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Comparative Algorithms for Oil Spill Detection from Multi Mode RADARSAT-1 SAR Satellite Data

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Computational Science and Its Applications - ICCSA 2011 (ICCSA 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6783))

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Abstract

The main objective of this work is to develop comparative automatic detection procedures for oil spill pixels in multimode (Standard beam S2, Wide beam W1 and fine beam F1) RADARSAT-1 SAR satellite data that were acquired in the Malacca Straits using two algorithms namely, post supervised classification, and neural network (NN) for oil spill detection. The results show that NN is the best indicator for oil spill detection as it can discriminate oil spill from its surrounding such as look-alikes, sea surface and land. The receiver operator characteristic (ROC) is used to determine the accuracy of oil spill detection from RADARSAT-1 SAR data. The results show that oil spills, look- alkies, and sea surface roughness are perfectly discriminated with an area difference of 20% for oil spill, 35% look–alikes, 15% land and 30% for the sea roughness. The NN shows higher performance in automatic detection of oil spill in RADARSAT-1 SAR data as compared to Mahalanobis classification with standard deviation of 0.12. It can therefore be concluded that NN algorithm is an appropriate algorithm for oil spill automatic detection and W1 beam mode is appropriate for oil spill and look-alikes discrimination and detection.

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Authors and Affiliations

  1. Institute of Geospatial Science and Technology (INSTeG), Universiti Teknologi Malaysia, 81310 UTM, Skudai, Johor Bahru, Malaysia

    Maged Marghany & Mazlan Hashim

Authors
  1. Maged Marghany
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  2. Mazlan Hashim
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Editor information

Editors and Affiliations

  1. Basilicata University Potenza, L.I.S.U.T. - D.A.P.I.T., 10, Viale dell’Ateneo Lucano, 85100, Potenza, Italy

    Beniamino Murgante

  2. Department of Mathematics and Computer Science, University of Perugia, Via Vanvitelli, 1, 06123, Perugia, Italy

    Osvaldo Gervasi

  3. Department of Applied Mathematics and Computational Sciences, University of Cantabria,, Avda. de los Castros, s/n, Santander, C.P. 39005, Spain

    Andrés Iglesias

  4. School of Business Systems, Monash University, VIC 3800, Clayton, Australia

    David Taniar

  5. Department of Intelligent Informatics, Kyushu Sangyo University, 2-3-1 Matsukadai, 813-8503, Higashi-ku, Fukuoka, Japan

    Bernady O. Apduhan

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© 2011 Springer-Verlag Berlin Heidelberg

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Marghany, M., Hashim, M. (2011). Comparative Algorithms for Oil Spill Detection from Multi Mode RADARSAT-1 SAR Satellite Data. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds) Computational Science and Its Applications - ICCSA 2011. ICCSA 2011. Lecture Notes in Computer Science, vol 6783. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21887-3_25

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  • DOI: https://doi.org/10.1007/978-3-642-21887-3_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21886-6

  • Online ISBN: 978-3-642-21887-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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