne sensor. It comprises targets like M-60, M-47, M-113, bridge layers, tank retrievers, and trucks in various types of scenes. The background-object segmentation consists of first estimating the ground level everywhere in the scene, and then for each sample simply subtracting the measured height and ground level height. No assumptions concerning flat terrain etc. are made. Samples with height above ground level higher than a certain threshold are clustered by utilizing a straightforward agglomerative clustering algorithm. Around each cluster the bounding box with minimum volume is determined. Based on these bounding boxes, too small as well as too large clusters can easily be removed. However, vehicle-sized clutter will not be removed. Clutter detection is based on estimating the normal vector for a plane approximation around each sample. This approach is based on the fact that the surface normals of a vehicle is more “modulo 90°” distributed than clutter. The aim of the classification has been to classify main battle tanks (MBTs) Two types of algorithms have been studied, one based on Dempster Shafer fusion theory, and one model based. Our dataset comprises clusters of 269 vehicles (among them 131 MBTs), and 253 clutter objects (i.e. in practice vehiclesized bushes). The experiments we have carried out show that the segmentation extracts all vehicles, the clutter detection removes 90% of the clutter, and the classification finds more than 95% of the MBTs as well as removes half of the remaining clutter.

LiDAR data are available in a variety of publicly-accessible forums, providing high-resolution, accurate 3- dimensional information about objects at the Earth's surface. Automatic extraction of information from LiDAR point clouds, however, remains a challenging problem. The focus of this research is to develop methods for point cloud classification and object detection which can be customized for specific applications. The methods presented rely on analysis of statistics of local neighborhoods of LiDAR points. A multi-dimensional vector composed of these statistics can be classified using traditional data classification routines. Local neighborhood statistics are defined, and examples are given of the methods for specific applications such as building extraction and vegetation classification. Results indicate the feasibility of the local neighborhood statistics approach and provide a framework for the design of customized classification or object detection routines for LiDAR point clouds.

RIEGL LIDAR instruments based on echo digitization and on-line waveform processing provide valuable attributes to every detected target: calibrated amplitude, calibrated estimated target reflectance, and echo pulse deviation. Additional attributes could be provided by employing enhanced algorithms. In hydrography an estimate for the backscattering coefficient of a water column, for topographic targets an estimate of the angle of incidence of the laser beam on flat targets can be determined. We present data sets based on on-line waveform processing of RIEGL's V-Line and assess the possibility of deriving additional attributes by performing more sophisticated analysis of the waveform.

Airborne Light Detection and Ranging (LiDAR) is used in many 3D applications, such as urban planning, city modeling, facility management, and environmental assessments. LiDAR systems generate dense 3D point clouds, which provide a distinct and comprehensive geometrical description of object surfaces. However, the challenge is that most of the applications require correct identification and extraction of objects from LiDAR point clouds to facilitate quantitative descriptions. This paper presents a feature-level fusion approach between LiDAR and aerial color (RGB) imagery to separate urban vegetation and buildings from other urban classes/cover types. The classification method used structural and spectral features derived from LiDAR and RGB imagery. Features such as flatness and distribution of normal vectors were estimated from LiDAR data, while the non-calibrated normalized difference vegetation index (NDVI) was calculated by combining LiDAR intensity at 1064 nm with the red channel from the RGB imagery. Building roof tops have regular surfaces with smaller variation in surface normal, whereas tree points generate irregular surfaces. Tree points, on the other hand, exhibit higher NDVI values when compared to returns from other classes. To identify vegetation points an NDVI map was used, while a vegetation mask was also derived from the RGB imagery. Accuracy was assessed by comparing the extraction result with manually digitized reference data generated from the high spatial resolution RGB image. Classification results indicated good separation between building and vegetation and exhibited overall accuracies greater than 85%.

With the development of increasingly advanced airborne sensing systems, there is a growing need to support sensor system design, modeling, and product-algorithm development with explicit 3D structural ground truth commensurate to the scale of acquisition. Terrestrial laser scanning is one such technique which could provide this structural information. Commercial instrumentation to suit this purpose has existed for some time now, but cost can be a prohibitive barrier for some applications. As such we recently developed a unique laser scanning system from readily-available components, supporting low cost, highly portable, and rapid measurement of below-canopy 3D forest structure. Tools were developed to automatically reconstruct tree stem models as an initial step towards virtual forest scene generation. The objective of this paper is to assess the potential of this hardware/algorithm suite to reconstruct 3D stem information for a single scan of a New England hardwood forest site. Detailed tree stem structure (e.g., taper, sweep, and lean) is recovered for trees of varying diameter, species, and range from the sensor. Absolute stem diameter retrieval accuracy is 12.5%, with a 4.5% overestimation bias likely due to the LiDAR beam divergence.

