One-class classification for mapping a specific land-cover class: SVDD classification of fenland

IEEE Transactions on Geoscience and Remote Sensing

Volumn 45, Issue 4, 2007, Pages 1061-1072

  Sanchez Hernandez, Carolina ^b   Boyd, Doreen S ^c   Foody, Giles M ^a,c  

^a IEEE   (United Kingdom)

^b ORDNANCE SURVEY   (United Kingdom)

^c UNIVERSITY OF NOTTINGHAM   (United Kingdom)

Author keywords

Conservation; Fen; Image classification; Land cover; Support vector

Indexed keywords

LAND COVER; LANDSAT ENHANCED THEMATIC MAPPER PLUS IMAGERY; SUPPORT-VECTOR DATA DESCRIPTION (SVDD);

CONSERVATION; IMAGE CLASSIFICATION; LAND USE; MAPPING; REMOTE SENSING; SUPPORT VECTOR MACHINES;

CLASSIFICATION (OF INFORMATION);

EID: 33947711252     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2006.890414     Document Type: Article

References (64)

1. J. Cihlar, "Land cover mapping of large areas from satellites: Status and research priorities," Int. J. Remote Sens., vol. 21, no. 6/7, pp. 1093-1114, Apr. 2000.
2. D. S. Boyd and G. M. Foody, "Changing land cover," in Global Environmental Issues, F. Harris, Ed. Chichester, U.K.: Wiley, 2004, pp. 65-94.
3. B. Jeon and D. A. Landgrebe, "Partially supervised classification using weighted unsupervised clustering," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 2, pp. 1073-1079, Mar. 1999.
4. D. Fernandez-Prieto, "An iterative approach to partially supervised classification problems," Int. J. Remote Sens., vol. 23, no. 18, pp. 3887-3892, Sep. 2002.
5. Z. Liu, W. Shi, and Q. Qin, Partially Supervised Classification - Based on Weighted Unlabeled Samples Support Vector Machine, vol. 3584. Berlin, Germany: Springer-Verlag, 2005, pp. 118-129.
6. X. Song, G. Fan, and M. Rao, "Automated CRP mapping using non-parametric machine learning approaches," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 4, pp. 888-897, Apr. 2005.
7. X. M. Song, G. Cherian, and G. L. Fang, "A v-insensitive SVM approach for compliance monitoring of the Conservation Reserve Program," IEEE Geosci. Remote Sens. Lett., vol. 2, no. 2, pp. 99-103, Apr. 2005.
8. J. D. Hill, N. D. Strommen, C. M. Sakamoto, and S. K. Leduc, "LACIE - Application of meteorology for United States and foreign wheat assessment," J. Appl. Meteorol., vol. 19, no. 1, pp. 22-34, Jan. 1980.
9. E. W. Ramsey, G. A. Nelson, S. K. Sapkota, E. B. Seegar, and K. D. Martella, "Mapping Chinese tallow with color-infrared photography," Photogramm. Eng. Remote Sens., vol. 68, no. 3, pp. 251-255, Mar. 2002.
10. E. Underwood, S. Ustin, and D. DiPietro, "Mapping nonnative plants using hyperspectral imagery," Remote Sens. Environ., vol. 86, no. 2, pp. 150-161, Jul. 2003.
11. D. S. Boyd, C. Sanchez-Hernandez, and G. M. Foody, "Mapping a specific class for priority habitats monitoring from satellite sensor data," Int. J. Remote Sens., vol. 27, no. 13, pp. 2631-2644, Jul. 2006.
12. G. M. Foody, "Status of land cover classification accuracy assessment," Remote Sens. Environ., vol. 80, no. 1, pp. 185-201, Apr. 2002.
13. _, "Hard and soft classifications by a neural network with a non-exhaustively defined set of classes," Int. J. Remote Sens., vol. 23, no. 18, pp. 3853-3864, Sep. 2002.
14. R. M. Lark, "Components of accuracy of maps with special reference to discriminant analysis of remote sensor data," Int. J. Remote Sens., vol. 16, no. 8, pp. 1461-1480, 1995.
15. G. M. Foody, P. M. Atkinson, P. Gething, N. A. Ravenhill, and C. K. Kelly, "Identification of specific tree species in ancient semi-natural woodland from digital aerial sensor imagery," Ecol. Appl., vol. 15, no. 4, pp. 1233-1244, Aug. 2005.
16. G. M. Foody, A. Mathur, C. Sanchez-Hernandez, and D. S. Boyd, "Training set size requirements for the classification of a specific class," Remote Sens. Environ., vol. 104, no. 1, pp. 1-14, Sep. 2006.
