The Belgian participation in the ORFEO programme A bilateral enrichment

1. The Belgian participation in the ORFEO programmeA bilateral enrichment Carine Petit & Joost Vandenabeele Oberpfaffenhofen – 28/03/2007

2. Outline • Context of the Belgian participation in ORFEO • Thematic section and few examples • Methodology section and few examples • Invitation for further collaboration • Conclusion

3. Context Belgium has a long tradition of collaboration with CNES : • SPOT (1986) and VEGETATION (1998) • CTIV hosted by VITO in Mol has been official distributor of VEGETATION images since 2006

4. In the framework of the Belgian participation in Pléiades programme (26 May 2004, 27 M€) Belgium ORFEO Budget: 1,2 MEURO Thematic section: 0,1 MEURO Methodology section 1,1 MEURO Duration: 2004-2009 Belgian participation in support programme for the use of Orfeo data

5. ORFEO thematic priorities • GT1 : Sea and coastlines • GT2 : Risks and humanitarian aid • GT3 : Cartography, Town and Country Planning • GT4 : Geology et Geophysics • GT5 : Hydrology • GT6 : Forestry • GT7 : Agriculture

6. GT1 : Sea and coastlinesMUMM, VITO GT2 : Risks and humanitarian aidERM GT3 : Cartography, Town and Country Planning VUB, RUG, ULg, ULB, ERM, ING, VITO Meeting in Brussels – 9/5/2005 GT6 : Forest UCL GT7 : AgricultureUCL, ULg, CRA Thematic section: Belgian participation

7. Belgian mapping expertise with HR remote sensing: Sediments dynamics along the Belgian shoreline Habitats on tidal sand banks Water quality parameters (suspended sediments and chl a) Atmospheric corrections above water bodies Capabilities in airborne hyperspectral RS discussed in the context of a Post-Pléiades mission VITO and MUMM communicated their requirements in terms of data and products to CNES Opens the door for possible joint projects Waiting to work with the data now ! GT1 : Sea and coastlines - Bart Deronde (VITO)

8. Time as coordinator of the working group A robust line detection tool (already in ORFEO toolbox) Better connection with GMOSS by: Facilitating the transfer of knowledge from GMOSS Introducing SERTIT as an associated partner in GMOSS Deeper relationships with UNOSAT and CNES in the context of GMOSS Participation in a real-time exercise with SERTIT and CNES CNES-RMA joint publications on SAR Training of RMA in CNES New publications, FP7 projects or other, further contacts closer to the end users Sharing of scientific tools, methods, and data GT2 : Risks and humanitarian aid – Vinciane Lacroix (RMA)

9. Belgian administration, end-user and researcher needs in the framework of CAP Agri-environmental indicators from the Belgian SAGRIWATEL project Definition of agricultural and environmental objects and of ORFEO product sheets Thematic study on a Walloon site New research opportunities using the ORFEO toolbox Feature extraction and object recognition from imagery that would be applied to agriculture and environmental purposes Valorisation in Belgian and European ongoing or future studies New collaboration with French partners in internal or official projects New research orientation to feature extraction and object recognition from imagery GT7 : Agriculture – Dominique Buffet (CRA)

10. One call for proposals for doctoral or post-doctoral research in October 2005 Budget per project: maximum 200 000 EUR Number of selected proposals: 6 Launch of the projects: March 2006 April - June 2007: first progress report first meetings of the steering committees >>> with experts from CNES and DLR Methodology section : Belgian participation

11. CHADE: Change detection for updates of vector databases through multi-level region-based classification of VHR data (Wolff – ULB) GEMITOR: Multimodal Geo-referencing of 3D VHR Optical and X-Band SAR imagery (Jamar – ULg) EMSOR: Automatic Extraction of Man-Made Structures from VHR Optical and SAR Data for Change Detection (Acheroy – RMS) URMO3D:Combination of satellite-derived raster and vector data for 3D city modelling (Goossens – UGent) SYNOPRA:Synergy of very high resolution optical and radar data in forest mapping and inventory (De Wulf – UGent) ASSIMIV: Detection and description of geographic objects by means of multi-source data assimilation (Defourny – UCL) Selection of ORFEO proposals

12. ORFEO theme Use of VHR optical imagery with ancillary data Main objectives Update and upgrade vector databases using a single image and ancillary data Study test sites Ardennes (Forest), Famenne (Open fields) and Montpellier (Open canopy) Images used and field measurements foreseen Quickbird DGPS survey for planimetric accuracy assessment ASSIMIV: Detection and description of geographic objects by means of multi-source data assimilation

13. Methodology key points Use of shade and parallax model to check boundaries Screening of artefacts of object boundaries between the database and the image Object-based texture analysis Contextual classification Duration: May 2006 to December 2009 Short presentation of the laboratory Lab of environmetrics and geomatics, Université catholique de Louvain Pr. Pierre Defourny : defourny@enge.ucl.ac.be Julien Radoux : radoux@enge.ucl.ac.be www.enge.ucl.ac.be ASSIMIV: Detection and description of geographic objects by means of multi-source data assimilation

14. Intermediate results (WP 100) Trigonometric GIS model of shade and parallax shift Region-based detection of boundaries Oversegmentation Spectral characterization of remaining boundaries Unsupervised classification based on ancillary data Detection of inconsistencies between image and vector out of a confidence interval ASSIMIV: Detection and description of geographic objects by means of multi-source data assimilation Shade Parallax shift

