The utility of acoustic-to-seismic coupling systems for landmine detection has been clearly established. In this approach, laser Doppler vibrometers (LDV) are used to measure the different responses to acoustic excitation in ground regions with and without buried landmines. Currently, for most applications, only the magnitude of the surface velocity is investigated and used to construct recognition algorithms. Recently, we introduced phase-based features in the classification scheme, significantly lowering false alarm rates at given detection probabilities. In this paper, we present
modeling equations that explain the phase features for ground areas both with and without buried landmines from the perspective of harmonic oscillator models. We also describe the image processing techniques applied to velocity data collected in the time domain with a moving LDV array. The observed signatures are also compared with the prediction of the models described. We also construct classifiers with only magnitude information and both magnitude and phase information for this time-domain data set. Classification results indicate that we can combine magnitude and phase features to improve the detection of buried mines while reducing false alarms. We also find that using phase information improves the distinction between ground regions with buried landmines or man-made clutter objects.
Proc. SPIE. 5089, Detection and Remediation Technologies for Mines and Minelike Targets VIII
KEYWORDS: Short wave infrared radiation, Optical filters, Digital image processing, Detection and tracking algorithms, Cameras, Image processing, Diffusion, Medical imaging, Digital imaging, Image enhancement
Autonomous detection of tripwires using optical systems is of great interest. This paper describes methods for detection of tripwires using an image processing algorithm based on the diffusion equation. A video camera with sensitivity in the near infrared (IR) band records the target scene and the digital images are then transported to a computer to apply an image processing algorithm to determine if a tripwire is present. In this paper, we show that coherence enhancing diffusion filtering can recover broken edges and smooth background without smoothing coherent structures. A comparison of detection results is given with and without diffusion filtering.
A variety of sensors have been investigated for the purpose of detecting buried landmines in outdoor environments. Mines with little or no metal are very difficult to detect with traditional mine detection systems. Ground Penetrating Radar (GPR) sensors have shown great promise in detecting low metal mines and can easily detect metal mines. Unfortunately, it can still be difficult to detect low-metal mines with GPR due to very low contrast between the mine and the surrounding medium. Acoustic-seismic systems were proposed by Sabatier et.al. and have also shown great promise in detecting low metal mines. There are now a wealth of references that discuss these systems and algorithms for processing data from these systems. Therefore, they will not be discussed in detail here. In fact, low-metal mines are easier to detect than metal mines with this acoustic-seismic systems. Low metal mines that are difficult for a GPR to detect can be quite easy to detect with acoustic-seismic systems. Sensor fusion with these sensors is of interest since together they can find a broader range of mines with relative ease. The algorithmic challenge is to determine a strategy for combining the multi-sensor information in a way that can increase the probability of detection without increasing the false alarm rate significantly. In this paper, we investigate fusion of information obtained from GPR and acoustic-seismic on real data measured from a mine lane containing three types of buried landmines and also areas containing no landmines. Algorithms are applied to data acquired from each sensor and confidence values are assigned to each location at which a measurement is made by each sensor. The GPR is used as a primary sensor. At each location at which the GPR algorithm declares an alarm, a modified likelihood-based approach is used to increase the GPR derived confidence if the likelihood that a mine is present, defined by the acoustic-based confidence, is larger than the likelihood that no mine is present. If the acoustic-derived confidence is very high, then a declaration is made even if there is no GPR declaration. The experiments were conducted using data acquired from the sensors at different times. The acoustic-seismic system collected data over a subset of the region at which the GPR collected data. Results are given only over those regions for which both sensors collected data.
Acoustic-Seismic methods for landmine detection are under intensive investigation. Data collected by the University of Mississippi have by processed by a variety of investigators with excellent results in many cases. Increases in performance are sought based on an understanding of the physical principles leading to the differences between the vibrational velocities of soil over buried landmines and over locations not above landmines. Donskoy suggested modeling the physical system using damped harmonic oscillators. This model suggests a use of magnitude and phase information in image processing algorithms for detecting. In this paper, some methods for incorporating magnitude and phase into image processing algorithms are described and demonstrated. Previous algorithms relied on magnitude only. Increased performance is achieved by incorporating phase into the algorithms.
