nach oben

Pattern Analysis and Applications

Erschienen in:

01.08.2010 | Theoretical Advances

One-class classification for oil spill detection

verfasst von: Attilio Gambardella, Giorgio Giacinto, Maurizio Migliaccio, Andrea Montali

Erschienen in: Pattern Analysis and Applications | Ausgabe 3/2010

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

SAR oil spill classification is a challenging topic, which is tackled by semi-empirical ad hoc approaches supported by very qualified experts. In all cases, the feature space is empirically defined, and two-class classification approaches are used. Although this approach allows achieving acceptable operational results, there is still room for improving both the comprehension of the physical phenomenon and the performance of classification techniques. In this paper, we propose a novel approach to oil-spill classification based on the paradigm of one-class classification. A classifier is trained using only examples of oil spills, instead of using oil spills and look-alikes, as in two-class approaches. Further, since the feature space is empirically defined, we also propose an objective technique to select the most powerful one that is suited for the oil-spill detection task at hand. Results on two case study datasets are reported to validate the proposed approach.

Vorheriger Artikel A system for 3D texture-based probabilistic object recognition and its applications

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Delilah HA (2002) Marine oil pollution: technologies and technologies for detection and early warning. European Commission JRC report, EUR 20231 EN

Fukuyama K, Shigenaka G, Van Blaricom GR (1998) Oil spill impacts and the biological basis for response guidance: an applied synthesis of research on tree subartic intertidal communities. NOAA Tech. Memorandum, NOS ORCA 125, Seattle

Brekke C, Solberg AHS (2005) Oil spill detection by satellite remote sensing. Remote Sens Environ 95:1–13

Migliaccio M, Tranfaglia M (2004) Oil spill observation by SAR: a review. In: Proceedings of US-Baltic international symposium on advances in marine environmental research monitoring and technologies. Klaipeda, Lithuania

Girard-Ardhuin F, Mercier G, Garello R (2003) Oil slick detection by SAR imagery: potential and limitation. In: Proceedings of ocean 2003, San Diego, USA, pp 164–169

Migliaccio M, Tranfaglia M, Ermakov SA (2005) A physical approach for the observation of oil spills in SAR images. IEEE J Ocean Eng 30(3):495–507CrossRef

Espedal HA, Wahl T (1999) Satellite SAR oil spill detection using wind history information. Int J Remote Sens 20(1):49–65CrossRef

Kubat M, Holte R, Matwin S (1998) Machine learning for the detection of oil spills in satellite radar images. Mach Learn 30(3):195–215CrossRef

Solberg AHS, Storvik G, Solberg R, Volden E (1999) Automatic detection of oil spills in ERS SAR images. IEEE Trans Geosci Remote Sens 37(4):1916–1924CrossRef

10.

Del Frate F, Petrocchi A, Lichtenegger J, Calabresi G (2000) Neural networks for oil spill detection using ERS-SAR data. IEEE Trans Geosci Remote Sens 38(5):2282–2287CrossRef

11.

Fiscella B, Giancaspro A, Nirchio F, Pavese P, Trivero P (2000) Oil spill detection using marine SAR images. Int J Remote Sens 21(18):3561–3566CrossRef

12.

Marghany M (2001) RADARSAT automatic algorithms for detecting oil spill pollution. Int J Appl Earth Observ Geoinf 3(2):191–196CrossRef

13.

Nirchio F, Sorgente M, Giancaspro A, Biamino W, Parisato E, Ravera R, Trivero P (2005) Automatic detection of oil spills from SAR images. Int J Remote Sens 26(3):1157–1174CrossRef

14.

Girard-Ardhuin F, Mercier G, Collard F, Garello R (2005) Operational oil-slick characterization by SAR imagery and synergistic data. IEEE J Ocean Eng 30(3):487–495CrossRef

15.

Keramitsloglou I, Cartalis C, Kiranoudis CT (2006) Automatic identification of oil spills on satellite images. In: Environmental modelling & software, vol 21, issue 5. Elsevier, Amsterdam, pp 640–652

16.

