This PDF contains front matter associated with SPIE Proceedings Volume 8020, including title page, copyright information, table of contents, and the conference committee listing.

A new approach for the design and fabrication of a miniaturized SWIR Hyperspectral imager is described. Previously, good results were obtained with a VNIR Hyperspectral imager, by use of light propagation within bonded solid blocks of fused silica. These designs use the Offner design form, providing excellent, low distortion imaging. The same idea is applied to the SWIR Hyperspectral imager here, resulting in a microHSI^TM SWIR Hyperspectral sensor, capable of operating in the 850-1700 nm wavelength range. The microHSI spectrometer weighs 910 g from slit input to camera output. This spectrometer can accommodate custom foreoptics to adapt to a wide range of fields-of-view (FOV). The current application calls for a 15 degree FOV, and utilizes an InGaAs image sensor with a spatial format of 640 x 25 micron pixels. This results in a slit length of 16 mm, and a foreoptics focal length of 61 mm, operating at F# = 2.8. The resulting IFOV is 417 μrad for this application, and a spectral dispersion of 4.17 nm/pixel. A prototype SWIR microHSI was fabricated, and the blazed diffraction grating was embedded within the optical blocks, resulting in a 72% diffraction efficiency at the wavelength of 1020 nm. This spectrometer design is capable of accommodating slit lengths of up to 25.6 mm, which opens up a wide variety of applications. The microHSI concepts can be extended to other wavelength regions, and a miniaturized LWIR microHSI sensor is in the conceptual design stage.

A unique, hyperspectral imaging plane "on-a-chip" developed for deployment as a High Performance Payload (HPP) on a micro or small unmanned aerial vehicle is described. HPP employs nanophotonics technologies to create a focal plane array with very high fill factor fabricated using standard integrated circuit techniques. The spectral response of each pixel can be independently tuned and controlled over the entire spectral range of the camera. While the current HPP is designed to operate in the visible, the underlying physical principles of the device are applicable and potentially implementable from the UV through the long-wave infrared.

It is well known that non-uniform illumination of a spectrometer changes the measured spectra. Laboratory calibration of hyperspectral imaging systems is careful to minimize this effect by providing repeatable, uniform illumination. In hyperspectral measurements the real world images result in non-uniform illumination. We define the resulting variation as real-world noise and we compare real-world noise to other noise sources. Both in-flight performance and calibration transfer between instruments degrade significantly because of real-world noise.

An instrument for monocular passive ranging based on atmospheric oxygen absorption near 762 nm has been designed, built and deployed to track emissive targets. An intensified CCD array is coupled to variable band pass liquid crystal filter and 3.5 - 8.8 degree field of view optics. The system was first deployed for a ground test viewing a static jet engine in afterburner at ranges of 0.35 - 4.8 km, establishing a range error of 15%. The instrument was also flight tested in a C-12 imaging an the exhaust plume of another aircraft afterburner at ranges up to 11 km.

A novel charge coupled device (CCD) array enables the combination of imaging and semi-active laser (SAL) target designation to enhance seeker functionality at reduced inventory cost with lower collateral damage risk. The integration of SAL detection with imaging requires a high level of spatial and temporal resolution of the laser pulse detector and its correlation with the field of view of the imaging sensor so that laser spot location and code are presented with the image in real time. This evaluation of a novel SAL CCD detector concept shows that it is possible to achieve a temporal resolution in the region of 5μsec, an order of magnitude better than the basic requirement, and to achieve sensitivity to the laser pulse that allows operation in direct sunlight. The analysis indicates that the SAL CCD meets requirements using standard CCD processes. This paper reviews the detector architecture options and shows how the temporal, spatial and sensitivity requirements can be met.

A typical airborne ground surveillance radar is a multimode system with a ground moving target indicator (GMTI) mode for surveillance and tracking of moving ground targets and synthetic aperture radar (SAR) modes for imaging of terrain features and stationary ground targets. One of the key features of the GMTI mode is the ability to perform wide area surveillance (WAS) of a substantial ground area, and in addition to provide persistent surveillance of a pre-specified ground area over a long period of time. The accomplishment of this task requires careful optimization of radar parameters and careful planning of the platform orbits so as to minimize the time spent turning the aircraft and repositioning the radar. This paper defines the notion of surveillance orbit efficiency which, for constant speed flight, is simply the percentage of time spent on the straight legs of a race track orbit. It then examines the orbit efficiency for each of three cases depending on the assumed radar azimuth field of view (FOV). This paper is a modified version of work described in a MITRE Technical Report for the US Army.

