The Airborne Littoral Reconnaissance Technology (ALRT) program has successfully demonstrated the Wide-Field Airborne Laser Diode Array Illuminator (ALDAI-W). This illuminator is designed to illuminate a large area from the air with limited power, weight, and volume. A detection system, of which the ALDAI-W is a central portion, is capable of detecting surface-laid minefields in absolute darkness, extending the allowed mission times to night operations. This will be an overview report, giving processing results and suggested paths for additional development.
The Airborne Littoral Reconnaissance Technologies (ALRT) project has developed and successfully demonstrated a nighttime operational minefield detection capability using commercial off-the-shelf high-power Laser Diode Arrays (LDAs). The Coastal System Station's ALRT project, under funding from the Office of Naval Research (ONR), has been designing, developing, integrating, and testing commercial arrays using a Cessna airborne platform over the last several years. This has led to the development of three test bed variants, as reported on last year: the Airborne Laser Diode Array Illuminator prototype (ALDAI-P), the original commercial array version (ALDAI-C), and the most recent wide field-of-view commercial version (ALDAI-W). Using the ALDAI-W variant because of its increased operational capabilities with higher altitudes and wider field of views, ALRT recently demonstrated nighttime operation by detecting minefields over several background variations, expanding Naval reconnaissance capabilities that had been previously limited to daytime operation. This paper describes the demonstration and shows results of the ALDAI-W test.
The Airborne Littoral Reconnaissance Technologies (ALRT) project has developed and tested a nighttime operational minefield detection capability using commercial off-the-shelf high-power Laser Diode Arrays (LDAs). The Coastal System Station’s ALRT project, under funding from the Office of Naval Research (ONR), has been designing, developing, integrating, and testing commercial arrays using a Cessna airborne platform over the last several years. This has led to the development of the Airborne Laser Diode Array Illuminator wide field-of-view (ALDAI-W) imaging test bed system. The ALRT project tested ALDAI-W at the Army’s Night Vision Lab’s Airborne Mine Detection Arid Test. By participating in Night Vision’s test, ALRT was able to collect initial prototype nighttime operational data using ALDAI-W, showing impressive results and pioneering the way for final test bed demonstration conducted in September 2003. This paper describes the ALDAI-W Arid Test and results, along with processing steps used to generate imagery.
The Streak Tube Imaging Lidar (STIL) is a three-dimensional electro-optic sensor that provides photographic quality images and was developed to identify objects of interest on the ocean bottom. STIL sends out a pulsed fan of light through the water column with the photon time of flight returns measured, giving range information that allows water backscatter to be separated from the bottom return. Although three-dimensional, the current choice of operation to display STIL data is to render it into two-dimensional contrast and range maps (images). This paper describes a fully automated process (single executable) that renders the three-dimensional STIL data cube into contrast and range maps, removes inherent system noise, and enhances low contrast regions for optimal display to the operator.
KEYWORDS: 3D image processing, Sensors, 3D modeling, 3D acquisition, 3D displays, Data modeling, Imaging systems, Reflectivity, Automatic target recognition, Electro optics
Electro-optic identification sensors provide photographic quality images and were developed to identify objects of interest on the ocean bottom. Two of these high-resolution sensors are currently in use: one based on Streak Tube Imaging Lidar (STIL) technology and the other based on Laser Line Scan (LLS) technology. Both of these sensors produce high fidelity imagery that is unparalleled in quality for underwater imaging systems. They differ in that LLS sensors produce two-dimensional (2-D) contrast images only while STIL produces three-dimensional (3-D) data that can be rendered into 2-D contrast and range maps (images). Although still an emerging technology, recent advances have begun to point to significant advantages with the supplementary range information (3-D information) in identifying objects of interest on the sea floor. This paper discusses some of these advantages of range information for 3-D visual display, computer aided identification and target recognition, modeling, and the general identification process.
The development of an underwater target identification algorithm capable of identifying various types of underwater targets, such as mines, under different environmental conditions pose many technical problems. Some of the contributing factors are: targets have diverse sizes, shapes and reflectivity properties. Target emplacement environment is variable; targets may be proud or partially buried. Environmental properties vary significantly from one location to another. Bottom features such as sand, rocks, corals, and vegetation can conceal a target whether it is partially buried or proud. Competing clutter with responses that closely resemble those of the targets may lead to false positives. All the problems mentioned above contribute to overly difficult and challenging conditions that could lead to unreliable algorithm performance with existing methods. In this paper, we developed and tested a shape-dependent feature extraction scheme that provides features invariant to rotation, size scaling and translation; properties that are extremely useful for any target classification problem. The developed schemes were tested on an electro-optical imagery data set collected under different environmental conditions with variable background, range and target types. The electro-optic data set was collected using a Laser Line Scan (LLS) sensor by the Coastal Systems Station (CSS), located in Panama City, Florida. The performance of the developed scheme and its robustness to distortion, rotation, scaling and translation was also studied.