Creation of 3D images through remote sensing is a topic of interest in many applications such as terrain / building modeling and automatic target recognition (ATR). Several photogrammetry-based methods have been proposed that derive 3D information from digital images from different perspectives, and lidar- based methods have been proposed that merge lidar point clouds and texture the merged point clouds with digital imagery. Image registra­ tion alone has difficulty with smooth regions with low contrast, whereas point cloud merging alone has difficulty with outliers and lack of proper convergence in the merging process. This paper presents a method to create 3D images that uses the unique properties of texel images (pixel­ fused lidar and digital imagery) to improve the quality and robustness of fused 3D images. The proposed method uses both image processing and point-cloud merging to combine texel images in an iterative technique. Since the digital image pixels and the lidar 3D points are fused at the sensor level, more accurate 3D images are generated because registration of image data automatically improves the merging of the point clouds, and vice versa. Examples illustrate the value of this method over other methods.

A novel use of Felzenszwalb’s graph based efficient image segmentation algorithm* is proposed for segmenting 3D volumetric foliage penetrating (FOPEN) Light Detection and Ranging (LiDAR) data for automated target detection. The authors propose using an approximate nearest neighbors algorithm to establish neighbors of points in 3D and thus form the graph for segmentation. Following graph formation, the angular difference in the points’ estimated normal vectors is proposed for the graph edge weights. Then the LiDAR data is segmented, in 3D, and metrics are calculated from the segments to determine their geometrical characteristics and thus likelihood of being a target. Finally, the bare earth within the scene is automatically identified to avoid confusion of flat bare earth with flat targets. The segmentation, the calculated metrics, and the bare earth all culminate in a target detection system deployed for FOPEN LiDAR. General purpose graphics processing units (GPGPUs) are leveraged to reduce processing times for the approximate nearest neighbors and point normal estimation algorithms such that the application can be run in near real time. Results are presented on several data sets.

Modern airborne LIDAR instruments are capable of accurately measuring fine detail, making them ideal for producing 3D models. A closely related problem is how best to map passive aerial imagery to the LIDAR-derived models. Typical solutions to this problem involve first constructing a surface representation from the LIDAR, then projecting imagery onto this surface. Unfortunately, a surface model can introduce errors into the process because it is not a good representation of the underlying scene geometry in areas containing overlapping or complex surfaces. A voxel-based 3D model of the LIDAR geometry is one alternative to the surface representation, and we show how this achieves more accurate results in complex areas when compared to existing approaches. Additional information we derive for the voxel model can also be used to assist with fusing the aerial imagery by driving quality metrics, and we demonstrate how this gives improved results. Multiple images covering the same area are required in order to capture details occluded in any single aerial photograph. We show how this occlusion affects fusion with the 3D model, and how any redundant color information can be filtered further to produce better products. Results are presented from our voxel-based fusion technique using LIDAR and coincident visible aerial imagery collected in the summer of 2011 over downtown Rochester, NY.

LiDAR is an efficient optical remote sensing technology that has application in geography, forestry, and defense. The effectiveness is often limited by signal-to-noise ratio (SNR). Geiger mode avalanche photodiode (APD) detectors are able to operate above critical voltage, and a single photoelectron can initiate the current surge, making the device very sensitive. These advantages come at the expense of requiring computationally intensive noise filtering techniques. Noise is a problem which affects the imaging system and reduces the capability. Common noise-reduction algorithms have drawbacks such as over aggressive filtering, or decimating in order to improve quality and performance. In recent years, there has been growing interest on GPUs (Graphics Processing Units) for their ability to perform powerful massive parallel processing. In this paper, we leverage this capability to reduce the processing latency. The Point Spread Function (PSF) filter algorithm is a local spatial measure that has been GPGPU accelerated. The idea is to use a kernel density estimation technique for point clustering. We associate a local likelihood measure with every point of the input data capturing the probability that a 3D point is true target-return photons or noise (background photons, dark-current). This process suppresses noise and allows for detection of outliers. We apply this approach to the LiDAR noise filtering problem for which we have recognized a speed-up factor of 30-50 times compared to traditional sequential CPU implementation.