The Navy recently began investing in the design of mission-specific payloads for the Small Tactical Unmanned Aircraft System (STUAS). STUAS is a Tier II size UAS with a roughly 35 pound mission payload and a gimbaled general-purpose electro optical/infrared (EO/IR) system. The EO/IR system is likely composed of a video camera in the visible, a mid-wave infrared (MWIR) and/or a long-wave infrared (LWIR) for night operations, and an infrared marker and laser range finder. Advanced Coherent Technologies, LLC (ACT), in a series of SBIR efforts, has developed a modular, multi-channel imaging system for deployment on airborne and UAV platforms. ACT's system, called EYE5, demonstrates how an EO/IR system combined with an on-board, real-time processor can be tailored for specific applications to produce real-time actionable data. The EYE5 sensor head and modular real-time processor descriptions are presented in this work. Examples of the system's abilities in various Navy-relevant applications are reviewed.

In order to meet volume requirement and provide high image quality for a Long Range Oblique Photography (LOROP) system, we adopted Cassegrain-type telescope with lens compensators for the operation in both regions of 0.6 ~ 0.9 μm (EO channel) and 3.7 ~ 4.8 μm (IR channel). To provide dual-band functionality, the tilted plane-parallel plate is applied and acts as a beam splitter located in the space between primary and secondary mirrors. The system is near to telecentric in detector space (EO) and telecentric in intermediate image space (IR). The telecentricity provides image height constancy while adjusting the focus. The optical system includes Back Scan Mechanism (BSM) to compensate image blurring for integration time.

The availability of imagery simultaneously collected from sensors of disparate modalities enhances an image analyst's situational awareness and expands the overall detection capability to a larger array of target classes. Dynamic cooperation between sensors is increasingly important for the collection of coincident data from multiple sensors either on the same or on different platforms suitable for UAV deployment. Of particular interest is autonomous collaboration between wide area survey detection, high-resolution inspection, and RF sensors that span large segments of the electromagnetic spectrum. The Naval Research Laboratory (NRL) in conjunction with the Space Dynamics Laboratory (SDL) is building sensors with such networked communications capability and is conducting field tests to demonstrate the feasibility of collaborative sensor data collection and exploitation. Example survey / detection sensors include: NuSAR (NRL Unmanned SAR), a UAV compatible synthetic aperture radar system; microHSI, an NRL developed lightweight hyper-spectral imager; RASAR (Real-time Autonomous SAR), a lightweight podded synthetic aperture radar; and N-WAPSS-16 (Nighttime Wide-Area Persistent Surveillance Sensor-16Mpix), a MWIR large array gimbaled system. From these sensors, detected target cues are automatically sent to the NRL/SDL developed EyePod, a high-resolution, narrow FOV EO/IR sensor, for target inspection. In addition to this cooperative data collection, EyePod's real-time, autonomous target tracking capabilities will be demonstrated. Preliminary results and target analysis will be presented.

Optical links, based on coherent homodyne detection and BPSK modulation with bidirectional data transmission of 5.6 Gbps over distances of about 5,000 km and BER of 10^-8, have been sufficiently verified in space. The verification results show that this technology is suitable not only for space applications but also for applications in the troposphere. After a brief description of the Laser Communication Terminal (LCT) for space applications, the paper consequently discusses the future utilization of satellite-based optical data links for Beyond Line of Sight (BLOS) operations of High Altitude Long Endurance (HALE) Unmanned Aerial Vehicles (UAV). It is shown that the use of optical frequencies is the only logical consequence of an ever-increasing demand for bandwidth. In terms of Network Centric Warfare it is highly recommended that Unmanned Aircraft Systems (UAS) of the future should incorporate that technology which allows almost unlimited bandwidth. The advantages of optical communications especially for Intelligence, Surveillance and Reconnaissance (ISR) are underlined. Moreover, the preliminary design concept of an airborne laser communication terminal is described. Since optical bi-directional links have been tested between a LCT in space and a TESAT Optical Ground Station (OGS), preliminary analysis on tracking and BER performance and the impact of atmospheric disturbances on coherent links will be presented.

