Abstract
Geographic Information Systems (GIS) have gained popularity in recent years because they provide spatial data management and access through the Web. This article gives a detailed description of a tool that offers an integrated framework for the detection and localization of marine spills using remote sensing, GIS, and cloud computing. Advanced segmentation algorithms are presented in order to isolate dark areas in SAR images, including fuzzy clustering and wavelets. In addition, cloud computing is used for scaling up the algorithms and providing communication between users.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
La tragedia del prestige, evolución de la marea negra (2009), http://www.lavozdegalicia.es/albumes/index.jsp?album=20021121133541
Brekke, C., Solberg, A.J.S.: Oil spill detection by satellite remote sensing. Remote Sensing of Environment 95(1), 1–13 (2005)
2004 IEEE International: Algorithms for oil spill detection in Radarsat and ENVISAT SAR images. Geoscience and Remote Sensing Symposium (2004)
Indregard, M., Solberg, A., Clayton, P.: D2-report on benchmarking oil spill recognition approaches and best practice. Tech. Rep. Archive No. 04-10225-A-Doc, Con- tract No: EVK2-CT-2003-00177, European Comission (2004)
Lee, J.: Speckle analysis and smoothing of synthetic aperture radar images. Computer Graphics and Image Processing 17, 24–32 (1981)
Seventh IEEE International Symposium on Cluster Computing and the Grid: Bridging the High Performance Computing Gap: the OurGrid Experience. IEEE Computer Society (2007)
Sotomayor, B., Childers, L.: Globus Toolkit 4: Programming Java Services. Morgan Kaufman, Elsevier (2006)
White, T.: Hadoop: The Definitive Guide, 2nd edn. O’Really Media|Yahoo Press (2010)
Owen, S., Anil, R.: Mahout in action (MEAP). Manning Publications Co. (2011)
Lin, J., Dyer, C.: Data-Intensive Text Processing with MapReduce, 2nd edn. Morgan and Claypool Publishers (2010)
Kresse, W., Fadaie, K.: Iso standards for geographic information. Springer (2004)
Geoserver user’s manual: Cql tutorial (2010), http://docs.geoserver.org/1.7.x/user/tutorials/cql
Nuñez, J., Llacer, J.: Astronomical image segmentation by selforganizing neural networks and wavelets. Neural Networks (16), 411–417 (2003)
Crosslin, R.L., Eddins, W.R., Sutherland, D.E.: Digital Image Processing, pp. 201–208, 443–457. Prentice Hall (1992)
Zhang, D.Q., Chen, S.C.: A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artificial Intelligence in Medicine 32 (2004)
Zhang, H., Berg, A., Maire, M., Malik, J.: Svm-knn: Discriminative nearest neighbor classification for visual category recognition. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fustes, D., Cantorna, D., Dafonte, C., Iglesias, A., Manteiga, M., Arcay, B. (2012). Cloud Integrated Web Platform for Marine Monitoring Using GIS and Remote Sensing: Application to Oil Spill Detection through SAR Images. In: Bravo, J., López-de-Ipiña, D., Moya, F. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2012. Lecture Notes in Computer Science, vol 7656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35377-2_62
Download citation
DOI: https://doi.org/10.1007/978-3-642-35377-2_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35376-5
Online ISBN: 978-3-642-35377-2
eBook Packages: Computer ScienceComputer Science (R0)