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Michal SHIMONI

Researcher

mshimoni@elec.rma.ac.be     +32-2-44-14194     Academic details

Research projects

MAUPP

Description:

MAUPP aims to improve our spatial understanding, prediction and forecast of urbanization and urban population in Africa through the use of remote sensing and spatial modelling. The project addresses two specific objectives:

- Produce an urban expansion model at moderate spatial resolution for African cities. This objective will be achieved through i) using HRRS and VHRRS (both optical and radar) to delineate urban extent of a set of cities, compare the accuracy and limitations of the obtained features and optimize the method based on HRRS, ii) using of the best methods identified using HRRS to generate a database of land cover change to urban over the last 30 years across a large number of cities in Africa, iii) using this database to build urban expansion models, evaluate their forecasting accuracy, and apply them to forecast the future distribution of the major urban extents in Africa.

- Understand and predict intra-urban variations in human population density in Afr

GEPATAR

GEPATAR focuses on the collection of data necessary for future preservation activities. For the majority of Belgian people, the country geologically speaking is more or less safe: no volcano, no major earthquake, and no major landslide. However, neo-ground movements are occurring and they are originated from industrial exploitation of the subsoil, urbanization and (des) industrialization processes, and may also be induced by several local phenomena. Ground movements may significantly affect the buildings and contribute to degradation of their structural stability. GEPATAR will develop tools to explore two large federal archive facilities: the RBINS satellite archive and the KIK-IRPA patrimony heritage data base. Dedicated permanent scattering tool will allow the efficient exploitation of hundreds satellite SAR archive data for ground movement mapping with millimeters accuracy. This risk map will permit a geographical search for selected heritage buildings that might be in risk at the K

C4/23

The general objective of this study is to investigate the potential benefit of fused high spatial and spectral resolution imagery and 3D airborne data for several defence applications in urban and harbour areas. The study will evaluate the improvement of the situation awareness and surveillance capability in complex urban and harbour areas using the addressed STARs.

RESPOND-IS

This project PROVIDES an external expertise to ?Respond? project of the GMES framework.

Respond is an alliance of European and International organisations working with the humanitarian community to improve access to maps, satellite imagery and geographic information.

The role of the RMA in the project is to give an expertise in:

-The validation of the RESPONDS?s map products and mainly flood maps;

-To assess the map validation process,

-To propose improvements.

GMOSS

The aim of the GMOSS network of excellence is to integrate Europe?s civil security research so as to acquire and nourish the autonomous knowledge and expertise base Europe needs if it is to develop and maintain an effective capacity for global monitoring using satellite earth observation.

ASME

This project to be funded by the "federal public service for health, food chain safety and environment", concerns a feasibility analysis on the use of sensors like synthetic aperture radar, thermal infrared camera or scanners and hyperspectral imaging spectrometers to enhance the protection of the marine environment (detection in time of pollutions).

This work will be performed in four constructive parts. In the first part, the UGMM operational needs will be gathered and analysed. In the second part, sensors or a set of sensors required to fulfil the end-user’s operational needs will be listed and for each sensor the spectral, spatial, radiometrical and temporal requirements will be derived. In the third part, the necessary adaptations will be listed that should be made to the available airborne platform. And in the fourth part, a costs analysis for off-the-shelf sensor/s acquisition, adapted hardware and adaptation to the airborne platform will be produced.

PolInSAR

L’interférométrie SAR combine deux images de la même scène acquises depuis deux positions de capteurs voisines l’une de l’autre afin d’obtenir une information en altitude sur la surface observée.

En interférométrie polarimétrique SAR (PollnSAR), les techniques polarimértriques sont injectées dans les applications interférométriques pour fournir une sensibilité combinée à la distribution verticale des mécanismes de diffusion.

Détection, restauration, segmentation et classification des images SAR ont déjà été développées dans le projet Stereo ASARTECH (SR00/04).

Dans le cadre de PollnSAR, le travail du SIC consiste à effectuer la fusion PollnSAR aux différents niveaux (bas, intermédiaire et haut niveau) et à évaluer, avec les autres partenaires et les utilisateurs potentiels, les potentialités de PollnSAR en télédétection.

TIRIS

The objective of this project is to detect presence and concentration of polluted gas compounds in the atmosphere using airborne MWIR and LWIR imaging spectroscopy data. AHS-160 data have been collected over a chemical industry situated in the port of Antwerp, during two operational periods on the same day. The airborne data have been calibrated and verified using numerous ground truth measurements collected using field thermal imaging reflectometer (SOC 400T) and traditional in-situ measurements collected in air quality monitoring stations in the port. The image processing uses non-linear absorption features of target gases spectral signature coupled with background suppression techniques to obtain absolute plume column density and plume temperature.

