Abstract
This paper presents the intelligent techniques approach for flood monitoring using Synthetic Aperture Radar (SAR) satellite images. We applied artificial neural networks and Self-Organizing Kohonen Maps (SOMs), to SAR image segmentation and classification. Our approach was used to process data from different SAR satellite instruments (ERS-2/SAR, ENVISAT/ASAR, RADARSAT-1/2) for different flood events: Tisza River, Ukraine and Hungary in 2001; Huaihe River, China in 2007; Mekong River, Thailand and Laos in 2008; Koshi River, India and Nepal in 2008; Norman River, Australia in 2009; Lake Liambezi, Namibia in 2009; Mekong River, Laos in 2009. This approach was implemented using Sensor Web paradigm for integrated system for flood monitoring and management.
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References
Botts M, Percivall G, Reed C, Davidson J (2007) OGC Sensor Web Enablement: Overview and High Level Architecture (OGC 07-165). http://portal.opengeospatial.org/files/?artifact_id=25562. Accessed 10 Sept 2008
Chu X, Kobialka T, Durnota B, Buyya R (2006) Open sensor web architecture: core services. In: Proceedings of the 4th international conference on intelligent sensing and information processing (ICISIP), IEEE Press, Piscataway, NJ, pp 98–103
Corbley KP (1999) Radar imagery proves valuable in managing and analyzing floods red river flood demonstrates operational capabilities. Earth Observation Magazine 8(10)
Csornai G, Suba Zs, Nádor G et al (2004) Evaluation of a remote sensing based regional flood/waterlog and drought monitoring model utilising multi-source satellite data set including ENVISAT data. In: Proceedings of the 2004 ENVISAT & ERS Symposium, Salzburg, Austria
Cunjian Y, Yiming W, Siyuan W et al (2001) Extracting the flood extent from satellite SAR image with the support of topographic data. In: Proceedings of International Conference on Info-Tech and Info-Networks, vol 1, pp 87–92
Dellepiane S, De Laurentiis R, Giordano F (2004) Coastline extraction from SAR images and a method for the evaluation of the coastline precision. Pattern Recognit Lett 25:1461–1470
Haykin S (1999) Neural networks: a comprehensive foundation. Prentice Hall, Upper Saddle River, NJ
Horritt MS (1999) A statistical active contour model for SAR image segmentation. Image Vis Comput 17:213–224
Horritt MS (2006) A methodology for the validation of uncertain flood inundation models. J Hydrol 326:153–165
Kohonen T (1995) Self-organizing maps. Series in information sciences, vol 30. Springer, Heidelberg
Kussul N, Shelestov A, Skakun S (2009) Grid and sensor web technologies for environmental monitoring. Earth Sci Inf 2:37–51
Mandl D, Frye SW, Goldberg MD et al (2006) Sensor webs: where they are today and what are the future needs? In: Proceedings of the second IEEE workshop on dependability and security in sensor networks and systems (DSSNS 2006), Columbia, MD, pp 65–70
Martinez JM, Le Toan T (2007) Mapping of flood dynamics and spatial distribution of vegetation in the Amazon floodplain using multitemporal SAR data. Remote Sens Environ 108:209–223
Moe K, Smith S, Prescott G et al (2008) Sensor web technologies for NASA earth science. In: Proceedings of 2008 IEEE aerospace conference, pp 1–7 Big Sky, MT Digital Object Identifier: 10.1109/AERO.2008.4526458
Niedermeier A, Romaneeßen E, Lenher S (2000) Detection of coastline in SAR images using wavelet methods. IEEE Trans Geosci Remote Sens 38(5):2270–2281
Rees WG (2001) Physical principles of remote sensing. Cambridge University Press, Cambridge
Scheuren J-M, de le Polain, Waroux O, Below R et al (2008) Annual disaster statistical review – the number and trends 2007. Center for Research of the Epidemiology of Disasters (CRED). Jacoffsaet Printers, Melin, Belgium
Shah-Hosseini H, Safabakhsh RA (2003) TASOM-based algorithm for active contour modelling. Pattern Recognit Lett 24:1361–1373
Shelestov A, Kussul N, Skakun S (2006) Grid technologies in monitoring systems based on satellite data. J Autom Inf Sci 38(3):69–80
Acknowledgments
This work is supported by ESA CAT-1 project “Wide Area Grid Testbed for Flood Monitoring using Spaceborne SAR and Optical Data” (No. 4181), and by joint project of the Science and Technology Center in Ukraine (STCU) and the National Academy of Sciences of Ukraine (NASU), “Grid Technologies for Multi-Source Data Integration” (No. 4928).
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Kussul, N., Shelestov, A., Skakun, S. (2011). Flood Monitoring from SAR Data. In: Kogan, F., Powell, A., Fedorov, O. (eds) Use of Satellite and In-Situ Data to Improve Sustainability. NATO Science for Peace and Security Series C: Environmental Security. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9618-0_3
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DOI: https://doi.org/10.1007/978-90-481-9618-0_3
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