17. P. Mantero, G. Moser, and S. B. Serpico, "Partially supervised classification of remote sensing images through SVM-based probability density estimation," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 559-570, Mar. 2005.
18. M. Moya, M. Koch, and L. Hostetler, "One-class classifier networks for target recognition applications," in Proc. World Congr. Neural Netw., 1993, pp. 797-801.
19. G. Ritter and M. Gallegos, "Outliers in statistical pattern recognition and an application to automatic chromosome classification," Pattern Recognit. Lett., vol. 18, no. 6, pp. 525-539, Jun. 1997.
20. C. Bishop, "Novelty detection and neural network validation," Proc. Inst. Electr. Eng. - Vision, Image Signal Process., vol. 141, no. 4, pp. 217-222, 1994.
21. N. Japkowicz, C. Myers, and M. Gluck, "A novelty detection approach to classification," in Proc. 14th Int. Joint Conf. Artif. Intell., 1995, pp. 518-523.
22. L. M. Manevitz and M. Yousef, "One-class SVMs for document classification," J. Mach. Learn. Res., vol. 2, no. 2, pp. 139-154, Mar. 2002.
23. D. M. J. Tax and R. P. W. Duin, "Uniform object generation for optimizing one-class classifiers," J. Mach. Learn. Res., vol. 2, no. 2, pp. 155-173, Mar. 2002.
24. C. Lai, D. M. J. Tax, R. P. W. Duin, E. Pekalska, and P. Paclik, On Combining One-Class Classifiers for Image Database Retrieval, vol. 2364. London, U.K.: Springer-Verlag, 2002, pp. 212-221.
25. Q. Guo, M. Kelly, and C. H. Graham, "Support vector machines for predicting distribution of sudden oak death in California," Ecol. Model., vol. 182, no. 1, pp. 75-90, Feb. 2005.
26. N. J. Pizzi, R. A. Vivanco, and R. L. Somorjai, "Evident: A functional magnetic resonance image analysis system," Artif. Intell. Med., vol. 21, no. 1-3, pp. 263-269, Jan.-Mar. 2001.
27. Y. Yang, T. Pierce, and J. Carbonell, "A study on retrospective and online event detection," in Proc. ACM SIGIR Conf. Res. and Develop. Inf. Retrieval, Melbourne, Australia, 1998, pp. 28-36.
28. G. Fumera, F. Roli, and G. Giacinto, "Reject option with multiple thresholds," Pattern Recognit., vol. 33, no. 12, pp. 2099-2101, Dec. 2000.
29. S. Roberts and L. Tarassenko, "A probabilistic resource allocating network for novelty detection," Neural Comput., vol. 6, no. 2, pp. 270-284, Mar. 1994.
30. D. Y. Yeung and C. Chow, "Parzen-window network intrusion detectors," in Proc. 16th ICPR, Quebec, QC, Canada, Aug. 11-15, 2002, vol. 4, pp. 385-388.
31. B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson, "Estimating the support of a high-dimensional distribution," Neural Comput., vol. 13, no. 7, pp. 1443-1471, Jul. 2001.
32. D. Tax and R. Duin, "Data domain description using support vectors," in Proc. Eur. Symp. Artif. Neural Netw., 1999, pp. 251-256.
33. D. M. J. Tax, "One-class classification," Ph.D. dissertation, Delft Univ. Technol., Delft, The Netherlands, 2001. unpublished.
34. V. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
35. C. Bishop, Neural Networks for Pattern Recognition. London, U.K.: Oxford Univ. Press, 1995.
36. T. Kohonen, Self-Organizing Maps. Heidelberg, Germany: Springer-Verlag, 1995.
37. N. Japkowicz, C. Myers, and M. Gluck, "A novelty detection approach to classification," in Proc. 14th Int. Joint Conf. Artif. Intell., 1995, pp. 518-523.
38. J. Hertz, A. Krogh, and R. Palmer, Introduction to the Theory of Neural Computation. Redwood City, CA: Addison-Wesley, 1991.
39. P. Baldi and K. Hornik, "Neural networks and principal component analysis: Learning from examples without local minima," Neural Netw., vol. 2, no. 1, pp. 53-58, 1989.
40. A. Ypma and R. Duin, "Support objects for domain approximation," in Proc. ICANN, Skovde, Sweden, 1998, pp. 719-724.
41. R. Duda and P. Hart, Pattern Classification and Scene Analysis. New York: Wiley, 1973.
42. C. Huang, L. S. Davis, and J. R. G. Townshend, "An assessment of support vector machines for land cover classification," Int. J. Remote Sens., vol. 23, no. 4, pp. 725-749, Feb. 2002.
