Structured output SVM for remote sensing image classification

Journal of Signal Processing Systems

Volumn 65, Issue 3, 2011, Pages 301-310

  Tuia, Devis ^a   Muñoz Marí, Jordi ^b   Kanevski, Mikhail ^a   Camps Valls, Gustavo ^b  

^a UNIVERSITY OF LAUSANNE   (Switzerland)

^b UNIVERSITY OF VALENCIA   (Spain)

Author keywords

Kernel methods; Land use classification; Structured output learning; Support vector machines

Indexed keywords

ACTIVE FIELD; CLASSIFICATION METHODS; HIERARCHICAL TREE; INPUT VALUES; KERNEL CLASSIFIERS; KERNEL FUNCTION; KERNEL METHODS; LAND USE CLASSIFICATION; LOSS FUNCTIONS; MISCLASSIFICATIONS; MULTISPECTRAL IMAGES; QUICKBIRD; REAL-LIFE PROBLEMS; REMOTE SENSING DATA; REMOTE SENSING IMAGE CLASSIFICATION; SATELLITE SENSORS; SPACE STRUCTURE; STRUCTURED OUTPUT LEARNING; SUPPORT VECTOR; URBAN AREAS; VERY HIGH SPATIAL RESOLUTIONS; VISUAL INSPECTION;

FORESTRY; IMAGE ANALYSIS; IMAGE CLASSIFICATION; IMAGE RECONSTRUCTION; REMOTE SENSING; SIGNAL PROCESSING; SPACE OPTICS; SUPPORT VECTOR MACHINES;

IMAGE SEGMENTATION;

ALGORITHMS; CLASSIFICATION; FORESTRY; IMAGE ANALYSIS; PROBLEM SOLVING; REMOTE SENSING;

EID: 81855227139     PISSN: 19398018     EISSN: 19398115     Source Type: Journal    
DOI: 10.1007/s11265-010-0483-8     Document Type: Article

References (22)

1. Altun, Y., Hofmann, T., & Tsochantaridis, I. (2007). Support vector learning for interdependent and structured output spaces. In G. Bakir, T. Hofmann, B. Schölkopf, A. J. Smola, & S. Vishwanathan (Eds.), Predicting structured data (pp. 85-105). Cambridge: MIT press.
2. Altun, Y., Tsochantaridis, I., & Hofmann, T. (2003). Hidden Markov support vector machines. In International conference on machine learning (ICML) (pp. 3-10).
3. BakIr, G., Hofmann, T., Schölkopf, B., Smola, A. J., & Vishwanathan, S. (2007). Predicting structured data. Cambridge: MIT Press.
4. Blaschko, M., & Lampert, C. (2008). Learning to localize objects with structured output regression. In D. Forsyth, P. Torr, & A. Zisserman (Eds.), Computer vision: ECCV 2008 (pp. 2-15). Heidelberg: Springer.
5. G Camps-Valls L Bruzzone 2005 Kernel-based methods for hyperspectral image classification IEEE Transactions on Geoscience and Remote Sensing 43 1351 1362 10.1109/TGRS.2005.846154 (Pubitemid 40811944)
6. Camps-Valls, G., & Bruzzone, L. (2009). Kernel methods in remote sensing image processing. Hoboken: Wiley.
7. G Camps-Valls L Gómez-Chova J Muñoz-Marí JL Rojo-Álvarez M Martínez-Ramón 2008 Kernel-based framework for multi-temporal and multi-source remote sensing data classification and change detection IEEE Transactions on Geoscience and Remote Sensing 46 6 1822 1835 10.1109/TGRS.2008.916201 (Pubitemid 351711220)
8. K Crammer Y Singer 2001 On the algorithmic implementation of multiclass problems Journal of Machine Learning Research 2 265 292
9. D Fasbender J Radoux P Bogaert 2008 Bayesian data fusion for adaptable image pansharpening IEEE Transactions on Geoscience and Remote Sensing 46 6 1847 1857 10.1109/TGRS.2008.917131 (Pubitemid 351711222)
10. GM Foody 2004 Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy Photogrammetric Engineering and Remote Sensing 50 5 627 633 (Pubitemid 39081774)
11. K Fukunaga RR Hayes 1989 Effects of sample size in classifier design IEEE Transactions on Pattern Analysis and Machine Intelligence 11 8 873 885 10.1109/34.31448
12. GF Hughes 1968 On the mean accuracy of statistical pattern recognition IEEE Transactions on Information Theory IT-14 1 55 63 10.1109/TIT.1968.1054102
13. T Joachims T Finley CNJ Yu 2009 Cutting-plane training of structural SVMs Machine Learning 77 27 59 10.1007/s10994-009-5108-8
14. Joachims, T., Galor, T., & Elber, R. (2005). Learning to align sequences: A maximum-margin approach. In B. Leimkuhler (Ed.), New algorithms for macromolecular simulation, LNCS (Vol. 49). Heidelberg: Springer.
15. F Melgani L Bruzzone 2004 Classification of hyperspectral remote sensing images with support vector machines IEEE Transactions on Geoscience and Remote Sensing 42 8 1778 1790 10.1109/TGRS.2004.831865
16. Richards, J. A., & Jia, X. (1999). Remote sensing digital image analysis. An introduction (3rd ed.). Berlin: Springer.
17. Schölkopf, B., & Smola, A. (2002). Learning with kernels. Cambridge: MIT Press.
18. Taskar, B., Guestrin, C., & Koller, D. (2003). Maximum-margin Markov networks. In Advances in neural information processing systems (NIPS).
19. I Tsochantaridis T Finley T Joachims T Hofmann Y Altun 2005 Large margin methods for structured and interdependent output variables Journal of Machine Learning Research 6 1453 1484 1222.68321
20. Tsochantaridis, I., Hofmann, T., Joachims, T., & Alturn, Y. (2004). Support vector machine learning for interdependent and structured output spaces. In Intl. conf. mach. learn.
21. D Tuia G Camps-Valls 2009 Semi-supervised remote sensing image classification with cluster kernels IEEE Geoscience and Remote Sensing Letter 6 1 224 228 10.1109/LGRS.2008.2010275
22. Zien, A., Brefeld, U., & Scheffer, T. (2007). Transductive support vector machines for structured variables. In Intl. conf. mach. learn.