Monitoring urban areas with Sentinel-2A data: Application to the update of the Copernicus High Resolution Layer Imperviousness Degree

Remote Sensing

Volumn 8, Issue 7, 2016, Pages

  Lefebvre, Antoine ^a   Sannier, Christophe ^b   Corpetti, Thomas ^c  

^a CAMPUS DE BEAULIEU   (France)

^b SIRS   (France)

^c UNIVERSITÉ DE NANTES   (France)

Author keywords

Copernicus; Data fusion; Imperviousness; Sentinel 2; Urban areas

Indexed keywords

DATA FUSION; FORMAL LOGIC;

COPERNICUS; DEMPSTER-SHAFER THEORY; IMPERVIOUSNESS; LAND COVER CLASSIFICATION; OPERATIONAL PRODUCTION; SENTINEL-2; URBAN AREAS; VERY HIGH RESOLUTION DATUM;

URBAN PLANNING;

EID: 85013641319     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs8070606     Document Type: Article

References (55)

1. United Nations. World Population Prospects: The 2015 Revision; Technical Report; United Nations: New York, NY, USA, 2015.
2. Immitzer, M.; Vuolo, F.; Atzberger, C. First experience with Sentinel-2 data for crop and tree species classifications in Central Europe. Remote Sens. 2016, 8, 166.
3. Radoux, J.; Chomé, G.; Jacques, D.; Waldner, F.; Bellemans, N.; Matton, N.; Lamarche, C.; D'Andrimont, R.; Defourny, P. Sentinel-2's potential for sub-pixel landscape feature detection. Remote Sens. 2016, 8, 488.
4. Pesaresi, M.; Corbane, C.; Julea, A.; Florczyk, A.; Syrris, V.; Soille, P. Assessment of the added-value of Sentinel-2 for detecting built-up areas. Remote Sens. 2016, 8, 299.
5. Bovolo, F.; Bruzzone, L. A theoretical framework for unsupervised change detection based on change vector analysis in the polar domain. IEEE Trans. Geosci. Remote Sens. 2007, 45, 218-236.
6. Gong, X.; Corpetti, T. Adaptive window size estimation in unsupervised change detection. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2013, 6, 991-1003.
7. Inglada, J.; Mercier, G. A new statistical similarity measure for change detection in multitemporal sAR Images and its extension to multiscale change analysis. IEEE Trans. Geosci. Remote Sens. 2007, 45, 1432-1445.
8. Mercier, G.; Derrode, S.; Trouvé, E.; Bombrun, L. Chapter 5 Change detection. In Multivariate Image Processing: Methods and Applications; Chanussot, J., Chehdi, K., Collet, C., Eds.; Wiley-ISTE: London, UK, 2009; pp. 109-152.
9. Hsu, Y.; Nagel, H.; Rekers, G. New likelihood test methods for change detection in image sequences. Comput. Vis. Graph. Image Process. 1984, 26, 73-106.
10. Aach, T.; Kaup, A.; Mester, R. Statistical model-based change detection in moving video. Signal Process. 1993, 31, 165-180.
11. Kervrann, C.; Boulanger, J.; Pecot, T.; Perez, P. Discriminant random field and patch-based redundancy analysis for image change detection. In Proceedings of the 2009 IEEE InternationalWorkshop on Machine Learning for Signal Processing, Grenoble, France, 1-4 September 2009; pp. 1-6.
12. He, L.; Laptev, I. Robust change detection in dense urban areas via SVM classifier. In Proceedings of the Joint Urban Remote Sensing Event, Shanghai, China, 20-22 May 2009; pp. 1-5.
13. Ridd, M.K.; Liu, J. A comparison of four algorithms for change detection in an urban environment. Remote Sens. Environ. 1998, 63, 95-100.
14. Blaschke, T.; Lang, S.; Hay, G. Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications; Springer: Berlin, Germany; Heidelberg, Germany, 2008.
15. Lefebvre, A.; Corpetti, T.; Hubert-Moy, L. A measure for change detection in very high resolution remote sensing images based on texture analysis. In Proceedings of the 16th IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, 7-10 November 2009; pp. 1697-1700.