43. G. Zhu and D. G. Blumberg, "Classification using ASTER data and SVM algorithms: The case study of Beer Sheva, Israel," Remote Sens. Environ., vol. 80, no. 2, pp. 233-240, May 2002.
44. M. Pal and P. M. Mather, "Support vector machines for classification in remote sensing," Int. J. Remote Sens., vol. 26, no. 5, pp. 1007-1011, Mar. 2005.
45. G. M. Foody and A. Mathur, "A relative evaluation of multi-class image classification by support vector machines," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 6, pp. 1335-1343, Jun. 2004.
46. D. M. J. Tax and R. P. W. Duin, "Support vector data description," Mach. Learn., vol. 54, no. 1, pp. 45-66, Jan. 2004.
47. V. Cherkassky and F. Mulier, Learning from Data - Concepts, Theory, and Methods. Hoboken, NJ: Wiley, 1998.
48. B. Schölkopf, R. Williamson, A. Smola, and J. Shawe-Taylor, "SV estimation of a distribution's support," in Proc. 13th Neural Inf. Process. Syst. Meeting, Denver, CO, 1999, pp. 582-588.
49. V. Vapnik, "An overview of statistical learning theory," IEEE Trans. Neural Netw., vol. 10, no. 5, pp. 988-999, Sep. 1999.
50. B. Boser, I. Guyon, and V. N. Vapnik, "A training algorithm for optimal margin classifiers," in Proc. 5th Annu. Workshop Comput. Learn. Theory, 1992, pp. 144-152.
51. M. A. Aizerman, E. M. Braverman, and L. I. Rozonoer, "Theoretical foundations of the potential function method in pattern recognition learning," Autom. Remote Control, vol. 25, no. 6, pp. 821-837, 1964.
52. R. K. Turner, S. Georgoiu, R. Brouwer, I. J. Bateman, and I. J. Langford, "Towards an integrated environmental assessment for wertland and catchment management," Geogr. J., vol. 169, no. 2, pp. 99-116, Jun. 2003.
53. Broads Authority, Broads Plan 2004. Norwich, U.K.: Witley Press, 2004.
54. F. Melgani and L. Bruzzone, "Classification of hyperspectral remote sensing images with support vector machines," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 8, pp. 1778-1790, Aug. 2004.
55. L. Guyon, J. Weston, S. Barnhill, and V. Vapnik, "Gene selection for cancer classification using support vector machines," Mach. Learn., vol. 46, no. 1-3, pp. 389-422, Jan.-Mar. 2002.
56. Y. Bazi and F. Melgani, "Towards an optimal SVM classification system for hypersepctral remote sensing images," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 11, pp. 3374-3385, Nov. 2006.
57. P. M. Mather, Computer Processing of Remotely-Sensed Images, 3rd ed. Chichester, U.K.: Wiley, 2004.
58. J. Piper, "Variability and bias in experimentally measured classifier error rates," Pattern Recognit. Lett., vol. 13, no. 10, pp. 685-692, Oct. 1992.
59. T. G. van Niel, T. R. McVicar, and B. Datt, "On the relationship between training sample size and data dimensionality of broadband multi-temporal classification," Remote Sens. Environ., vol. 98, no. 4, pp. 468-480, Oct. 2005.
60. G. M. Foody, "Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy," Photogramm. Eng. Remote Sens., vol. 70, no. 5, pp. 627-633, May 2004.
61. J. R. Anderson, E. E. Hardy, J. T. Roach, and R. E. Witmer, A Land Use and Land Cover Classification System for Use With Remote Sensor Data, Reston, VA: U.S. Geol. Surv., Professional Paper 964, 1976.
62. G. M. Foody and A. Mathur, "Toward intelligent training of supervised image classifications: Directing training data acquisition for SVM classification," Remote Sens. Environ., vol. 93, no. 1/2, pp. 107-117, Oct. 2004.
63. C. Sanchez-Hernandez, "Land cover mapping of one specific protected habitat under the requirements of the European Union Habitats Directive," Ph.D. dissertation, Kingston Univ., Kingston upon Thames, U.K., 2006. unpublished.
64. A. Kobler, S. Dzeroski, and I. Keramitoglu, "Habitat mapping using machine learning-extended kernel-based reclassification of an Ikonos satellite image," Ecol. Model., vol. 191, no. 1, pp. 83-95, Jan. 2006.