Muzzle blast trajectories from firings of a 152 mm caliber gun howitzer were obtained with high-speed optical imagers and used to assess the fidelity with which low dimensionality models can be used for data reduction. Characteristic flow regions were defined for the blast waves. The near-field region was estimated to extend to 0.98 - 1.25 meters from the muzzle and the far-field region was estimated to begin at 2.61 - 3.31 meters. Blast wave geometries and radial trajectories were collected in the near through far-fields with visible imagers operating at 1,600 Hz. Beyond the near-field the blast waves exhibited a near-spherical geometry in which the major axis of the blast lay along the axis of the gun barrel and measured within 95% of the minor axis. Several blast wave propagation models were applied to the mid and far-field data to determine their ability to reduce the blast wave trajectories to fewer parameters while retaining the ability to distinguish amongst three munitions configurations. A total of 147 firings were observed and used to assess within-configuration variability relative to separation between configurations. Results show that all models perform well, and drag and point blast model parameters additionally provide insight into phenomenology of the blast.

The certification and testing of new airborne structures is a costly undertaking. This paper presents which measures can be taken to limit the cost and certification required in order to improve the capabilities of the current airborne as-sets, by applying a building block approach to the design and certification of airborne pod structures. A simple way of improving aircraft capabilities is by adding external pod structures, which has been performed for many applications over many years. However, this paper describes a truly modular approach, in which a typical airborne pod structure may be reconfigured to many various roles, with only limited re-certification requirements. Using existing or general aerodynamic shapes, the basic outer shape for the external store is defined, which is then combined with a modular substructure which can accommodate a large variety of electronic and/or optical sensors. This also allows the airborne pod structure to perform several intelligence collecting operations during the same sortie, thereby limiting the time spent near the danger area. The re-use of existing substructure modules reduces the cost and leadtime of the design phase allowing for a rapid entry into service. The modular design, relying on proven interface systems between the building blocks, significantly reduces risk involved in new programs. The certification process is also discussed in order to optimize the use of the pod structure modularity and certification requirements in order to simplify the certification task, by drawing similarity to existing designs. Finally the paper covers how modularity is implemented in new composite pod designs with stealth capabilities.

Recent research efforts at Georgia Tech have focused on the development of a multi-resolution ocean clutter model. This research was driven by the need to support both surveillance and search requirements set by several government customers. These requirements indicated a need to support target detection and tracking for both resolved and unresolved scenarios for targets located either above or on an ocean surface. As a result of this changing sensor resolution characteristic for the various acquisition scenarios, a need for accurate ocean surface models at different geometric resolutions arose. Georgia Tech met this need through development of a multi-resolution approach to modeling both the ocean surface and, subsequently, the ocean signature across the optical spectrum. This approach combined empirical overhead data with high resolution ocean surface models to construct a series of varying resolution ocean clutter models. This paper will describe the approach to utilizing and merging the various clutter models as well as the results of using these models in the target detection and tracking analysis. Remaining issues associated with this clutter model development will be identified and potential solutions discussed.

In many civilian and military applications, early warning IR detection systems have been developed over the years to detect long-range targets in scenarios characterized by highly structured background clutter. In this framework, a well-established detection scheme is realized with two cascaded stages: (i) background clutter removal, (ii) detection over the residual clutter. The performance of the whole detection system is especially determined by the choice and setting of the background estimation algorithm (BEA). In this paper, a novel procedure to automatically select the best performing BEA is proposed which relies on a selection criterion (BEA-SC) where the performances of the detection system are investigated via-simulation for the available BEAs and for different values of their parameters setting. The robustness of the BEA-SC is investigated to examine the performance of the detection system when the characteristics of the targets in the scene sensibly differ from the synthetic ones used in the BEA-SC, i.e. when the BEA is not perfectly tuned to the targets of interest in the scene. We consider target detection schemes that include BEAs based on well-established two-dimensional (2-D) filters. BEA-SC is applied to sequences of IR images acquired on scenarios typical of surveillance applications. Performance comparison is carried out in terms of experimental receiver operating characteristics (EX-ROC). The results show that the recently introduced BEA-SC is robust in the detection of targets whose characteristics are those expected in typical early warning systems.