Compression of LIDAR point cloud offers many challenges to the signal processing community. Compression schemes must preserve both the numerical and geometrical aspects of the data, while dealing with the sparsely distributed threedimensional nature of it. Very few effective compression methods have been developed for this type of data, and only a handful of those methods offer the advantages of scalability. The focus of this research and development activity was to design and implement a series of preprocessing techniques that address the common obstacles found when pursuing scalable LIDAR point cloud compression. Three main areas being addressed are spatial scalability by means of effective indexing techniques; range reduction and redundancy exploitation; and resolution scalability by means of sub-band decomposition and sampling. These techniques will be combined with two different entropy encoding schemes –namely LZW and MQ encoding, yielding scalable 12:1 compression rates.

Geiger-mode avalanche photodiode (GMAPD) Lidar systems can be used to image targets that are partially concealed by foliage. This application of GMAPD Lidar is challenging because most APDs operating in Geiger- mode report only one range measurement per transmitted laser pulse. If a GMAPD makes a foliage range measurement, it cannot make a range measurement to a target concealed by the foliage. When too much laser energy is received, the vast majority of range measurements are from the foliage and only a small percentage are from the target. Some GMAPD Lidar systems can report their average detection probability during operation. The average detection probability, which is often called “P-det”, is calculated over an array of GMAPDs, over multiple laser pulses, or over both. However, the detection probability does not distinguish between target range measurements, foliage range measurements, and noise events. In this paper, it is shown that when certain collection parameters are known, that the probability of detecting a target obscured by foliage can be maximized by selecting the appropriate “P-det”. It is also shown that for a typical foliage penetration scenario where most of the reflected laser energy is from the foliage that operating with a “P-det" between 65% and 80% produces a near-maximum target detection probability.

Two FPGA embedded programming approaches are considered and compared for a 20 kHz pulse repetition rate coherent Doppler lidar system which acquires return signals at 400 Msamples/second and operates with signal to noise ratios as low as -20 dB. In the first approach, the acquired return signal is gated in time and the square modulus of the fast Fourier transform is accumulated for each of the range gates, producing a series of power spectra as a function of range. Wind speed decisions based on numerical estimators can then be made after transferring the range gated accumulated power spectra to a host computer, enabling the line of sight wind speed as a function of range gate to be calculated and stored for additional processing. In the second FPGA approach, a digital IQ demodulator and down sampler reduces the data flow requirements so that an autocorrelation matrix representing a pre-selected number of lags can be accumulated, allowing for the process of range gating to be explored on the host computer. The added feature of the second approach is that it allows for an additional capability to adjust the range gate period dynamically as the state of the atmospheric boundary layer (e.g. backscatter coefficient and stability condition) changes. A simple manual beam scanning technique is used to calculate the wind field vector which is graphically displayed on time-height cross section plots. A comparison to other observed and modeled information is presented suggesting the usefulness for the characterization of microscale meteorology.

The mitigation of orbital debris was addressed in the most recent release of the National Space Policy directing space faring agencies to pursue technologies that will “mitigate and remove on-orbit debris.” No matter what abatement technology is developed and deployed, still lacking is the remote sensing infrastructure to locate and track these objects with adequate precision. We propose using GSFC's ground-based laser ranging facility to provide meter-level or better ranging precision on optically passive 10-30 cm orbital debris targets with the goal of improving current predictions up to 85%. The improved location accuracy also has the immediate benefit of reducing costly false alarms in collision predictions for existing assets.

