Nada Milisavljevic

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A two-level approach for modeling and fusion of antipersonnel mine detection sensors in terms of belief functions within the Dempster–Shafer framework is presented. Three promising and complementary sensors are considered: a metal detector, an infrared camera, and a ground-penetrating radar. Since the metal detector, the most often used mine detection(More)
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(More)
A method for modeling and combination of measures extracted from a ground-penetrating radar (GPR) in terms of belief functions within the Dempster–Shafer framework is presented and illustrated on a real GPR data set. A starting point in the analysis is a preprocessed C-scan of a sand-lane containing some mines and false alarms. In order to improve the(More)
Two approaches for combining humanitarian mine detection sensors are presented—one based on belief functions and the other one based on possibility theory. The approaches are described in parallel. First, different measures are extracted from the sensor data. Mass functions and possibility distributions are then derived from the measures based on prior(More)
The satellite-borne SAR (Synthetic Aperture Radar) is a quite promising tool for high-resolution geo-surface measurement. Recently, there has been a great interest in Coherent Change Detection (CCD), where the coherence between two SAR images is evaluated and analyzed to detect surface changes. The sample coherence threshold may be used to distinguish(More)
Two main humanitarian mine action types may benefit from multi-sensor data fusion techniques: 1) close range antipersonnel (AP) mine detection and 2) mined area reduction. Data fusion for these two applications is presented here. Close range detection consists of detection of (sub-)surface anomalies that may be related to the presence of mines (e.g.,(More)