Critical factors for MTI performance are determined using a Ground Truth Database containing diverse imagery from a range of UAVs operating in-theatre. The Minimum Target Size is measured and identified as the most critical characteristic of the MTI performance envelope. Other factors for MTI performance are discussed. Receiver Operating Characteristic curves are presented to compare MTI performance w.r.t. false alerts. This methodology provides an objective measurement of the performance envelope of MTI systems.

For detecting vehicles in large scale aerial images we first used a non-parametric method proposed recently by Rosin to define the regions of interest, where the vehicles appear with dense edges. The saliency map is a sum of distance transforms (DT) of a set of edges maps, which are obtained by a threshold decomposition of the gradient image with a set of thresholds. A binary mask for highlighting the regions of interest is then obtained by a moment-preserving thresholding of the normalized saliency map. Secondly, the regions of interest were over-segmented by the SLIC superpixels proposed recently by Achanta et al. to cluster pixels into the color constancy sub-regions. In the aerial images of 11.2 cm/pixel resolution, the vehicles in general do not exceed 20 x 40 pixels. We introduced a size constraint to guarantee no superpixels exceed the size of a vehicle. The superpixels were then classified to vehicle or non-vehicle by the Support Vector Machine (SVM), in which the Scale Invariant Feature Transform (SIFT) features and the Linear Binary Pattern (LBP) texture features were used. Both features were extracted at two scales with two size patches. The small patches capture local structures and the larger patches include the neighborhood information. Preliminary results show a significant gain in the detection. The vehicles were detected with a dense concentration of the vehicle-class superpixels. Even dark color cars were successfully detected. A validation process will follow to reduce the presence of isolated false alarms in the background.

Several new techniques are introduced to the component-based vehicle detection in the aerial imagery. The shape-independent tricolour attenuation model based on the spectral power density difference between the regions lighted by direct sunlight and/or diffuse skylight is used to identify cast shadows. The simple linear iterative clustering (SLIC) performs local clustering for superpixels, which were merged by a statistical region merging (SRM) method based on the independent bounded difference inequality theorem. The car body parts were found with Support Vector Machine based on the radiometric and geometric features of the segmented regions. All the algorithms used in this approach require minimum human intervention, providing a robust detection.

Automatic object detection and tracking has been widely applied in the video surveillance systems for homeland security and data fusion in the remote sensing and airborne imagery. The typical applications include human motion analysis and the vehicle detection. Here we implement object detection and tracking under shape graphs of interesting objects integrating local contextual information (corner/point features, etc) of the objects. On the top layer, shapes/sketches provide a discrimination measure to describe the global status of the interesting objects. This kind of information is very useful to improve the object tracking performance for occlusion. The shape can be modeled as a graph or hyper graph through its local geometric features. On the bottom layer, local geometric features are used to capture local properties of objects and perform correspondence estimation of high-level shapes. The local features provide a way to conquer inaccurate object segmentation and extraction. The experiments were implemented on human face tracking and vehicle detection and tracking.

In video tracking systems using image subtraction for motion detection, the global motion is usually estimated to compensate for the camera motion. The accuracy and robustness of the global motion compensation critically affects the performance of the target tracking process. The global motion between video frames can be estimated by matching the features from the image background. However, the features from moving targets contain both camera and target motion and should not be used to calculate the global motion. Sparse optical flow is a classical image matching method. However, the image features selected by optical flow may come from moving targets, with some of the image features matched not being accurate, which leads to poor video tracking performance. Least Median of Square (LMedS) is a popular robust linear regression model and has been applied to real-time video tracking systems implemented in hardware to process up to 7.5 frames/second. In this paper, we use a robust regression method to select features only from the image background for robust global motion estimation, and we develop a real-time (10 frames/second), software-based video tracking system that runs on an ordinary Windows-based general-purpose computer. The software optimization and parameter tuning for real-time execution are discussed in detail. The tracking performance is evaluated with real-world Unmanned Air Vehicle (UAV) video, and we demonstrate the improved global motion estimation in terms of accuracy and robustness.