ASTRO+

ASTRO+ aim is to show to EU stakeholders the potential of using Space to support Security operations, objectives being to:

- Demonstrate to the Users its unique advantages in specific situations: non intrusive and legal, available everywhere at any time, robust and non-vulnerable to local threats. Propose a structured and effective access to the EU resources providing timely response to security.

- Contribute to establish an RandT basis by networking Users, Institutions, Industry and Research enhancing the European competitiveness.

ASTRO+ is organised in 3 strands:

- Mission oriented aspect to demonstrate in realistic situation the added value of space to the different end-users: military staff, NGO’s and civil security.

- The integration of the different space technologies: Earth Observation, telecommunication and navigation. This includes the transfer of technologies available in laboratories.

Identification of high efficiency-to-cost R&T directions, for short to long-term service improvement.

PROBA

This document is a proposal for the realisation of a feasibility study to set up requirement consolidation for future hyperspectral thermal infrared imager space mission devoted to dual “civil” and “security” applications.

The feasibility study will provide a review of potential applications of hyperspectral TIR instruments, derivation of instrument requirements and state-of-the-art survey of spaceborne and airborne TIR hyperspectral relevant instrumentation.

HYSAR

Cette recherche consiste à fusionner des données SAR polarimétriques et interférométriques et des données hyperspectrales pour effectuer la classification d’objets faits par l’homme dans des scènes urbaines et industrielles.

Les objectifs scientifiques sont les suivants :

• La fusion d’informations complémentaires redondantes et extraites de données E-SAR et HYMAP

L’amélioration de la discrimination visuelle grâce à la superposition des objets classifiés par fusion et à la modélisation numérique du terrain dérivé de l’interférogramme E-SAR

