KEYWORDS: 3D modeling, Synthetic aperture radar, Scattering, Data modeling, 3D image processing, Systems modeling, Radar, 3D image reconstruction, Deconvolution, Point spread functions
Sparse data collection geometries represent a significant challenge to high-resolution three-dimensional SAR
imaging. In particular, sparse sampling can lead to significant sidelobe structure in high-resolution reconstructions.
To help constrain volumetric SAR reconstructions, we have introduced a surface model. We justify this
based on physical phenomenology: higher frequency SAR systems exhibit only limited surface penetration. In
our method, we jointly estimate the surface models and reconstructions, significantly reducing sidelobing artifacts
in comparison with traditional reconstructions. Our paper and presentation illustrate reconstructions both
with and without surface models to demonstrate the potential improvement.
KEYWORDS: Sensors, Land mines, General packet radio service, Electromagnetic coupling, Sensor fusion, Mining, Monte Carlo methods, Statistical modeling, Soil science, Statistical analysis
In this paper, we develop a system to exploit sensor fusion for detecting and locating plastic A/P mines. We design and test the system using data from Monte Carlo electromagnetic induction spectroscopy (EMIS) and ground penetrating radar (GPR) simulations. We include the effects of both random soil surface variability and sensor noise. In the presence of a rough surface and a heterogeneous, multi-element clutter environment, we obtain good results fusing EMIS and GPR data using a statistical approach. More generally, we demonstrate a framework for simulating and testing sensor configurations and sensor fusion approaches for landmine and unexploded ordinance (UXO) detection systems. Taking advantage of high- fidelity electromagnetic simulation, we develop a controlled environment for testing sensor fusion concepts, from varied sensor arrangements to detection algorithms. In this environment, we can examine the effect of changing mine structure, soil parameters, and sensor geometry on the sensor fusion problem. We can then generalize these results to produce mine detectors robust to real-world variations.
DARPA's Moving and Stationary Target Acquisition and Recognition (MSTAR) program has shown that image segmentation of Synthetic Aperture Radar (SAR) imagery into target, shadow, and background clutter regions is a powerful tool in the process of recognizing targets in open terrain. Unfortunately, SAR imagery is extremely speckled. Impulsive noise can make traditional, purely intensity-based segmentation techniques fail. Introducing prior information about the segmentation image -- its expected 'smoothness' or anisotropy -- in a statistically rational way can improve segmentations dramatically. Moreover, maintaining statistical rigor throughout the recognition process can suggest rational sensor fusion methods. To this end, we introduce two Bayesian approaches to image segmentation of MSTAR target chips based on a statistical observation model and Markov Random Field (MRF) prior models. We compare the results of these segmentation methods to those from the MSTAR program. The technique we find by mapping the discrete Bayesian segmentation problem to a continuous optimization framework can compete easily with the MSTAR approach in speed, segmentation quality, and statistical optimality. We also find this approach provides more information than a simple discrete segmentation, supplying probability measures useful for error estimation.
Target recognition research for Synthetic Aperture Radar (SAR) has been made easier with the introduction of target chip sets. The target chips typically are of good quality and consist of three regions: target, shadow and background clutter. Target chip sets allow recognition researchers to bypass the quality filtering and detection phases of the automatic recognition process. So, the researcher can focus on segmentation and matching techniques. A manual segmentation process using supervised quality control is introduced in this paper. Using 'goodness of fit' measures the quality of manual segmentation on SAR target chips is presented. Using the expected metrics associated with the manual segmentation process, the performance of automated segmentation techniques can be evaluated. The approach of using manual segmentation to evaluate the performance of automated segmentation techniques is presented by demonstrating the results on a simple automated segmentation technique that incorporates speckle removal and segmentation.
KEYWORDS: Sensors, Land mines, General packet radio service, Electromagnetic coupling, Mining, Sensor fusion, Signal detection, Monte Carlo methods, Statistical modeling, Statistical analysis
In this paper, we develop a statistical detection system exploiting sensor fusion for the detection of plastic A/P miens. We design and test the system using data from Monte Carlo electromagnetic induction (EMI) and ground penetrating radar (GPR) simulations. We include the effects of both random soil surface variability and sensor noise. In spite of the presence of a rough surface, we can obtain good result fusing EMI and GPR data using a statistical approach in a simple clutter environment. More generally, we develop a framework for simulation and testing of sensor configurations and sensor fusion approaches for landmine and unexploded ordinance detection systems. Exploiting accurate electromagnetic simulation, we develop a controlled environment for testing sensor fusion concepts, from varied sensor arrangements to detection algorithms, In this environment, we can examine the effect of changing mine structure, soil parameters, and sensor geometry on the sensor fusion problem. We can then generalize these results to produce mine detectors robust to real-world variations.
We present an analysis of statistical model based data-level fusion for near-IR polarimetric and thermal data, particularly for the detection of mines and mine-like targets. Typical detection-level data fusion methods, approaches that fuse detections from individual sensors rather than fusing at the level of the raw data, do not account rationally for the relative reliability of different sensors, nor the redundancy often inherent in multiple sensors. Representative examples of such detection-level techniques include logical AND/OR operations on detections from individual sensors and majority vote methods. In this work, we exploit a statistical data model for the detection of mines and mine-like targets to compare and fuse multiple sensor channels. Our purpose is to quantify the amount of knowledge that each polarimetric or thermal channel supplies to the detection process. With this information, we can make reasonable decisions about the usefulness of each channel. We can use this information to improve the detection process, or we can use it to reduce the number of required channels.
We present a statistically-based method for the enhancement and detection of mines and mine-like targets, in multi-channel imagery. Standard approaches to such multi-channel image processing take advantage of the correlation across channels within a pixel, but typically do not exploit the spatial dependency between pixels. This work aims to construct appropriate spatial statistical models for multi-channel mine imagery and apply these models to allow both image enhancement as well as direct and improved detection of anomalies (i.e., targets) in such data. We base the method on a Markov Random Field (MRF) model that incorporates a priori information about both the target's and the background's spatial characteristics. In particular, we find a Maximum A Posterior (MAP) detector of mine targets in background under the prior assumption target pixels are locally spatially dependent. We implement our algorithm on polarimetric and thermal data obtained from the Remote Minefield Detection System (REMIDS), with favorable results compared to a Maximum Likelihood (ML) detector that performs detections on a pixel-by-pixel basis, i.e. without spatial correlation.
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