In real-world outdoor video, moving targets such as vehicles and people may be partially or fully occluded by background objects such as buildings and trees, which makes tracking them continuously a very challenging task. In the present work, we present a system to address the problem of tracking targets through occlusions in a motion-based target detection and tracking framework. For an existing track that is fully occluded, a Kalman filter is applied to predict the target's current position based upon its previous locations. However, the prediction may drift from the target's true trajectory due to accumulated prediction errors, especially when the occlusion is of long duration. To address this problem, tracks that have disappeared are checked with an extra data association procedure that evaluates the potential association between the track and the new detections, which could be a previously tracked target that is just coming out of occlusion. Another issue that arises with motion-based tracking is that the algorithm may consider the visible part of a partially occluded target as the entire target region. This is problematic because an inaccurate target motion trajectory model will be built, causing the Kalman filter to generate inaccurate target position predictions, which can yield a divergence between the track and the true target trajectory. Accordingly, we present a method that provides reasonable estimates of the partially-occluded target centers. Experimental results conducted on real-world unmanned air vehicle (UAV) video sequences demonstrate that the proposed system significantly improves the track continuity in various occlusion scenarios.

Presented is a method to blindly estimate the location of a transmitter from the signal observed by a single moving receiver. This process is based on the observation that the observed Doppler characteristics are essentially uniquely determined by the transmission frequency, the location of the transmitter, and the time-varying flight path of the receiver. We accurately estimate the instantaneous frequency of the received signal and blindly calculate the transmitted frequency from the received signal and the instantaneous position and velocity of the receiver. The transmitter location is then estimated by minimizing a cost function representing the difference between the Doppler characteristics calculated from the relative geometry of the transmitter and receiver and the Doppler characteristics estimated from the received signal. The method has the advantages that only one receiving antenna is required and the emitter may be located with no a priori knowledge of the emitter location or frequency. In addition, the process is essentially independent of the flight path of the receiver.

Object tracking is a direct or indirect key issue in many different military applications like visual surveillance, automatic visual closed-loop control of UAVs (unmanned aerial vehicles) and PTZ-cameras, or in the field of crowd evaluations in order to detect or analyse a riot emergence. Of course, a high robustness is the most important feature of the underlying tracker, but this is hindered significantly the more the tracker needs to have low calculation times. In the UAV application introduced in this paper the tracker has to be extraordinarily quick. In order to optimize the calculation time and the robustness in combination as far as possible, a highly efficient tracking procedure is presented for the above mentioned application fields which relies on well-known color histograms but uses them in a novel manner. This procedure bases on the calculation of a color weighting vector representing the significances of object colors like a kind of an object's color finger print. Several examples from the above mentioned military applications are shown to demonstrate the practical relevance and the performance of the presented tracking approach.

The effect of low sample frame rate on interpretability is often confused with the impact it has on encoding processes. In this study, the confusion was avoided by ensuring that none of the low-frame rate clips had coding artifacts. Under these conditions, the lowered frame rate was not associated with a statistically significant change in interpretability. Airborne, high definition 720P, 60 FPS video clips were used as source material to produce test clips with varying sample frame rates, playback rates, and degrees of target motion. Frame rates ranged from 7.5 FPS to 60 FPS. Playback rates ranged up to 8X normal speed. Target motion ranged from near zero MPH up to 300 MPH.

Modern day remote video cameras enjoy the ability of producing quality video streams at extremely high resolutions. Unfortunately, the benefit of such technology cannot be realized when the channel between the sensor and the operator restricts the bit-rate of incoming data. In order to cram more information into the available bandwidth, video technologies typically employ compression schemes (e.g. H.264/MPEG 4 standard) which exploit spatial and temporal redundancies. We present an alternative method utilizing region of interest (ROI) based compression. Each region in the incoming scene is assigned a score measuring importance to the operator. Scores may be determined based on the manual selection of one or more objects which are then automatically tracked by the system; or alternatively, listeners may be pre-assigned to various areas that trigger high scores upon the occurrence of customizable events. A multi-resolution wavelet expansion is then used to optimally transmit important regions at higher resolutions and frame rates than less interesting peripheral background objects subject to bandwidth constraints. We show that our methodology makes it possible to obtain high compression ratios while ensuring no loss in overall situational awareness. If combined with modules from traditional video codecs, compression ratios of 100:1 to 1000:1, depending on ROI size, can easily be achieved.

