Commercial Lidar often focus on reporting the range associated with the strongest laser return pulse, first return pulse, or last return pulse. This technique works well when observing discrete objects separated by a distance greater than the laser pulse length. However, multiple reflections due to more closely layered objects produce overlapping laser return pulses. Resolving the multi-layered object ranges in the resulting complex waveforms is the subject of this paper. A laboratory setup designed to investigate the laser return pulse produced by multi-layered objects is described along with a comparison of a simulated laser return pulse and the corresponding digitized laser return pulse. Variations in the laboratory setup are used to assess different strategies for resolving multi-layered object ranges and how this additional information can be applied to detecting objects partially obscured in vegetation.
Commercial sensor technology has the potential to bring cost-effective sensors to a number of U.S. Army applications.
By using sensors built for a widespread of commercial application, such as the automotive market, the Army can
decrease costs of future systems while increasing overall capabilities. Additional sensors operating in alternate and
orthogonal modalities can also be leveraged to gain a broader spectrum measurement of the environment. Leveraging
multiple phenomenologies can reduce false alarms and make detection algorithms more robust to varied concealment
materials. In this paper, this approach is applied to the detection of roadside hazards partially concealed by light-to-medium
vegetation. This paper will present advances in detection algorithms using a ground vehicle-based commercial
LADAR system. The benefits of augmenting a LADAR with millimeter-wave automotive radar and results from
relevant data sets are also discussed.
The US Army's RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD), Countermine
Division is evaluating the compressibility of airborne
multi-spectral imagery for mine and minefield detection
application. Of particular interest is to assess the highest image data compression rate that can be afforded without the
loss of image quality for war fighters in the loop and performance of near real time mine detection algorithm. The
JPEG-2000 compression standard is used to perform data compression. Both lossless and lossy compressions are
considered. A multi-spectral anomaly detector such as RX (Reed & Xiaoli), which is widely used as a core
algorithm baseline in airborne mine and minefield detection on different mine types, minefields, and terrains to identify
potential individual targets, is used to compare the mine detection performance. This paper presents the compression
scheme and compares detection performance results between compressed and uncompressed imagery for various level
of compressions. The compression efficiency is evaluated and its dependence upon different backgrounds and other
factors are documented and presented using multi-spectral data.
This paper presents the development of a simulation tool to facilitate the exploration and evaluation of design tradeoffs for an Unmanned Aerial Vehicle (UAV) based minefield detection system. Mine and minefield performance estimates and design tradeoffs are obtained using explicit evaluation of detection statistics simulated under different sensors, minefield layout scenarios, and mission specific constraints. The simulated mine and minefield level performance results are compared with analytical results where available. Design tradeoffs are studied in terms of different sensor and mission profile parameters such as signal to clutter ratio, target size, field-of-regard, and detection algorithms. The analytical relationship and simulated results of mine and minefield detection performance based on these parameters are presented. Different metrics for evaluating minefield performance and their influences on design tradeoffs are discussed, and suggestions are made.
Laser illumination techniques continue to be investigated to support airborne detection of minefields. Polarization characteristics of manmade objects and natural backgrounds are potential discriminants for detection of mines and minefields or supplementing other detection features. NVESD sponsored development of a dual-band system prototype to investigate airborne minefield detection and determine the feasibility of packaging a system for use aboard a tactical UAV in day and night operations. The prototype contains both an infrared sensor and a laser subsystem. The laser subsystem is an active imaging near IR (NIR) assembly consisting of a laser illuminator and a receiver. The illuminator integrates multiple stacked laser diode arrays with a laser controller capable of generating short, high-energy pulses of selectable amplitude to control optical power output. The receiver includes a linear polarization analyzer and a range-gated, image intensified CCD camera. This paper presents a technical description of the active NIR subsystem, including laboratory and field testing, system modeling, and preliminary results of analysis and signal processing efforts. We describe measured reflectivity and polarization characteristics of various object classes, including mines, backgrounds, and clutter. We indicate the observed class separability due to the polarization characteristics of the object classes.
This paper establishes the class separability of mines in various backgrounds for the low sun angle using Dec. 2000 and June 2001 airborne 808nm laser imagery. Specifically, this paper provides the polarization distribution of mines and background types based on four statistics: in-plane (P); cross-plane (S); P-S, and degree of polarization (DoP = (P-S)/(P+S)). This study provides a first look at which polarization can benefit the performance for airborne minefield detection and under which background conditions for a particular time of the year (i.e. low sun angle scenarios). This study presenting the polarization class distribution provides a good basis for the algorithm development effort for an automatic mine/minefield detection system using 808nm laser imagery. This study used two subsets from the December 2000 and June 2001 airborne data collections collected with the Sci-Tech breadboard 808nm laser. To accurately represent the distribution of the mines and background, there are 24,000 mine and 144,000 background pixels were manually to ensure the perfect registration between pixels located in P and S images for the same mine or background.
This paper quantifies the overall detection performance for landmines in various background and solar conditions in an attempt to provide the performance bounds for airborne mine detection systems. Specifically, for comparison purposes, this paper quantifies the detection performance based on the RX detection algorithm implemented as the baseline LAMD approach, RX implementation as a correlation operator, and intensity thresholding approach using airborne laser imagery. The generated receiver operating characteristic (ROC) curves, in turn, provide a good basis for system trade-off study in terms of computational time and complexity, and performance benefit for real-time systems. This paper includes the ROC curves with and without man-made objects to access the effect of the man-made objects based on these algorithms. The paper uses two subsets from the December 2000 and June 2001 airborne data collections using the SciTech Breadboard 808nm laser at a U.S. Army test site. The total mine opportunities and the area coverage are 1619 and 146,000 m2, respectively. The total number of man-made objects are 800 (approximately 137 images of which each image contains approximately 8 man-made objects). The man-made object list contains mine sized aluminum plates and wood, coke cans and others. Mine list contains M20, M19, TM62M, TM62P2, TM62P3, RAAM, VS1.b.
This is a follow-up work to analyze completely the detectability of the buried mines for the spectral regions extending from Visible/Near IR (VNIR) to Longwave IR (LWIR). Similar to previous work focusing on the VNIR region (1) this paper presents the quantitative detectability of the buried mines in the 3-5)mum and 8-12)mum regions. Specifically, this paper presents a statistical analysis for the buried mines in specified spectral regions for various soils and burial durations. As shown in the previous work (1) the performance based on the single hypothesis test using the distance measure was better than the intensity thresholding method. This paper focuses on only the distance measure method for statistical analysis of the data, and subsequently, classification to quantify the detectability of the buried mines in the 3 to 5 and 8 to 12 micron regions.
This paper present the quantitative detection performance of buried mine reflectance signature sin various soils and burial durations. The spectral signatures including the distribution representing class separation between mines and background is performed. The quantified detection performance is based on single hypothesis test using the distance measure and the thresholding method. This paper uses a subset of the data collected under the Night Vision and Electronic Sensors Directorate sponsored ERIM Hyperspectral Mine Detection Phenomenology data collections. The dat set used was collected with an Analytical Spectral Devices Field Spectrometer at Ft. Carson, and contains about 700 mine, and 600 background signatures with hundreds of bands extending from .35 to 2.5 micrometers .
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.