Landing mission concepts that are being developed for exploration of solar system bodies are increasingly ambitious in their implementations and objectives. Most of these missions require accurate position and velocity data during their descent phase in order to ensure safe, soft landing at the pre-designated sites. Data from the vehicle’s Inertial Measurement Unit will not be sufficient due to significant drift error after extended travel time in space. Therefore, an onboard sensor is required to provide the necessary data for landing in the GPS-deprived environment of space. For this reason, NASA Langley Research Center has been developing an advanced Doppler lidar sensor capable of providing accurate and reliable data suitable for operation in the highly constrained environment of space. The Doppler lidar transmits three laser beams in different directions toward the ground. The signal from each beam provides the platform velocity and range to the ground along the laser line-of-sight (LOS). The six LOS measurements are then combined in order to determine the three components of the vehicle velocity vector, and to accurately measure altitude and attitude angles relative to the local ground. These measurements are used by an autonomous Guidance, Navigation, and Control system to accurately navigate the vehicle from a few kilometers above the ground to the designated location and to execute a gentle touchdown. A prototype version of our lidar sensor has been completed for a closed-loop demonstration onboard a rocket-powered terrestrial free-flyer vehicle.

Two flash lidars, integrated from a number of cutting-edge components from industry and NASA, are lab characterized and flight tested for determination of maximum operational range under the Autonomous Landing and Hazard Avoidance Technology (ALHAT) project (in its fourth development and field test cycle) which is seeking to develop a guidance, navigation, and control (GNC) and sensing system based on lidar technology capable of enabling safe, precise crewed or robotic landings in challenging terrain on planetary bodies under any ambient lighting conditions. The flash lidars incorporate pioneering 3-D imaging cameras based on Indium-Gallium-Arsenide Avalanche Photo Diode (InGaAs APD) and novel micro-electronic technology for a 128 x 128 pixel array operating at 30 Hz, high pulse-energy 1.06 μm Nd:YAG lasers, and high performance transmitter and receiver fixed and zoom optics. The two flash lidars are characterized on the NASA-Langley Research Center (LaRC) Sensor Test Range, integrated with other portions of the ALHAT GNC system from partner organizations into an instrument pod at NASA-JPL, integrated onto an Erickson Aircrane Helicopter at NASA-Dryden, and flight tested at the Edwards AFB Rogers dry lakebed over a field of humanmade geometric hazards during the summer of 2010. Results show that the maximum operational range goal of 1 km is met and exceeded up to a value of 1.2 km. In addition, calibrated 3-D images of several hazards are acquired in realtime for later reconstruction into Digital Elevation Maps (DEM’s).

Atmospheric aerosols have a significant impact on climate change through the scattering and absorption of incoming solar and outgoing thermal radiation. The aerosol optical properties can be directly measured using an elastic-Raman lidar. However, extraction of the extinction coefficient from the optical depth profile requires the use of numerical differentiation with these lidars, which suffers the random and systematic noises limitation and thereby reducing the detection sensitivity and accuracy. A new method to improve the quality of Raman lidar data processing is presented. Compared to the conventional method, the proposed method has the advantage, which can directly retrieve the aerosol extinction coefficients without numerical differentiation. Trial values of lidar ratio (from 10 to 90 sr with an increment of 1 sr) are applied to Fernald solution of the elastic lidar signals at 354.7 nm and all aerosol backscatter coefficients are obtained. The exact aerosol backscatter coefficients retrieved by combing elastic and Raman signals are used as constrain of these results of Fernald method to determine aerosol true lidar ratios as well as extinction coefficients. The numerical simulations demonstrated that the proposed method provides good accuracy and resolution of aerosol profile retrievals. And the method is also applied to elastic-Raman lidar measurements at the Hampton University, Hampton, Virginia.

Two versions of airborne wind profiling algorithms for the pulsed 2-micron coherent Doppler lidar system at NASA Langley Research Center in Virginia are presented. Each algorithm utilizes different number of line-of-sight (LOS) lidar returns while compensating the adverse effects of different coordinate systems between the aircraft and the Earth. One of the two algorithms APOLO (Airborne Wind Profiling Algorithm for Doppler Wind Lidar) estimates wind products using two LOSs. The other algorithm utilizes five LOSs. The airborne lidar data were acquired during the NASA’s Genesis and Rapid Intensification Processes (GRIP) campaign in 2010. The wind profile products from the two algorithms are compared with the dropsonde data to validate their results.