The paper presents a new algorithm for efficient compression of sequences of medical images, based on the Inverse Pyramid Decomposition, called Group coding. The same approach is adapted for the efficient archiving of multispectral images as well. The algorithm is based on joint processing of all images in a group, representing the same object, and obtained using sensors of changing light length (multispectral images) or after time intervals. The background of the new approach is the use of the Inverse Pyramid Decomposition, which performs leveled image representation with increasing quality of the approximations obtained in the consecutive decomposition levels. The coarsest approximation of one of the images in the group, selected to be used as a reference one, is used to calculate the next (better) approximations of the remaining images in the group. As a result, is obtained efficient compression of the processed groups of images, which is of high importance for their efficient archiving and storage in image databases. Numerous experiences were performed with satellite and medical images, which proved the method efficiency.

Most non-conventional approaches to image restoration of scenes observed over long atmospheric slant paths require multiple frames of short exposure images taken with low noise focal plane arrays. The individual pixels in these arrays often exhibit spatial non-uniformity in their response. In addition base motion jitter in the observing platform introduces a frame-to-frame linear shift that must be compensated for in order for the multi-frame restoration to be successful. In this paper we describe a maximum a-posteriori parameter estimation approach to the simultaneous estimation of the frame-to-frame shifts and the array non-uniformity. This approach can be incorporated into an iterative algorithm and implemented in real time as the image data is being collected. We present a brief derivation of the algorithm as well as its application to actual image data collected from an airborne platform.

Unmanned aerial vehicles (UAVs) capture real-time video data of military targets while keeping the warfighter at a safe distance. This keeps soldiers out of harm's way while they perform intelligence, surveillance and reconnaissance (ISR) and close-air support troops in contact (CAS-TIC) situations. The military also wants to use UAV video to achieve force multiplication. One method of achieving effective force multiplication involves fielding numerous UAVs with cameras and having multiple videos processed simultaneously by a single operator. However, monitoring multiple video streams is difficult for operators when the videos are of low quality. To address this challenge, we researched several promising video enhancement algorithms that focus on improving video quality. In this paper, we discuss our video enhancement suite and provide examples of video enhancement capabilities, focusing on stabilization, dehazing, and denoising. We provide results that show the effects of our enhancement algorithms on target detection and tracking algorithms. These results indicate that there is potential to assist the operator in identifying and tracking relevant targets with aided target recognition even on difficult video, increasing the force multiplier effect of UAVs. This work also forms the basis for human factors research into the effects of enhancement algorithms on ISR missions.

Airborne surveillance and reconnaissance are essential for successful military missions. Such capabilities are critical for troop protection, situational awareness, mission planning, damage assessment, and others. Unmanned Aerial Vehicles (UAVs) gather huge amounts of video data but it is extremely labour-intensive for operators to analyze hours and hours of received data. At MDA, we have developed a suite of tools that can process the UAV video data automatically, including mosaicking, change detection and 3D reconstruction, which have been integrated within a standard GIS framework. In addition, the mosaicking and 3D reconstruction tools have also been integrated in a Service Oriented Architecture (SOA) framework. The Visualization and Exploitation Workstation (VIEW) integrates 2D and 3D visualization, processing, and analysis capabilities developed for UAV video exploitation. Visualization capabilities are supported through a thick-client Graphical User Interface (GUI), which allows visualization of 2D imagery, video, and 3D models. The GUI interacts with the VIEW server, which provides video mosaicking and 3D reconstruction exploitation services through the SOA framework. The SOA framework allows multiple users to perform video exploitation by running a GUI client on the operator's computer and invoking the video exploitation functionalities residing on the server. This allows the exploitation services to be upgraded easily and allows the intensive video processing to run on powerful workstations. MDA provides UAV services to the Canadian and Australian forces in Afghanistan with the Heron, a Medium Altitude Long Endurance (MALE) UAV system. On-going flight operations service provides important intelligence, surveillance, and reconnaissance information to commanders and front-line soldiers.

