The application of ground-penetrating radar (GPR) in humanitarian demining labors presents two major challenges: (1) the development of affordable and practical systems to detect metallic and non-metallic antipersonnel (AP) landmines under different conditions, and (2) the development of accurate soil characterization techniques
to evaluate soil properties effects and determine the performance of these GPR-based systems. In this paper, we present a new integrated approach for characterizing electromagnetic (EM) properties of mine-affected soils and detecting landmines using a low cost hand-held vector network analyzer (VNA) connected to a highly directive
antenna. Soil characterization is carried out using the radar-antenna-subsurface model of Lambot et al.1 and full-wave inversion of the radar signal focused in the time domain on the surface reflection. This methodology is integrated to background subtraction (BS) and migration to enhance landmine detection. Numerical and laboratory experiments are performed to show the effect of the soil EM properties on the detectability of the
landmines and how the proposed approach can ameliorate the GPR performance.
Humanitarian demining is a very dangerous, cost and time intensive work, where a lot of effort is usually wasted in inspecting suspected areas that turn out to be mine-free. The main goal of the project SMART (Space and airborne Mined Area Reduction Tools) is to apply a multisensor approach towards corresponding signature data collection, developing adapted data understanding and data processing tools for improving the efficiency and reliability of level 1 minefield surveys by reducing suspected mined areas. As a result, the time for releasing mine-free areas for civilian use should be shortened. In this paper, multisensor signature data collected at four mine suspected areas in different parts of Croatia are presented, their information content is discussed, and first results are described. The multisensor system consists of a multifrequency multipolarization SAR system (DLR Experimental Synthetic Aperture Radar E-SAR), an optical scanner (Daedalus) and a camera (RMK) for color infrared aerial views. E-SAR data were acquired in X-, C-, L- and P- bands, the latter two being fully polarimetric interferometric. This provides pieces of independent information, ranging from high spatial resolution (X-band) to very good penetration abilities (P-band), together with possibilities for polarimetric and interferometric analysis. The Daedalus scanner, with 12 channels between visible and thermal infrared, has a very high spatial resolution. For each of the sensors, the applied processing, geocoding and registration is described. The information content is analyzed in sense of the capability and reliability in describing conditions inside suspected mined areas, as a first step towards identifying their mine-free parts, with special emphasis set on polarimetric and interferometric information.
We discuss the problem of detecting minelike shapes in data coming from mine detection sensors that can provide images, such as an imaging metal detector, an infrared camera or a ground-penetrating radar. Firstly, we show a way for selecting possibly dangerous regions that should be further analyzed, i.e. to which shape analysis methods should be applied. Then, two shape detection methods are presented, both based on the randomized Hough transform. Most of the mines are of a cylindrical shape, so, due to some burial angle, they appear elliptical in 2D images that are taken parallel to the ground. Thus, one of the two presented methods deals with the detection of elliptical shapes. The other method is developed for detecting the hyperbolic signatures of mines in B-scans (vertical data slices into the ground) of ground-penetrating radar data. Finally, pieces of information that can be extracted from detected ellipses and hyperbolas are discussed, and two ways are suggested for their further use towards determining whether a particular selected region contains a mine indeed or not. Both methods are illustrated on real data.
In this paper, two methods for fusion of mine detection sensors are presented, based on belief functions and on voting procedures, respectively. Their application is illustrated and compared on a real multisensor data set collected at the TNO test facilities under the HOM-2000 project. This set contains data acquired by metal detector, infrared camera and ground penetrating radar. The data acquisition and preprocessing are briefly described. For some typical cases presented in this data set, the characteristics extracted and used by both methods are discussed, as well as the answers given by each method and possible causes of potential differences in results. Also, it is shown how the different voting schemes compare to belief functions modeling in various situations, based on the knowledge that is put into the belief functions. Since the roots of the two methods are different, i.e. belief functions involve expert knowledge while voting is a simple approach, the explanations involve these differences. Problems that arise when comparing and evaluating different methods are also addressed. Finally, it is shown that both of the methods have their advantages and drawbacks, depending on the measurement and operational conditions. This paper is a result of a joint work at three European institutions towards a common goal: humanitarian demining.
In this paper, ideas for modeling humanitarian mine detection sensors and their combination within Dempster- Shafer framework are presented. Reasons for choosing this framework are pointed out, taking into account specificity and sensitivity of the problem. This work is done in the scope of the HUDEM project, where three promising and complementary sensors are investigated, so detail analysis is performed in case of fusing the data from them. A way for including in the model influence of various factors on sensor and their result ins discussed as well and will be further analyzed in the future. The application of the approach proposed in this paper is illustrated on the case of sensing metallic objects, but it is possible to modify it for other situations.