Studies were performed to carry out semi-empirical validation of a new measurement approach we propose for molecular mixing ratios determination. The approach is based on relative measurements in bands of O₂ and other molecules and as such may be best described as cross band relative absorption (CoBRA). The current validation studies rely upon well verified and established theoretical and experimental databases, satellite data assimilations and modeling codes such as HITRAN, line-by-line radiative transfer model (LBLRTM), and the modern-era retrospective analysis for research and applications (MERRA). The approach holds promise for atmospheric mixing ratio measurements of CO₂ and a variety of other molecules currently under investigation for several future satellite lidar missions. One of the advantages of the method is a significant reduction of the temperature sensitivity uncertainties which is illustrated with application to the ASCENDS mission for the measurement of CO₂ mixing ratios (XCO₂). Additional advantages of the method include the possibility to closely match cross-band weighting function combinations which is harder to achieve using conventional differential absorption techniques and the potential for additional corrections for water vapor and other interferences without using the data from numerical weather prediction (NWP) models.

Lidar is a powerful tool for measuring the vertical profiles of aerosols. Dusts are irregularly-shaped particles with varied composition and strong index of refraction variations in the LWIR. We measure dust indices using ellipsometry and transmission through KBr pellets. Milling makes the ellipsometry data less dependent on incidence angle, and the results of measurements on milled materials agree with those from transmission measurements. Measurements show that the spectrum of a milled Arizona Road Dust (ARD) approaches that of pure quartz, indicating a decrease of absorption efficiency for particles larger than the absorption length. These indices of refraction will be used in the future to simulate extinction for the beam of a LWIR lidar.

The optical scattering from laser beams propagating through atmospheric aerosols has been shown to be very useful in describing air pollution aerosol properties. This research explores and extends that capability to particulate matter. The optical properties of Arizona Road Dust (ARD) samples are measured in a chamber that simulates the particle dispersal of dust aerosols in the atmospheric environment. Visible, near infrared, and long wave infrared lasers are used. Optical scattering measurements show the expected dependence of laser wavelength and particle size on the extinction of laser beams. The extinction at long wavelengths demonstrates reduced scattering, but chemical absorption of dust species must be considered. The extinction and depolarization of laser wavelengths interacting with several size cuts of ARD are examined. The measurements include studies of different size distributions, and their evolution over time is recorded by an Aerodynamic Particle Sizer. We analyze the size-dependent extinction and depolarization of ARD. We present a method of predicting extinction for an arbitrary ARD size distribution. These studies provide new insights for understanding the optical propagation of laser beams through airborne particulate matter.

Aerosol optical scattering experiments are often large, expensive, and provide poor control of dust uniformity and size distribution. The size distribution of such suspended atmospheric aerosols varies rapidly in time, since larger particles settle quickly. Even in large chambers, 10 micron particles settle in tens of seconds. We describe lab-scale experiments with stable particle distributions. A viscous colloidal solution can stabilize the particles for sufficient time to measure optical scattering properties. Colloids with different concentrations or size distributions enable nearly time independent studies of prepared distributions. We perform laser aureole scattering from a colloid containing a few percent by volume of Arizona Road Dust (ARD) in mineral oil and glycerin, and 1-micron polystyrene spheres in water. We discuss aureole analysis, the differences expected in scattering properties due to the index of refraction of the mineral oil medium versus air, and the impact of non-spherical shape on the scattering. This research demonstrates that particles suspended in a viscous medium can be used to simulate aerosol optical scattering in air, while enabling signal averaging, offering reproducibility, and easing problems resulting from parameter variations in studies of dust properties.

Lidar is a powerful tool for measuring the vertical profiles of aerosols in the atmosphere using Rayleigh and Raman lidar techniques. Bistatic lidar can be used to obtain the angular structure of the scattered light. When the aerosols are uniformly distributed, this information can be analyzed to provide particle size distribution information. However, dusts tend to be irregularly shaped particles with varied composition. We investigate the impact of the irregular shape using optical scattering at several wavelengths, scanning electron microscopy, and T-matrix calculations. In particular, we study the rapid loss of Mie scattering resonances as the particle shape departs from spherical. Different size distributions produced by different size-cuts of Arizona Road Dust (ARD) are studied.

Super resolution techniques have been previously used enhance topographic data directly (instead of via enhancement of imagery). However, system performance degrades as the pattern database’s size increases. To keep system performance at an acceptable level, a smaller-than-optimal database must be utilized. This results in error being introduced due to differences in feature and average region height between the training and presented data. With the proposed approach, an area-average height value (AAHV) is computed. The work presented compares the averageheight- and-difference-pattern-based topographic approach to the previous actual-height-based version. The utility of incorporating an area of pattern overlap is also considered.

