A novel decision fusion method based on weights of evidence model

International Journal of Image and Data Fusion

Volumn 5, Issue 2, 2014, Pages 123-137

  Song, Benqin ^a   Li, Peijun ^a  

^a PEKING UNIVERSITY   (China)

Author keywords

classification; decision fusion; multi sensor data; weights of evidence

Indexed keywords

EID: 84899718589     PISSN: 19479832     EISSN: 19479824     Source Type: Journal    
DOI: 10.1080/19479832.2014.894143     Document Type: Article

References (34)

1. Benediktsson, J.A. and Kanellopoulos, I., 1999. Classification of multisource and hyperspectral data based on decision fusion. IEEE Transactions on Geoscience and Remote Sensing, 37 (3), 1367-1377. doi:10.1109/36.763301
2. Benediktsson, J.A. and Swain, P.H., 1992. Consensus theoretic classification methods. IEEE Transactions on Systems, Man and Cybernetics, 22 (4), 688-704. doi:10.1109/21.156582
3. Benediktsson, J.A., Sveinsson, J.R., and Swain, P.H., 1997. Hybrid consensus theoretic classification. IEEE Transactions on Geoscience and Remote Sensing, 35 (4), 833-843. doi:10.1109/36.602526
4. Bonham-Carter, G.F., Agterberg, F.P., and Wright, D.F., 1989. Weights of evidence modelling: a new approach to mapping mineral potential (Paper 89-9). In: F.P. Agterberg and G.F. Bonham-Carter, eds. Statistical applications in the earth sciences. Ottawa, ON: Geological Survey of Canada, 171-183.
5. Bovolo, F., Bruzzone, L., and Carlin, L., 2010. A novel technique for subpixel image classification based on support vector machine. IEEE Transactions on Image Processing, 19 (11), 2983-2999. doi:10.1109/TIP.2010.2051632
6. Breiman, L., 1996. Bagging predictors. Machine Learning, 24, 123-140. doi:10.1007/BF00058655
7. Briem, G.J., Benediktsson, J.A., and Sveinsson, J.R., 2002. Multiple classifiers applied to multisource remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 40 (10), 2291-2299. doi:10.1109/TGRS.2002.802476
8. Bruzzone, L., Chi, M., and Marconcini, M., 2006. A novel transductive SVM for semisupervised classification of remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing, 44 (11), 3363-3373. doi:10.1109/TGRS.2006.877950
9. Chen, B. and Varshney, P.K., 2002. A Bayesian sampling approach to decision fusion using hierarchical models. IEEE Transactions on Signal Processing, 50 (8), 1809-1818. doi:10.1109/TSP.2002.800419
10. Cheng, Q. and Agterberg, F.P., 1999. Fuzzy weights of evidence method and its application in mineral potential mapping. Natural Resources Research, 8 (1), 27-35. doi:10.1023/A:1021677510649
11. Desclee, B., et al., 2013. Multi-sensor monitoring system for forest cover change assessment in Central Africa. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6 (1), 110-120. doi:10.1109/JSTARS.2013.2240263
12. Dilts, T.E., Sibold, J.S., and Biondi, F., 2009. A weights-of-evidence model for mapping the probability of fire occurrence in Lincoln County, Nevada. Annals of the Association of American Geographers, 99 (4), 712-727. doi:10.1080/00045600903066540
13. Doan, H.T.X. and Foody, G.M., 2007. Increasing soft classification accuracy through the use of an ensemble of classifiers. International Journal of Remote Sensing, 28, 4609-4623. doi:10.1080/01431160701244872
14. Dobrowski, S.Z., et al., 2006. Improving image derived vegetation maps with regression based distribution modeling. Ecological Modeling, 192 (1-2), 126-142.
15. Fasbender, D., Radoux, J., and Bogaert, P., 2008. Bayesian data fusion for adaptable image pan sharpening. IEEE Transactions on Geoscience and Remote Sensing, 46 (6), 1847-1857. doi:10.1109/TGRS.2008.917131
16. Fauvel, M., Chanussot, J., and Benediktsson, J.A., 2006. Decision fusion for the classification of urban remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 44 (10), 2828-2838. doi:10.1109/TGRS.2006.876708