Methods for processing continuously acquired data using an Acoustic/Seismic system are described. Data were acquired from 80-300 Hz. Two independent chains of processing were pursued. In one chain, pre-processing and normalization were followed by shape feature extraction using eccentricity and minor axis length. In another chain, Independent Component Analysis was used to generate image templates. The results were combined using piecewise linear discriminants. Probabilities of detection of 97.5% and 100% with false alarm rates of 0.01 and 0.03 were achieved on training and validation sets, respectively.
Signatures of buried landmines are often difficult to separate from those of clutter objects. Often, shape information is not directly obtainable from the sensors used for landmine detection. The Acoustic Sensing Technology (AST), which uses a Laser Doppler Vibrometer (LDV) that measures the spatial pattern of particle velocity amplitude of the ground surface in a variety of frequency bands, offers a unique look at subsurface phenomena. It directly records shape related information. Generally, after preprocessing the frequency band images in a downward looking LDV system, landmines have fairly regular shapes (roughly circular) over a range of frequencies while clutter tends to exhibit irregular shapes different from those of landmines. Therefore, shape description has the potential to be used in discriminating mines from clutter. Normalized Fourier Descriptors (NFD) are shape parameters independent of size, angular orientation, position, and contour starting conditions. In this paper, the stack of 2D frequency images from the LDV system are preprocessed by a linear combination of order statistics (LOS) filter, thresholding, and 2D and 3D connected labeling. Contours are extracted form the connected components and aggregated to produce evenly spaced boundary points. Two types of Normalized Fourier Descriptors are computed from the outlines. Using images obtained from a standard data collection site, these features are analyzed for their ability to discriminate landmines from background and clutter such as wood and stones. From a standard feature selection procedure, it was found that a very small number of features are required to effectively separate landmines from background and clutter using simple pattern recognition algorithms. Details of the experiments are included.
Previous results with Hidden Markov models showed that they could be used to perform reliable classification between mines and background/clutter under a variety of conditions. Since the, new features have been defined and continuous models have been implemented. In this paper, new results are presented for applying them to calibration lane GPR data obtained during the vehicle mounted mine detection (VMMD) Advanced Technology Demonstrations. Morphological Neural Networks can be trained to perform feature extraction and detection simultaneously. Generalizing these networks to incorporate Choquet Integrals provides the added capability of robustness and improved feature learning. These features can provide complementary information compared to those generate by humans. Result of applying these networks to calibration lane GPR data from the VMMD Advanced Technology Demonstrations are provided. Combinations of the various methodologies with previously developed algorithms are also evaluated.
Linear Combination of Order Statistics (LOS) filters are a special case of the Choquet integral filters. LOS are a class of nonlinear filters parameterized by a set of n weights. Different values of the weights lead to different filters. Examples include the median and other order statistic filters, local averaging filters, and trimmed average filters. Differences of LOS filters have been used in the past as target detection filters by nonlinearly comparing a small, targets size region with the surrounding region. The delta operator, proposed by Gelenbe et. al. for land mine detection, can be represented as a special case of a difference of LOS operators. Weights of LOS operators can be determined by solving an optimization problem, represented as a quadratic program. In this paper, experiments are conducted in determining optimal differences of LOS operators using the DARPA backgrounds data. The results are that the delta-operator is the solution of the optimization problem for this data set.
Gray-scale morphological operators are commonly employed in image processing applications, such as texture analysis, target detection and image enhancement. One major problem with these operators is their sensitivity to noise. Another issue is finding the right structuring elements for a process. This paper describes Choquet integral-based morphological operators. These operators do not necessarily use max and min and therefore they are less sensitive to noise. In this paper, we also introduce a technique to find an optimal gray scale structuring element. These developments yield applications, including but not limited to target detection and multi-layer filtering.
The paper describes the application of Choquet integral filters to automatic object detection in laser radar (LADAR) imagery. Choquet integrals are nonlinear integrals with respect to non-additive measures. These integrals can be used to represent typical nonlinear filters such as order statistic filters, linear combination of order statistic filters, weighted median filters and others. A Choquet integral filter is characterized by a measure. The representation of these filters as integrals with respect to measures provides an opportunity for optimizing the filters by finding optimal measures. Both optimal and heuristic filters are designed and compared on real data.