Airborne Lidar measurement technology, as an efficient way of acquiring three-dimensional geographic information, plays an important role in building DSM and DEM rapidly. Because the airborne Lidar measurement system usually integrates multiple devices including GPS receiver, INS, laser rangefinder and CCD camera, the relative geometric position and attitude relationships among these devices must be accurately measured in order to get the points with high precision and thereby satisfy the accuracy requirements of produced DSM and DEM. It is proved that the misalignment of airborne Lidar system, which is represented by angle deviations of yaw, pitch and roll, is the most significant source of systematic error in airborne Lidar measurement. In this paper, the effect of pitch angle error on the 3D coordinates of measured point is firstly analyzed. On this basis, a calibration method of the pitch angle deviation for airborne Lidar system by using the geometric characteristics of spire houses is put forward. The proposed pitch angle deviation calibration method consists of four key steps: (1) Initial pitch angle calculation. In the light of the offset distance between the ridge lines of the same house acquired by airborne Lidar system flying in opposite directions, an initial pitch angle deviation can be calculated. After separating the effect of pitch angle deviation, the rectified laser point cloud data are obtained. (2) Roof plane equation determination. The plane equations of both roof slopes are determined by fitting algorithms with the 3D coordinates of points located in the same spire roof. (3) Distance standard error calculation. The distance of each point to the roof plane is computed and applied to the calculation of distance standard error. (4) Final pitch angle deviation calculation. Taking the distance standard error as the overlapping criterion, the pitch angle deviation correction is iteratively calculated according to the aforesaid procedure until the distance standard error is less than a given value. The final pitch angle deviation is the sum of all the pitch angle deviation corrections. Experiments show that the proposed calibration method is correct and effective.

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.

Atmospheric turbulence produces intensity modulation or "scintillation" effects, on both on the outward path and on the return path, that degrade laser radar (ladar) target acquisition, ranging, and imaging. Quantitative previous measurements of ladar scintillation have used tiny flat mirrors and corner-cube retro-reflectors as their objects. In actuality, the real finite sized objects create scintillation averaging on the outgoing path and the finite sized telescope apertures produce scintillation averaging on the return path. We will quantify these effects and compare them to the tiny mirror and corner-cube retro-reflector quantitative data from the literature. Methods for modeling the outward path and the inward path scintillation effects and the target produced laser-speckle over arbitrary focal plane array detector areas will be discussed. The analysis of the ladar receiver-operating-characteristic (ROC) and signal-to-noise ratio (SNRp) or mean squared over a variance will also be discussed.

In this paper, we describe a detailed performance comparison of alternative single-pixel, single-mode LIDAR architectures including (i) linear-mode APD-based direct-detection, (ii) optically-preamplified PIN receiver, (iii) PINbased coherent-detection, and (iv) Geiger-mode single-photon-APD counting. Such a comparison is useful when considering next-generation LIDAR on a chip, which would allow one to leverage extensive waveguide-based structures and processing elements developed for telecom and apply them to small form-factor sensing applications. Models of four LIDAR transmit and receive systems are described in detail, which include not only the dominant sources of receiver noise commonly assumed in each of the four detection limits, but also additional noise terms present in realistic implementations. These receiver models are validated through the analysis of detection statistics collected from an experimental LIDAR testbed. The receiver is reconfigurable into four modes of operation, while transmit waveforms and channel characteristics are held constant. The use of a diffuse hard target highlights the importance of including speckle noise terms in the overall system analysis. All measurements are done at 1550 nm, which offers multiple system advantages including less stringent eye safety requirements and compatibility with available telecom components, optical amplification, and photonic integration. Ultimately, the experimentally-validated detection statistics can be used as part of an end-to-end system model for projecting rate, range, and resolution performance limits and tradeoffs of alternative integrated LIDAR architectures.