16. Niemeyer, I.; Marpu, P.; Nussbaum, S. Change detection using object features. In Object-Based Image Analysis; Blaschke, T.; Lang, S.; Hay, G., Eds.; Lecture Notes in Geoinformation and Cartography; Springer: Berlin, Germany; Heidelberg, Germany, 2008; pp. 185-201.
17. Celik, T. Change detection in satellite images using a genetic algorithm approach. IEEE Geosci. Remote Sens. Lett. 2010, 7, 386-390.
18. Moser, G.; Serpico, S. Unsupervised change detection from multichannel SAR data by Markovian data fusion. IEEE Trans. Geosci. Remote Sens. 2009, 47, 2114-2128.
19. Bovolo, F.; Bruzzone, L.; Marconcini, M. A novel approach to unsupervised change detection based on a semisupervised SVM and a similarity measure. IEEE Trans. Geosci. Remote Sens. 2008, 46, 2070-2082.
20. Petit, C.; Scudder, T.; Lambin, E. Quantifying processes of land-cover change by remote sensing: Resettlement and rapid land-cover changes in south-eastern Zambia. Int. J. Remote Sens. 2001, 22, 3435-3456.
21. Silipaswan, C.; Verbyla, D.; Mcguire, A. Land cover change on the Seward Peninsula: The use of remote sensing to evaluate the potential influences of climate warming on historical vegetation dynamics. Can. J. Remote Sens. 2001, 27, 542-554.
22. Lu, D.; Mausel, P.; Brondizio, E.; Moran, E. Change detection techniques. Int. J. Remote Sens. 2004, 25, 2365-2407.
23. Radke, R.; Andra, S.; Al-Kofahi, O.; Roysam, B. Image change detection algorithms: A systematic survey. IEEE Trans. Image Process. 2005, 14, 294-307.
24. Serpico, S.; Bruzzone, L. Information processing for remote sensing. In Change Detection; Chen, C.H., Ed.; World Scientific Publishing: Singapore, 1999; pp. 319-336.
25. Singh, A. Digital change detection techniques using remotely-sensed data. Int. J. Remote Sens. 1989, 10, 989-1003.
26. Du, P.; Liu, S.; Gamba, P.; Tan, K.; Xia, J. Fusion of difference images for change detection over urban areas. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2012, 5, 1076-1086.
27. Gan, Q.; Harris, C.J. Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion. IEEE Trans. Aerosp. Electron. Syst. 2001, 37, 273-279.
28. Sun, S.L.; Deng, Z.L. Multi-sensor optimal information fusion Kalman filter. Automatica 2004, 40, 1017-1023.
29. Shafer, G. Mathematical Theory of Evidence; Princeton University Press: Princeton, NJ, USA, 1976.
30. Smarandache, F.; Dezert, J. Advances and Applications of DSmT for Information Fusion (Collected Works); Infinite Study; American Research Press: Rehoboth, NM, USA, 2006; Volume 2.
31. Le Hégarat-Mascle, S.; Seltz, R.; Hubert-Moy, L.; Corgne, S.; Stach, N. Performance of change detection using remotely sensed data and evidential fusion: Comparison of three cases of application. Int. J. Remote Sens. 2006, 27, 3515-3532.
32. Lu, Y.; Trinder, J.; Kubik, K. Automatic building detection using the Dempster-Shafer algorithm. Photogramm. Eng. Remote Sens. 2006, 72, 395-403.
33. Corpetti, T.; Planchon, O. Front detection on satellite images based on wavelet and evidence theory: Application to the sea breeze fronts. Remote Sens. Environ. 2011, 115, 306-324.
34. Mora, B.; Fournier, R.A.; Fouher, S. Mapping the health of mature deciduous forest stands by fusing multi-source geospatial data with Dempster's combination rule. Int. J. Remote Sens. 2012, 33, 1139-1163.
35. Rottensteiner, F.; Trinder, J.; Clode, S.; Kubik, K. Using the Dempster-Shafer method for the fusion of LIDAR data and multi-spectral images for building detection. Inf. Fusion 2005, 6, 283-300.
36. Le Hegarat-Mascle, S.; Bloch, I.; Vidal-Madjar, D. Application of Dempster-Shafer evidence theory to unsupervised classification in multi-source remote sensing. IEEE Trans. Geosci. Remote Sens. 1997, 35, 1018-1031.