Predicting ground vehicle performance requires in-depth knowledge, captured as numeric parameters, of the terrain on which the vehicles will be operating. For off-road performance, predictions are based on rough terrain ride comfort, which is described using a parameter entitled root-mean-square (RMS) surface roughness. Likewise, on-road vehicle performance depends heavily on the slopes of the individual road segments. Traditional methods of computing RMS and road slope values call for high-resolution (inch-scale) surface elevation data. At this scale, surface elevation data is both difficult and time consuming to collect. Nevertheless, a current need exists to attribute large geographic areas with RMS and road slope values in order to better support vehicle mobility predictions, and high-resolution surface data is neither available nor collectible for many of these regions. On the other hand, meter scale data can be quickly and easily collected for these areas using unmanned aerial vehicle (UAV) based IFSAR and LIDAR sensors. A statistical technique for inferring RMS values for large areas using a combination of fractal dimension and spectral analysis of five-meter elevation data is presented. Validation of the RMS prediction technique was based on 43 vehicle ride courses with 30-centimeter surface elevation data. Also presented is a model for classifying road slopes for long road sections using five-meter elevation data. The road slope model was validated against one-meter LIDAR surface elevation profiles. These inference algorithms have been successfully implemented for regions of northern Afghanistan, and some initial results are presented.

Separate tracking of objects such as people and the luggages they carry is important for video surveillance applications as it would allow making higher level inferences and timely detection of potential threats. However, this is a challenging problem and in the literature, people and objects they carry are tracked as a single object. In this study, we propose using thermal imagery in addition to the visible band imagery for tracking in indoor applications (such as airports, metro or railway stations). We use adaptive background modeling in association with mean-shift tracking for fully automatic tracking. Trackers are refreshed using the background model to handle occlusion and split and to detect newly emerging objects as well as objects that leave the scene. Visible and thermal domain tracking information are fused to allow tracking of people and the objects they carry separately using their heat signatures. By using the trajectories of these objects, interactions between them could be deduced and potential threats such as abandoning of an object by a person could be detected in real-time. Better tracking performance is also achieved compared to using a single modality as thermal reflection and halo effect which adversely affect tracking are eliminated by the complementing visible band data. The proposed method has been tested on videos containing various scenarios. The experimental results show that the presented method is effective for separate tracking of objects such as people and their belongings and for detecting the interactions in the presence of occlusions.

There is increasing interest in imaging spectrometers working in the SWIR and LWIR wavelength bands. Commercially available detectors are not only expensive, but have a limited number of pixels, compared with visible band detectors. Typical push broom hyperspectral imaging systems consist of a fore optic imager, a slit, a line spectrometer, and a two dimensional focal plane with a spatial and spectral direction. To improve the spatial field coverage at a particular resolution, multiple systems are incorporated, where the "linear fields of view" of the systems are aligned end to end. This solution is prohibitive for many applications due to the costs of the multiple detectors, coolers, spectrometers, or the space, weight, or power constraints. Corning will present a cost effective solution utilizing existing detectors combined with innovative design and manufacturing techniques.

Recent advances in digital photography have enabled the development and demonstration of plenoptic cameras with impressive capabilities. They function by recording sub-aperture images that can be combined to re-focus images or to generate stereoscopic pairs. Plenoptic methods are being explored for fusing images from distributed arrays of cameras, with a view toward applications in which hardware resources are limited (e.g. size, weight, power constraints). Through computer simulation and experimental studies, the influences of non-idealities such as camera position uncertainty are being considered. Component image rescaling and balancing methods are being explored to compensate. Of interest is the impact on precision passive ranging and super-resolution. In a preliminary experiment, a set of images from a camera array was recorded and merged to form a 3D representation of a scene. Conventional plenoptic refocusing was demonstrated and techniques were explored for balancing the images. Nonlinear methods were explored for combining the images limited the ghosting caused by sub-sampling. Plenoptic processing was explored as a means for determining 3D information from airborne video. Successive frames were processed as camera array elements to extract the heights of structures. Practical means were considered for rendering the 3D information in color.