15. Forthcoming activities Statistical outlier detection >>> ORFEO Toolbox Use of a priori information Multimodal distribution modeling Object-based texture description From pixels to objects Feature selection Contextual classification Self learning from ancillary data ASSIMIV: Detection and description of geographic objects by means of multi-source data assimilation

16. ASSIMIV as a part of ORFEO New collaborations Partnership with “Maison de la télédétection” initiated for WP 200 Thanks to ORFEO toolbox Great opportunity to share methods Easy access to state-of-the-art algorithms ASSIMIV: Detection and description of geographic objects by means of multi-source data assimilation

17. Limited knowledge on the combined use and fusion of SAR and optical data Great interest of end-users for: cartography security (rapid mapping, damage assessment) Need of methods to combine SAR and optical data at a metric resolution EMSOR:Automatic Extraction of Man-Made Structures from VHR Optical and SAR Data for Change Detection

18. “Images of the same object obtained by different sensors are different representations of the same reality” [Inglada and Giros, IEEE TGRS, Oct 2004] Measures exist of the similarity between images for multi-sensor image registration (medical imaging and computer vision) Extension of the similarity measures used for image registration to change detection Several “families” of similarity measures exist; among those valid for multi-sensor images, we have: measures based on radiometry values and their probabilities measures using only probabilities (e.g., mutual information) EMSOR: Method is based onsimilarity measures

19. How to deal with very different information sources? What is meant by “similarity” between, e.g., a radar and an optical image? How to estimate the reduction of similarity due to a change in the scene? Similarity measures identify a pixel as changed if the probability of its radiometry value with respect to that of the neighbours, in the two images, is not the same (i.e. beyond a given threshold) EMSOR: Similarity measures used as change detectors raise several questions

20. EMSOR: A time series of optical (1990), SAR (1993) and hyperspectral (2004) images was tested Hyperspectral (2004) Optical (1990) Mutual information similarity image • Derived change map with ground truth data in • red: always present structures • yellow: new ones

21. Varying characteristics of the “similarity” images led to no “absolute” measure of changes Better results of change estimation obtained with Optical/HS images with the mutual information and the cluster reward algorithm measures Similarity images allowed distinction between vegetated areas and man-made structures Comments Acknowledgements • Thanks to M. Idrissa for the software development • Researcher: Vito Alberga (alberga@elec.rma.ac.be) • Signal and Image Centre, Royal Military Academy

22. Use of three databases - Crop field database Ministry of Agriculture (CAP) - 1:10000 topo maps National Geographic Institute 1:1000 PICC database Ministry of equipment and transport Analysis ofuser needs and requirements Towards a method of change detection Geocoding, orthorectification of images and co-registration with DB Direct Image – Database change detection Direct integration of the vector database in the segmentation process Identification and classification of the new objects compared with the vector database CHADE: Change detection for updating vector databases through multi-level region-based classification of VHR data

23. New object detection C Image Database Segmentation

24. IGEAT CHADE: Change detection for updating vector databases through multi-level region-based classification of VHR data • Future tasks • Classification for the object identification and change detection at object level • Overcome some problems: occlusions, shadows, elevation displacement… >>> Collaboration with ASSIMIV www.ulb.ac.be/project/orfeo SIGTel, IGEAT, Université Libre de Bruxelles Promoter: Wolff Eléonore (ewolff@ulb.ac.be) Researcher: Carleer Alexandre (acarleer@ulb.ac.be)

25. ORFEO theme Optic/Radar complementarity and interferometry Methodological challenge To develop the ressources required in terms of georeferencing of visible and radar images in a common reference frame To perform a pixel level fusion of both modalities Main objectives Allow joint operational exploitation of optical and radar Very High Resolution data using 3D information issued from SAR interferometry Presentation by Antonella Belmonte in the geometry session (on 28 March at 11:50) GEMITOR: Multimodal Geo-referencing of 3D VHR Optical and X-Band SAR imagery

26. New Research programme in Earth Observation Approved on 3 February 2006 Duration: 2006 – 2014 Budget: 25,85 M€ Mid-April 2007: second call worth 1,79 M€ Proposals to be submitted for 3types of projects: Thematic poles Spin-off projects >>> ORFEO Innovation projects >>> ORFEO Partnerships research-public-private Invitation to participate in STEREO II

27. Research thematic priorities ORFEO (2004-2009) • GT1 : Sea and coastlines • GT2 : Risks and humanitarian aid • GT3 : Cartography, Town and Country Planning • GT4 : Geology et Geophysics • GT5 : Hydrology • GT6 : Forestry • GT7 : Agriculture STEREO (2006-2014) • Global monitoring of vegetation and evolution of terrestrial ecosystems • Management of the local and regional environment (water, soil, forest, agriculture, coastal zones, urban areas) • Health and humanitarian aid • Security and risk management

28. STEREO II: International cooperation EXTERNAL BUDGET:10% STEREO BUDGET: 90 % 80 10 10 : Belgian teams : International teams • International teams • Cannot coordinate a project • Must get 50 % external co-financing • Call in 2006 : • 9,19 M€,15 projects with21 international partners

29. Belgium offered money, time and working forces Belgian expertise was shared within 5 thematic groups 6 Belgian methodological studies will provide the ORFEO toolbox with algorithms and methods Belgium got data, French and German expertise, access to the ORFEO toolbox Common publications New collaborations, invitation to participate in a Belgian call for proposals Win-win experience for both parts To conclude … … thank you for your attention !