37. Hagolle, O.; Huc, M.; Pascual, D.V.; Dedieu, G. A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENmS, LANDSAT and SENTINEL-2 images. Remote Sens. Environ. 2010, 114, 1747-1755.
38. Zhu, Z.; Woodcock, C.E. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens. Environ. 2012, 118, 83-94.
39. Lefebvre, A.; Corpetti, T.; Hubert-Moy, L. Object-oriented approach and texture analysis for change detection in very high resolution images. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA, 7-11 July 2008; pp. 663-666.
40. Smits, P.; Annoni, A. Updating land-cover maps by using texture information from very high-resolution space-borne imagery. IEEE Trans. Geosci. Remote Sens. 1999, 37, 1244-1254.
41. Puissant, A.; Hirsch, J.;Weber, C. The utility of texture analysis to improve per-pixel classification for high to very high spatial resolution imagery. Int. J. Remote Sens. 2005, 26, 733-745.
42. Gong, P.; Howarth, P. The use of structural information for improving land-cover classification accuracies at the rural-urban fringe. Photogramm. Eng. Remote Sens. 1990, 56, 67-73.
43. Herold, M.; Liu, X.; Clarke, K. Spatial metrics and image texture for mapping urban land use. Photogramm. Eng. Remote Sens. 2003, 69, 991-1001.
44. Pesaresi, M.; Gerhardinger, A.; Kayitakire, F. A robust built-up area presence index by anisotropic rotation-invariant textural measure. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2008, 1, 180-192.
45. Haralick, R.; Shanmugam, K.; Dinstein, I. Textural features for image classification. IEEE Trans. Syst. Man Cybernet. 1973, 3, 610-621.
46. Pesaresi, M.; Blaes, X.; Ehrlich, D.; Ferri, S.; Gueguen, L.; Halkia, M.; Kauffmann, M.; Kemper, T.; Marin-Herrera, M.A.; Ouzounis, G.K.; et al. A global human settlement layer from optical HR/VHR RS data: Concept and first results. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2013, 6, 2102-2131.
47. EEA. Urban Sprawl in Europe. The Ignored Challenge Technical Report 10; European Environment Agency: Copenhagen, Denmark, 2006.
48. Breiman, L. Bagging predictors. Mach. Learn. 1996, 140, 123-140.
49. Puissant, A.; Rougier, S.; Stumpf, A. Object-oriented mapping of urban trees using Random Forest classifiers. Int. J. Appl. Earth Observ. Geoinf. 2014, 26, 235-245.
50. Gangkofner, U.;Weichselbaum, J.; Kuntz, S.; Brodsky, L.; Larsson, K.; de Pasquale, V. Update of the European high-resolution layer of built-up areas and soil sealing 2006 with Image2009 data. In Proceedings of the 30th EARSeL Symposium: Remote Sensing for Science, Education and Culture, Paris, France, 31 May-4 June 2010.
51. Lefebvre, A.; Beaugendre, N.; Pennec, A.; Sannier, C.; Corpetti, T. Using data fusion to update built-up areas of the 2012 European High-Resolution Layer Imperviousness. In Proceedings of the 33rd EARSeL Symposium, Matera, Italy, 3-6 June 2013.
52. Xian, G.; Homer, C. Updating the 2001 national land cover database impervious surface products to 2006 using Landsat imagery change detection methods. Remote Sens. Environ. 2010, 114, 1676-1686.
53. Sannier, C.; Gallego, J.; Dahmer, J.; Smith, G.; Dufourmont, H.; Pennec, A. Validation of Copernicus high resolution layer on imperviousness degree for 2006, 2009 and 2012. In Proceedings of the International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Montpellier, France, 5-8 July 2016.
54. Maucha, G.; Büttner, G.; Kosztra, B. Final Draft European Validation of GMES FTS Soil Sealing Enhancement Data Project Manager; Technical Report; European Environment Agency: Copenhagen, Denmark, 2010.
55. Breiman, L. Random forests. Mach. Learn. 2001, 45, 5-32.