Publications

  1. J-F Lopez, M Shimoni, and T Grippa. Extraction of African urban and rural structural features using SAR sentinel-1 data. In IEEE Joint Urban Remote Sensing Event (JURSE), Dubai, 2017.
  2. M Shimoni, J Lopez, J Walstra, P y Declercq, L Bejarano-Urrego, E Verstrynge, D Derauw, R Hayen, and K Van Balen. GEPATAR: A GEOTECHNICAL BASED PS-INSAR TOOLBOX FOR ARCHITECTURAL CONSERVATION IN BELGIUM. In International Geoscience and Remote Sensing Symposium (IGARSS), Texas, 2017. IEEE.
  3. Manuel Campos-Taberner, Adriana Romero-Soriano, Carlo Gatta, Gustau Camps-Valls, Adrien Lagrange, Bertrand Le Saux, Annebeau Ere, Alexandre Boulch, Adrien Chan-Hon-Tong, Stephane Herbin, Hicham Randrianarivo, Marin Ferecatu, Michal Shimoni, Gabriele Moser, Devis Tuia, A Lagrange, B Le Saux, A Beaupére, A Boulch, A Chan-Hon-Tong, S Herbin, and H Randrianarivo. Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 9(12):5547-5559, 2016.
  4. M Shimoni, R Haelterman, and P Lodewyckx. Advancing the retrievals of surface emissivity by modelling the spatial distribution of temperature in the thermal hyperspectral scene. In SPIE Defence and Security, Baltimore, 2016.
  5. A.-V Vo, L Truong-Hong, D F Laefer, D Tiede, S D 'oleire-Oltmanns, A Baraldi, M Shimoni, G Moser, and D Tuia. Processing of Extremely High Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest—Part B: 3-D Contest. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 9(12):5560-5575, 2016.
  6. Manuel Cubero-Castan, Jocelyn Chanussot, Veronique Achard, Xavier Briottet, and Michal Shimoni. A physics-based unmixing method to estimate subpixel temperatures on mixed pixels. IEEE Transactions on Geoscience and Remote Sensing, 53(4):1894-1906, 2015.
  7. Wenzhi Liao, Xin Huang, Frieke Van Coillie, Sidharta Gautama, Aleksandra Pi??urica, Wilfried Philips, Hui Liu, Tingting Zhu, Michal Shimoni, Gabriele Moser, and Devis Tuia. Processing of Multiresolution Thermal Hyperspectral and Digital Color Data: Outcome of the 2014 IEEE GRSS Data Fusion Contest. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6):2984-2996, 2015.
  8. M. Shimoni, R. Haelterman, and P. Lodewyckx. Data fusion for improving thermal emissivity separation from hyperspectral data. In International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2015.
  9. M. Shimoni, J. Lopez, Y. Forget, E. Wolff, C. Michellier, T. Grippa, C. Linard, and M. Gilbert. An urban expansion model for African cities using fused multi temporal optical and SAR data. In International Geoscience and Remote Sensing Symposium (IGARSS), 2015.
  10. D. Borghys, M. Idrissa, M. Shimoni, O. Friman, M. Axelsson, M. Lundberg, and C. Perneel. Fusion of multispectral and stereo information for unsupervised target detection in very high resolution airborne data. In Proc. SPIE Signal Processing, Sensor Fusion, and Target Recognition XXII, SPIE Vol 8745, Baltimore, April 2013, 2013.
  11. Dirk C Borghys, Mahamadou Idrissa, Michal Shimoni, Ola Friman, Maria Axelsson, Mikael Lundberg, and Christiaan Perneel. Fusion of multispectral and stereo information for unsupervised target detection in VHR airborne data. In SPIE Defence and Security, Baltimore, 2013. SPIE.
  12. Manuel Cubero-Castan, Xavier Briottet, Véronique Achard, Michal Shimoni, and Jocelyn Chanussot. THE COMPARABILITY OF AGGREGATED EMISSIVITY AND TEMPERATURE OF HETEROGENEOUS PIXEL TO CONVENTIONAL TES METHODS. In IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Gainesville, 2013.
  13. Alwin Dimmeler, Hendrik Schilling, Michal Shimoni, Dimitri Bulatov, and Wolfgang Middelmann. Combined Airborne Sensors in Urban Environment. In SPIE Defence and Security, Dublin, 2013.
  14. Ingmar Renhorn, Veronique Achard, Maria Axelsson, Koen Benoist, Dirk Borghys, Xavier Briottet, Rob Dekker, Alwin Dimmeler, Ola Friman, Ingebjørg Kåsen, Stefania Matteoli, Maria Lo Moro, Thomas Olsvik Opsahl, Mark Van Persie, Salvatore Resta, Hendrik Schilling, Piet Schwering, Michal Shimoni, Trym Vegard Haavardsholm, and Françoise Viallefont. Hyperspectral Reconnaissance in Urban Environment. In SPIE Defence and Security, Baltimore, 2013. SPIE.
  15. M Shimoni, R Haelterman, and C Perneel. SHORT TEMPORAL CHANGE DETECTION IN COMPLEX URBAN AREA. In International Geoscience and Remote Sensing Symposium (IGARSS), Melbourne, 2013. IEEE.
  16. M. Shimoni, M. Idrissa, D. Borghys, T. Haavardsholm, T-O. Opsahl, and C. Perneel. Urban features classification using 3D hyperspectral data. In 5th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Florida, USA, June 2013, 2013.
  17. Michal Shimoni, Mahamadou Idrissa, Dirk Borghys, Trym Haavardsholm, Thomas-Olsvik Opsahl, and Christiaan Perneel. URBAN FEATURES CLASSIFICATION USING 3D HYPERSPECTRAL DATA. In IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013.
  18. Manuel Cubero-castan, Xavier Briottet, Michal Shimoni, Jocelyn Chanussot, and F-Saint Martin H. PHYSIC BASED AGGREGATION MODEL FOR THE UNMIXING OF TEMPERATURE AND OPTICAL PROPERTIES IN THE INFRARED DOMAIN Onera , the French Aerospace Lab , Toulouse 31055 , France Signal and Image Centre , Dept . of Electrical Engineering ( SIC-RMA ), 1000 Brussels ,. In IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lisbon, 2012. IEEE.