17. Felicsimo, A.M., et al., 2002. Modeling the potential distribution of forests with a GIS. Photogrammetric Engineering and Remote Sensing, 68, 455-461.
18. Foody, G.M., Boyd, D.S., and Sanchez-Hernandez, C., 2007. Mapping a specific class with an ensemble of classifiers. International Journal of Remote Sensing, 28 (8), 1733-1746. doi:10.1080/01431160600962566
19. Good, I.J., 1985. Weight of evidence: a brief survey. In: J.M. Bernardo, et al., eds. Bayesian statistic 2. Amsterdam: Elsevier, 249-269.
20. Jimenez, L.O., Morales-Morell, A., and Creus, A., 1999. Classification of hyper dimensional data based on feature and decision fusion approaches using projection pursuit, majority voting, and neural networks. IEEE Transactions on Geoscience and Remote Sensing, 37 (3), 1360-1366. doi:10.1109/36.763300
21. Keerthi, S.S. and Lin, C.J., 2003. Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Computation, 15 (7), 1667-1689. doi:10.1162/089976603321891855
22. Longbotham, N., et al., 2012. Multi-modal change detection, application to the detection of flooded areas: outcome of the 2009-2010 data fusion contest. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5 (1), 331-342. doi:10.1109/JSTARS.2011.2179638
23. Li, P. and Huang, Y., 2005. Land cover classification of remotely sensed image with hierarchical iterative method. Progress in Natural Science, 15 (5), 442-447. doi:10.1080/10020070512331342370
24. Platt, J.C. 2000. Probabilities for SV machines. Cambridge, MA: MIT Press, 61-74.
25. Plaza, A., et al., 2009. Recent advances in techniques for hyperspectral image processing. Remote Sensing of Environment, 113, S110-S122. doi:10.1016/j.rse.2007.07.028
26. Romero-Calcerrada, R. and Luque, S., 2006. Habitat quality assessment using weights of evidence based GIS modelling: the case of Picoides tridactylus as species indicator of the biodiversity value of the Finnish forest. Ecological Modeling, 196 (1-2), 62-76.
27. Rosen, P.A., et al., 2004. Updated repeat orbit interferometry package released. Eos, Transactions American Geophysical Union, 85 (5), 47-47. doi:10.1029/2004EO050004
28. Schapire, R.E., 1999. A brief introduction to boosting. In: Proceedings of the 16th international joint conference on Artificial intelligence, 31 July-6 August 1999, Stockholm. San Francisco, CA: Morgan Kaufmann, 2, 1401-1406.
29. Simone, G., et al., 2002. Image fusion techniques for remote sensing applications. Information Fusion, 3, 3-15. doi:10.1016/S1566-2535(01)00056-2
30. Solberg, A.H.S., Jain, A.K., and Taxt, T., 1994. Multisource classification of remotely sensed data: fusion of Landsat TM and SAR images. IEEE Transactions on Geoscience and Remote Sensing, 32 (4), 768-778. doi:10.1109/36.298006
31. Spiegelhalter, D.J. and Knill-Jones, R.P., 1984. Statistical and knowledge-based approaches to clinical decision-support systems, with an application in gastroenterology. Journal of the Royal Statistical Society, Series A (General), 147 (1), 35-77. doi:10.2307/2981737
32. Waske, B. and Benediktsson, J.A., 2007. Fusion of support vector machines for classification of multisensor data. IEEE Transactions on Geoscience and Remote Sensing, 45 (12), 3858-3866. doi:10.1109/TGRS.2007.898446
33. Waske, B. and van der Linden, S., 2008. Classifying multilevel imagery from SAR and optical sensors by decision fusion. IEEE Transactions on Geoscience and Remote Sensing, 46 (5), 1457-1466. doi:10.1109/TGRS.2008.916089
34. Wu, T.F., Lin, C.J., and Weng, R.C., 2004. Probability estimates for multi-class classification by pairwise coupling. Journal of Machine Learning Research, 5, 975-1005.