Recently Spectrolab has successfully demonstrated a compact 32x32 Laser Detection and Range (LADAR) camera with single photo-level sensitivity with small size, weight, and power (SWAP) budget for threedimensional (3D) topographic imaging at 1064 nm on various platforms. With 20-kHz frame rate and 500- ps timing uncertainty, this LADAR system provides coverage down to inch-level fidelity and allows for effective wide-area terrain mapping. At a 10 mph forward speed and 1000 feet above ground level (AGL), it covers 0.5 square-mile per hour with a resolution of 25 in2/pixel after data averaging. In order to increase the forward speed to fit for more platforms and survey a large area more effectively, Spectrolab is developing 32x128 Geiger-mode LADAR camera with 43 frame rate. With the increase in both frame rate and array size, the data collection rate is improved by 10 times. With a programmable bin size from 0.3 ps to 0.5 ns and 14-bit timing dynamic range, LADAR developers will have more freedom in system integration for various applications. Most of the special features of Spectrolab 32x32 LADAR camera, such as non-uniform bias correction, variable range gate width, windowing for smaller arrays, and short pixel protection, are implemented in this camera.

We have developed a LIDAR system with a sensor head which, although it includes a scanning mechanism, is less than 20 cc in size. The system is not only small, but is also highly sensitive. Our LIDAR system is based on time-of-flight measurements, and incorporates an optical fiber. The main feature of our system is the utilization of optical amplifiers for both the transmitter and the receiver, and the optical amplifiers enable us to exceed the detection limit set by thermal noise. In conventional LIDAR systems the detection limit is determined by the thermal noise, because the avalanche photo-diodes (APD) and trans-impedance amplifiers (TIA) that they use detect the received signals directly. In the case of our LIDAR system, the received signal is amplified by an optical fiber amplifier before reaching the photo diode and the TIA. Therefore, our LIDAR system boosts the signal level before the weak incoming signal is depleted by thermal noise. There are conditions under which the noise figure for the combination of an optical fiber amplifier and a photo diode is superior to the noise figure for an avalanche photo diode. We optimized the gains of the optical fiber amplifier and the TIA in our LIDAR system such that it would be capable of detecting a single photon. As a result, the detection limit of our system is determined by shot noise. We have previously demonstrated scanning up to a range of 80 m with this LIDAR system with a 2 mm diameter of receiving lens. We improved the optical amplifier and the peak output power of LIDAR was over 10KW. We redesigned the sensor-head and improved coupling efficiency. As a result, we succeeded in scanning over a range of 100 m. This small and highly sensitive measurement technology shows great potential for use in LIDAR.

The primary objective of the atmospheric profiling lidar aboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission launched in April 2006 has been studying the climate impact of clouds and aerosols in the atmosphere. However, CALIPSO lidar also collects information about other components of the Earth’s ecosystem, such as polar ice sheets. The purpose of this study is to propose a new technique to provide high resolution of polar ice sheet surface elevation from CALIPSO single shot lidar measurements (70 m spot size). The new technique relies on an empirical relationship between the peak signal ratio and the distance between the surface and the peak signal range bin center to achieve high altimetry resolution. The ice sheet surface elevation results in the region of Greenland and Antarctic compare very well with the Ice, Cloud and land Elevation Satellite (ICESat) laser altimetry measurements. The comparisons suggest that the obtained CALIPSO ice sheet surface elevation by the new technique is accurate to within 1 m. Based on the new technique, the preliminary data product of along-track topography retrieved from the CALIPSO lidar measurements is available to the altimetry community for evaluation.

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 forensic investigations.

Lidar backscatter signal resulting from laser light scattering from the aerosol and molecular in the atmosphere contains various information about the geometrical and physical properties of aerosol and molecular. The lidar backscatter signal can provide information about the planetary boundary layer (PBL) stratification by using aerosol as a tracer for convective and mixing processes. A PBL height and structure detecting technique based on the fractal dimension of three-wavelength backscatter signals is advanced. In this PBL height detecting technique, the three-wavelength backscatter signals are obtained by the Hampton University (HU, 37.02° N, 76.33° W) lidar. The fractal dimension was calculated using the three-wavelength lidar signals. The PBL heights obtained from fractal dimension of threewavelength lidar signals is compared with PBL heights obtained from the potential temperature profiles which are provided by NASA Langely Research Center (10 miles from HU). And results of the two methods agree well. Moreover, fractal dimension method can reduce the influence of the geometrical form factor on the PBL detecting to expand the detecting range of PBL and remove the effect of plume. Also, the fractal dimension method can show the PBL dynamics and the PBL evolution clearly.