  19. Ingmar Renhorn, Maria Axelsson, Koen Benoist, Dirk Bourghys, Yannick Boucher, Xavier Briottet, Sergio De Ceglie, Rob Dekker, Alwin Dimmeler, Remco Dost, Ola Friman, Ingebjørg Kåsen, Jochen Maerker, Mark van Persie, Salvatore Resta, Piet Schwering, Michal Shimoni, and Trym Vegard Haavardsholm. DETECTION IN URBAN SCENARIO USING COMBINED AIRBORNE IMAGING SENSORS. In SPIE Defence and Security, Baltimore, 2012.
  20. M Shimoni and C Perneel. DEDICATED CLASSIFICATION METHOD FOR THERMAL HYPERSPECTRAL IMAGING. In International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2012.
  21. Michal Shimoni, Xavier Briottet, Manuel Cubero-castan, Christiaan Perneel, Véronique Achard, Jocelyn Chanussot, Onera Dota, Avenue Edouard Belin, F-Toulouse, and F-Saint Martin. PERFORMANCE ANALYSIS OF UNSUPERVISED UNMIXING MODELS FOR THERMAL HYEPSRPECTRAL. In IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Beijin, 2012. IEEE.
  22. M. Shimoni, X. Briottet, C. Perneel, B. Tanguy, Y. M. Frédéric, and E. Ben-Dor. Validation of physical unmixing model in the radiative domain. In Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing. IEEE, 2011.
  23. M Shimoni, M Crosetto, S Lang, P Bally, and F Boubila. The Independent Service Validation in GMES RESPOND: The Flood Validation Exercise. International Journal of Digital Earth, 11(1), 2011.
  24. M Shimoni, P Lagueux, C Perneel, and J-P Gagnon. Detection of occluded targets using thermal imaging spectroscopy. In NATO MSS. IEEE, 2011.
  25. M Shimoni, G Tolt, C Perneel, and J Ahlberg. DETECTION OF VEHICLES IN SHADOW AREAS. In IEEE, editor, IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lisbon, 2011.
  26. M. Shimoni, G. Tolt, C. Perneel, and J. Ahlberg. Detection of vehicles in shadow areas using combined hyperspectral and lidar data. In International Geoscience and Remote Sensing Symposium (IGARSS), Lisbon, 2011. IEEE.
  27. G Tolt, M Shimoni, and J Ahlberg. A SHADOW DETECTION METHOD FOR REMOTE SENSING IMAGES USING VHR HYPERSPECTRAL AND LIDAR DATA. In IEEE IGARSS, 2011.
  28. M Shimoni, R Heremans, and C Perneel. DETECTION OF SMALL CHANGES IN COMPLEX URBAN AND INDUSTRIAL SCENES USING IMAGING SPECTROSCOPY. In International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2010.
  29. M Shimoni, C Perneel, and J-P Gagnon. DETECTION OF OCCLUDED TARGETS USING THERMAL IMAGING SPECTROSCOPY. In IEEE, editor, IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Reykjavik, 2010.
  30. D Borghys, E Truyen, M Shimoni, and C Perneel. Anomaly detection in complex environments: Evaluation of the inter- and intra-method consistency. In 1st IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Grenoble, France, August 2009.
  31. D Borghys, E Truyen, M Shimoni, and C Perneel. Anomaly Detection in Hyperspectral Images of Complex Scenes. In Earsel Symposium, Chania, Greece, June 2009.
  32. M Shimoni, D. Borghys, R Heremans, C Perneel, and M Acheroy. Fusion of PolSAR and PolInSAR data for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11:169-180, June 2009.
  33. M Shimoni, D Borghys, R Heremans, C Perneel, and M Acheroy. Fusion of PolSAR and PolInSAR data for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11:169-180, 2009.
  34. M Shimoni, R Heremans, D Borghys, and C Perneel. Change Detection in Complex Industrial and Urban Scenes using VNIR and TIR Hyperspectral Imagery. In 6th EARSeL SIG IS Workshop, Tel Aviv, Israel, March 2009.
  35. M Shimoni, D. Borghys, R Heremans, C Perneel, and M Acheroy. Land-cover classification using fused PolSAR and PolInSAR. In European Conference on Synthetic Aperture Radar (EUSAR), Friederichshafen, Germany, June 2008.
  36. D Borghys, Michal Shimoni, G Degueldre, and C Perneel. Improved object recognition by fusion of hyperspectral and SAR data. In 5th EARSeL SIG IS workshop on Imaging Spectroscopy: Innovation in environmental research, Bruges, Belgium, April 2007.
  37. D Borghys, Michal Shimoni, and C Perneel. Change detection in urban scenes by fusion of SAR and Hyperspectral data. In Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VII, volume 6749, Florence, Italy, September 2007.
  38. D Borghys, Michal Shimoni, and C Perneel. Semi-Automatic Hyperspectral Image Classification of Urban Areas using Logistic Regression. In 1st EARSeL SIG Urban Remote Sensing Workshop Urban Remote Sensing - Challenges and Solutions, Berlin, GE, March 2006.
  39. D Borghys, Michal Shimoni, and C Perneel. Supervised classification of hyperspectral images using a combination of spectral and spatial information. In SPIE Conference on Image And Signal Processing for Remote Sensing XI; Bruges, volume 5982, September 2005.
  40. V. Lacroix, M. Shimoni, M. Acheroy, and E. Wolff. A geographical information system for humanitarian demining. In ISPRS, Vol XXXIII, Amsterdam, July 2000. (PostScript)