Nada Milisavljevic

Royal Military Academy Belgium

Nada Milisavljević earned an electrical engineering degree from the University of Novi Sad in 1992, a Master of Science degree from the University of Belgrade in 1996, and a PhD degree in applied sciences from École Nationale Supérieure des Télécommunications, Paris (2001). From 1992 until 1997 she worked as a research and teaching assistant at the University of Novi Sad. Since 1997 she has been working as a researcher at the Royal Military Academy, involved in humanitarian demining, image processing and data fusion. She is an International Board member for the International Journal of Image and Data Fusion.

1books edited

3chapters authored

Latest work with IntechOpen by Nada Milisavljevic

Data fusion is a research area that is growing rapidly due to the fact that it provides means for combining pieces of information coming from different sources/sensors, resulting in ameliorated overall system performance (improved decision making, increased detection capabilities, diminished number of false alarms, improved reliability in various situations at hand) with respect to separate sensors/sources. Different data fusion methods have been developed in order to optimize the overall system output in a variety of applications for which data fusion might be useful: security (humanitarian, military), medical diagnosis, environmental monitoring, remote sensing, robotics, etc.

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Sensor and Data Fusion

Edited by Nada Milisavljevic

Chapters authored

Salt Tectonics of the Lisan Diapir Revealed by Synthetic Aperture Radar Images

By Damien Closson, Najib Abou Karaki, Nada Milisavljević, Frédéric Hallot, and Marc Acheroy

Part of the book: Tectonics

Estimation of Cultivated Areas Using Multi-Temporal SAR Data

By Nada Milisavljević, Francesco Collivignarelli and Francesco Holecz

Part of the book: Land Applications of Radar Remote Sensing

Data Fusion for Close‐Range Detection

By Nada Milisavljevic

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.

Part of the book: Mine Action

Nada Milisavljevic

Latest work with IntechOpen by Nada Milisavljevic

Sensor and Data Fusion

Chapters authored

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