An innovative support vector machine based method for contextual image classification

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 87, Issue , 2014, Pages 241-248

  Negri, Rogério Galante ^a   Dutra, Luciano Vieira ^a   Sant'Anna, Sidnei João Siqueira ^a  

^a INSTITUTO NACIONAL DE PESQUISAS ESPACIAIS   (Brazil)

Author keywords

Contextual information; Image classification; Support vector machine

Indexed keywords

GEOMETRY; IMAGE CLASSIFICATION;

ALOS/PALSAR; COMPUTATIONAL COSTS; CONTEXTUAL INFORMATION; MODE FILTERS; SVM CLASSIFICATION; TEST SITE; TRAINING PHASE;

SUPPORT VECTOR MACHINES;

ALOS; IMAGE CLASSIFICATION; INNOVATION; NUMERICAL MODEL; PALSAR; PIXEL; REMOTE SENSING; STOCHASTICITY;

AMAZON RIVER;

EID: 84890734733     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2013.11.004     Document Type: Short Survey

References (21)

1. Besag J. On the statistical analysis of dirty pictures. J. Roy. Stat. Soc. 1986, 259-302.
2. Besbes, O., Boujemaa, N., Belhadj, Z., 2009. Contextual classification of high-resolution satellite images. In: Proc. IEEE Symposium on Computational Intelligence for Image Processing, Nashville, 30-2 April, pp. 41-47.
3. Bovolo F., Bruzzone L. A context-sensitive technique based on support vector machines for image classification. Lect. Notes Comput. Sci. 2005, 3376:260-265.
4. Bovolo, F., Bruzzone, L., Marconcini, M., 2006. A novel context-sensitive SVM for classification of remote sensing images. In: Proc. IEEE International Geoscience and Remote Sensing Symposium, Denver, 30-6 August, pp. 2498-2501.
5. Bruzzone L., Persello C. A novel context-sensitive semisupervised SVM classifier robust to mislabeled training samples. IEEE Trans. Geosci. Remote Sens. 2009, 47(7):2142-2154.
6. Canny J. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 1986, 8(6):679-698.
7. Congalton R.G., Green K. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices 1999, Lewis, Boca Raton.
8. Cristianini N., Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods 2000, Cambridge University Press, New York.
9. Geman S., Geman D. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 1984, 6(6):721-741.
10. Gurney C.M., Townsend J.R.G. The use of contextual information in the classification of remotely sensed data. Photogramm. Eng. Remote Sens. 1983, 49:55-64.
11. Khedam, R., Belhadj-Aissa, A., Ranchin, T., 2003. Study of ICM parameters influence on images satellite contextual classification. In: Proc. Geoinformation for European Wide Integration, Prague, 4-6 June, pp. 79-85.
12. Marroquin J., Mitter S., Poggio T. Probabilistic solution of ill-posed problems in computational vision. J. Am. Stat. Assoc. 1987, 82:76-89.
13. Mather P.M. Computer Processing of Remotely-Sensed Images: An Introduction 2004, John Wiley & Sons, Chichester. third ed.
14. Schölkopf B., Smola A.J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond 2002, MIT Press, Cambridge.
15. Theodoridis S., Koutroumbas K. Pattern Recognition 2008, Academic Press, San Diego. forth ed.
16. Thorsten J. Making large-scale support vector machine learning practical. Advances in Kernel Methods 1999, 169-184. MIT Press, Cambridge, MA. B. Schölkopf, C.J.C. Burges, A.J. Smola (Eds.).
17. Vedaldi A., Zisserman A. Efficient additive kernels via explicit feature maps. IEEE Trans. Pattern Anal. Mach. Intell. 2010, 34(3):480-492.
18. Vempati, S., Vedaldi, A., Zisserman, A., Jawahar, C.V., 2010. Generalized RBF feature maps for efficient detection. In: Proc. Proceedings of the British Machine Vision Conference, Aberystwyth, 31-3 September, pp. 1-11.
19. Webb A.R. Statistical Pattern Recognition 2002, John Wiley & Sons, Chichester. second ed.
20. Weston, J., Watkins, C., 1998. Multi-Class Support Vector Machines. Technical Report, Egham, 20 May, University of London.
21. Zortea, M., De Martino, M., Serpico, S., 2007. A SVM ensemble approach for spectral-contextual classification of optical high spatial resolution imagery. In: Proc. IEEE International Geoscience and Remote Sensing Symposium. Barcelona, 23-28 July, pp. 1489-1492.