Data Fusion for Close‐Range Detection

Two approaches for combining humanitarian mine detection sensors are described in parallel, one based on belief functions and the other one based on possibility theory. In a first step, different measures are extracted from the sensor data. After that, based on prior information, mass functions and possibility distributions are derived. The combination of possibility degrees, as well as of masses, is performed in two steps. The first one applies to all measures derived from one sensor. The second one combines results obtained in the first step for all sensors used. Combination operators are chosen to account for different characteristics of the sensors. Comparison of the combination equations of the two approaches is performed as well. Furthermore, selection of the decision rules is discussed for both approaches. These approaches are illustrated on a set of real mines and non‐dangerous objects and using three sensors: an infrared camera, an imaging metal detector and a ground‐penetrating radar

InSAR Coherence and Intensity Changes Detection

This research aims at differentiating human-induced effects over the landscape from the natural ones by exploiting a combination of amplitude and phase changes in satellite radar images. At a first step, ERS and Envisat data stacks are processed using COS software developed by the company SARMAP. Various features related to amplitude and phase as well as to their changes are then extracted from images of the same sensor. Combinations of the features extracted from one image, from several images of one sensor as well as from different sensors are performed to derive robust indicators of potential human-related changes. Finally, possibilities of exploiting and integrating other types of information sources such as various reports, maps, historical or agricultural data, etc. in the combination process are analyzed to improve the obtained results. The outcomes are used to evaluate the potential of this method applied to Sentinel-1 images

Denoising and migration techniques for target identification from ground penetrating radar 2d data; a case study

Le radar de pénétration du sol (GPR) est une technique de télédétection employée pour obtenir l'endroit et la réflectivité spatiaux des objets enterrés. Puisque la plupart des antennes de GPR ne sont pas directives, les signaux dispersés enregistrés par le radar se prolongent au-dessus d'une grande ouverture latérale [1, 2]. Dans cette étude, des algorithmes de débruitage et de migration sont employés pour refocaliser les signaux dispersés de nouveau à leur point d'origine. Les données ont été prises pour différents scénarios. Afin de réaliser la séparation optimale de la signature de la cible de la réponse du soil, techniques de débruitage son utilisées sur les données 2D. La transformée de Hough randomisé est employé pour extraire des informations importantes [3]. Ces informations sont incluses dans un algorithme de migration [4], et la largeur aproximée de l'objet dans la direction du balayage est trouvée. Bien que les résultas sont pormetteurs, les algorithmes doivent toujours être validés dans différentes conditions

Remote Sensing for Non‐Technical Survey

This chapter describes the research activities of the Royal Military Academy on remote sensing applied to mine action. Remote sensing can be used to detect specific features that could lead to the suspicion of the presence, or absence, of mines. Work on the automatic detection of trenches and craters is presented here. Land cover can be extracted and is quite useful to help mine action. We present here a classification method based on Gabor filters. The relief of a region helps analysts to understand where mines could have been laid. Methods to be a digital terrain model from a digital surface model are explained. The special case of multi‐spectral classification is also addressed in this chapter. Discussion about data fusion is also given. Hyper‐spectral data are also addressed with a change detection method. Synthetic aperture radar data and its fusion with optical data have been studied. Radar interferometry and polarimetry are also addressed

Prediction of the effects of soil and target properties on the antipersonnel landmine detection performance of ground-penetrating radar: A Colombian case study

The performance of ground-penetrating (GPR) radar is determined fundamentally by the soil electromagnetic (EM) properties and the target characteristics. In this paper, we predict the effects of such properties on the antipersonnel (AP) landmine detection performance of GPR in a Colombian scenario. Firstly, we use available soil geophysical information in existing pedotransfer models to calculate soil EM properties. The latter are included in a two-dimensional (21)), finite-difference time-domain (FDTD) modeling program in conjunction with the characteristics of AP landmines to calculate the buried target reflection. The approach is applied to two soils selected among Colombian mine-affected areas, and several local improvised explosive devices (IEDs) and AP landmines are modeled as targets. The signatures from such targets buried in the selected soils are predicted, considering different conditions. Finally, we show how the GPR can contribute in detecting Iow- and non-metallic targets in these Colombian soils. Such a system could be quite adequate for complementing humanitarian landmine detection by metal detectors. (c) 2007 Elsevier B.V. All rights reserved

Land Applications of Radar Remote Sensing

The aim of this book is to demonstrate the use of SAR data in three application domains, i.e. land cover (Part II), topography (Part III), and land motion (Part IV). These are preceded by Part I, where an extensive and complete review on speckle and adaptive filtering is provided, essential for the understanding of SAR images. Part II is dedicated to land cover mapping. Part III is devoted to the generation of Digital Elevation Models based on radargrammetry and on a wise fusion (by considering sensor characteristic and acquisition geometry) of interferometric and photogrammetric elevation data. Part IV provides a contribution to three applications related to land motion

Reduction of Mine Suspected Areas by Multisensor Airborne Measurements - First Results

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 goals 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 the first results are described. The multisensor system consists of a multifrequency multipolarisation SAR system (DLR Experimental Synthetic Apertur Radar ESAR), an optical scanner (Daedalus) and a camera (RMK) for CIR aerial views. ESAR data were acquired in X-, C-, L- and P- bands, the latter two being fully polarimetric interferometric. This provides 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 long infrared, has a very high spatial resolution. For each of the sensors, the applied processing, geocoding and registration is described. The information content is analysed 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

Filtering soil surface and antenna effects from GPR data to enhance landmine detection

The detection of antipersonnel landmines using ground-penetrating radar (GPR) is particularly hindered by the predominant soil surface and antenna reflections. In this paper, we propose a novel approach to filter out these effects from 2-D off-ground monostatic GPR data by adapting and combining the radar antenna subsurface model of Lambot et al. with phase-shift migration. First, the antenna multiple reflections originating from the antenna itself and from the interaction between the antenna and the ground are removed using linear transfer functions. Second, a simulated Green's function accounting for the surface reflection is subtracted. The Green's function is derived from the estimated soil surface dielectric permittivity using full-wave inversion of the radar signal for a measurement taken in a local landmine-free area. Third, off-ground phase-shift migration is performed on the 2-D data to filter the effect of the antenna radiation pattern. We validate the approach in laboratory conditions for four differently detectable landmines embedded in a sandy soil. Compared to traditional background subtraction, this new filtering method permits a better differentiation of the landmine and estimation of its depth and geometrical properties. This is particularly beneficial for the detection of landmines in low-contrast conditions