Two weeks left for submitting to IWMbD2017
The deadline for the submission of peer-reviewed papers to the Third International Workshop on Metamaterials-by-Design has been extended to September…
Ing. Ahmed won the "Student Competition Award" at QNDE-2017
The ELEDIA Research Center is pleased to announce that Ing. S. Ahmed won among all the applicants disciplines into "Student Competition…
IWMbD2017 Registration is Open!
The ELEDIA Research Center announces that the Registration to the "Third International Workshop on Metamaterials-by-Design" is open. The Workshop will…
IEEE-JMMCT Special Section on MbD
The ELEDIA Research Center is pleased to announce an upcoming Special Section of the IEEE Journal on Multiscale and Multiphysics…
R. J. Mailloux has joined ELEDIA
The ELEDIA Research Center is pleased to announce that Dr. R. J. Mailloux is member of the ELEDIA Teaching Staff.…
Subsurface sensing through electromagnetic interrogating waves has been widely employed in the last few years in applications such as mine detection, archeological analysis, natural resources exploration, and NDE/NDT of structures and pavements. Nevertheless, real-time reliable processing techniques able to automatically detect relevant features in electromagnetic backscattered data are still not available, because of the inherent complexity of the problem at hand. Accordingly, the development of inverse scattering techniques specifically targeted at the detection of subsurface anomalies (e.g., landmines) is of great interest.
Landmine detection, Earth Science, environmental remediation, archeological analysis, geophysical investigation.





Members of the ELEDIA Research Center have developed several techniques for the detection and classification of buried object based on the learning-by-example paradigm. More specifically, the study, derivation, and testing of inverse scattering approaches based on Support Vector Machines (SVM) has been carried out in order to enable the real-time classification of multiple buried targets and determination of the probability of occurrence of relevant objects in the investigation domain.


Keywords: Subsurface Imaging, Support Vector Machines, Learning-by-Example Techniques, Inverse Scattering Problem, Pattern Classification.

See Also
  • A. Massa, Boni, A.; M. Donelli, "A Classification Approach Based on SVM for Electromagnetic Subsurface Sensing," IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 9, pp. 2084-2093, Sept. 2005 doi:10.1109/TGRS.2005.853186
  • L. Lizzi, F. Viani, P. Rocca, G. Oliveri, M. Benedetti, A. Massa, "Three-dimensional real-time localization of subsurface objects - From theory to experimental validation," 2009 IEEE International Geoscience and Remote Sensing Symposium, vol. 2, pp.II-121-II-124, 12-17 July 2009 doi:10.1109/IGARSS.2009.5418018
  • M. Donelli, M. Benedetti, Lesselier, D.; A. Massa, "A qualitative two-step inversion approach for the reconstruction of subsurface defects," 2009 IEEE International Geoscience and Remote Sensing Symposium, vol. 4, pp.IV-224-IV-227, 12-17 July 2009 doi:10.1109/IGARSS.2009.5417331
  • M. Donelli, M. Benedetti, P. Rocca, Melgani, F.; A. Massa, "Three dimensional electromagnetic sub-surface sensing by means of a multi-step SVM-based classification technique," 2007 IEEE Antennas and Propagation Society International Symposium, pp.1801-1804, 9-15 June 2007 doi:10.1109/APS.2007.4395866