Topouzelis K, Karathanassai V, Pavlakis P, Rokos D (2003) Oil spill detection: SAR multi-scale segmentation & object features evaluation. In: Proceedings of SPIE, vol 4880, pp 77–87

17.

Stathakis D, Topouzelis K, Karathanassi V (2006) Large-scale feature selection using evolved neural networks. In: Proceedings of SPIE, vol 6365

18.

Solberg AHS, Brekke C, Husoy PO (2007) Oil spill detection in Radarsat and Envisat SAR images. IEEE Trans Geosci Remote Sens 45(3):746–755CrossRef

19.

Tax DMJ (2001) One class classification. PhD thesis, Delft University of Technology, The Netherlands

20.

Fukunaga K (1990) Introduction to statistical pattern recognition, 2nd edn. Academic Press, San DiegoMATH

21.

Duda R, Hart P, Stork D (2001) Pattern classification, 2nd edn. Wiley, New YorkMATH

22.

Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4–37CrossRef

23.

Jain AK, Zongker D (1997) Feature selection: evaluation, application, and small sample performance. IEEE Trans Pattern Anal Mach Intell 19(2):153–158CrossRef

24.

Galicia Mission Data Analysis Report, Telaer Consortium (2003) Available at: http://www.telaer.it/it/MissioneGaliziaContent.htm

25.

Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning. Springer, Berlin

26.

Giacinto G, Perdisci R, Del Rio M, Roli F (2008) Intrusion detection in computer networks by a modular ensemble of one-class classifiers. Inf Fusion 9(1):69–82CrossRef

27.

Perdisci R, Guofei G, Lee W (2006) Using an ensemble of one-class SVM classifiers to Harden payload-based anomaly detection systems. In: ICDM’06, pp 488–498

28.

Yunqiang C, Xiang SZ, Huang TS (2001) One-class SVM for learning in image retrieval. In: Proceedings of the 2001 international conference on image processing, vol 1, pp 34–37

29.

Zhang C, Chen X, Chen M, Chen S-C, Shyu M-L (2005) A Multiple instance learning approach for content based image retrieval using one-class support vector machine. In: IEEE international conference on multimedia and expo, ICME 2005, pp 1142–1145

30.

Wang D, Yeung DS, Tsang ECC (2006) Structured one-class classification systems. IEEE Trans Syst Man Cybern B Cybern 36(6):1283–1295CrossRef

31.

Sanchez-Hernandez C, Boyd DS, Foody GM (2007) One-class classification for mapping a specific land-cover class: SVDD classification of Fenland. IEEE Trans Geosci Remote Sens 45(4):1061–1073CrossRef

32.

Scholkopf B, Platt J, Shawe-Taylor J, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13(7):1443–1471CrossRef

33.

Vapnik V (1998) Statistical learning theory. Wiley

34.

Jin Huang, Charles X. Ling (2005) Using AUC and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng 17(3):299–310

35.

Duin RPW, Juszczak P, Paclik P, Pekalska E, de Ridder D, Tax DMJ (2004) PRTools4A Matlab toolbox for pattern recognition. Delft University of Technology, The Netherlands

Titel: One-class classification for oil spill detection
verfasst von: Attilio Gambardella
Giorgio Giacinto
Maurizio Migliaccio
Andrea Montali
Publikationsdatum: 01.08.2010
Verlag: Springer-Verlag
Erschienen in: Pattern Analysis and Applications / Ausgabe 3/2010
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI: https://doi.org/10.1007/s10044-009-0164-z

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 3/2010

Domain-independent feature extraction for multi-classification using multi-objective genetic programming

A system for 3D texture-based probabilistic object recognition and its applications

A comparative study on feature extraction for fingerprint classification and performance improvements using rank-level fusion

Symbol spotting in vectorized technical drawings through a lookup table of region strings

Cancellable biometrics and user-dependent multi-state discretization in BioHash

Combined invariants to blur and rotation using Zernike moment descriptors

Premium Partner