A background equalization algorithm was developed in 1996 to enhance fluctuating high/low signal strength regions found in laser line scan data. This algorithm was very effective in enhancing low contrast objects obscured in low signal strength regions, and was developed to enhance data for quick and easy inspection of objects within the imagery. The background equalization algorithm is based on a least squares error estimate of the image background, including object pixels. The adaptive background equalization algorithm has modified the background equalization algorithm to exclude object pixels for a more accurate estimate of the image background. This is accomplished by integrating a background mask into the background equalization routine that separates object pixels from background pixels. In addition to using the background mask for improved image enhancement, the background mask can also be used for object detection of discernible objects that stick out from the image background. The adaptive background equalization algorithm and its applications are discussed in this paper.
Laser line scan imagery has been found to have fluctuating brightness regions due to high/low signal strengths when scanning data. The low signal strength regions can obscure visibility of potential mines, sometimes to the point of preventing identification by an operator inspecting the data. Two contrast enhancement routines have been developed to enhance obscured objects in the low signal strength regions: a background equalization routine that equalizes the high/low signal strength regions and a local histogram clipping routine that applies a moving window histogram clip to enhance details in the low strength regions without effecting the high strength regions.
During the summer of 1996 a series of field trials were conducted in the Florida Keys and Bahama Islands to
evaluate the ability of a unique laser line scan system to measure and map the fluorescent characteristics of
coral reef environments. Typical fluorescence maps that were obtained are presented and compared with
monochrome and RGB color images of the same reefs. The monochrome images were obtained with the laser
line scan system simuftaneously with the fluorescent maps. The RGB images, which were also obtained with the
laser line scan system, were recorded in the same location on a subsequent thai.
This paper illustrates various issues involved in the use of imagery in mine warfare. Particular emphasis is given to environmental factors which can create false alarms.
The surf zone environment represents a very difficult challenge for electro-optic surveillance programs. Data from these programs have been shown to contain dense clutter from vegetation, biological factors (fish), and man-made objects, and is further complicated by the water to land transition which has a significant impact on target signal-to-noise ratios (SNR). Also, targets can be geometrically warped from the sea surface and by occlusion from sand and breaking waves. The Program Executive Office Mine Warfare (PMO-210) recently sponsored a test under the Magic Lantern Adaptation (MLA) program to collect surf zone data. Analysis of the data revealed a dilemma for automatic target recognition algorithms; threshold target features high enough to reduce high false alarm rates from land clutter or low enough to detect and classify underwater targets. Land image typically have high SNR clutter with crisp edges while underwater images have lower SNR clutter with blurred edges. In an attempt to help distinguish between land and underwater images, target feature thresholds were made to vary as a function of the SNR of image features within images and as a function of a measure of the edge crispness of the image features. The feasibility of varying target feature thresholds to reduce false alarm rates was demonstrated on a target recognition program using a small set of MLA data. Four features were developed based on expected target shape and resolution: a contrast difference measure between circular targets and their local backgrounds, a signal-to-noise ratio, a normalized correlation, and a target circularity measure. Results showed a target probability of detection and classification (Pdc) of 50 - 78% with false alarms per frame of less than 4%.
A major obstacle to target detection by airborne electro-optic systems in the shallow water environment is distortion due to wave action from the sea surface air-to-water interface. This problem is being studied by the Program Executive Office Mine Warfare (PMO-210) through an ongoing project called Magic Lantern Adaptation. Under funding from this program, a pier test has been planned to collect data to help understand and model the warping effects of the air-to-water interface. To measure the amount of distortion occurring from wave action, a figure of merit was developed by a fuzzy 'ANDing' of 1) a scale and bias invariant normalized correlation, 2) the ratio of signal power to local noise power, and 3) the ratio of signal strength to overall image background variance. Results show that the figure of merit can successfully evaluate the 'goodness' of target distortion. On a scale from 0 to 100, with 100 indicating no distortion, 'good' targets were measured in the 70s, 'fair' targets in the 50s, and 'poor' targets in the 20s and 30s. The choice of scale and bias invariant correlation was derived after evaluating the problems encountered with standard and scale invariant correlation techniques. This paper contains a discussion of this evaluation.
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