1. [1] C. Lardeux, P-L. Frison, C. Tison, J-C. Souyris, B. Stoll, B. Fruneau, and J-P. Rudant, "Support vector machine for multifrequency SAR polarimetric data classification," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 12, pp. 4143–4152, 2009. [ DOI:10.1109/TGRS.2009.2023908] 2. [2] M. Salehi, M. R. Sahebi, and Y. Maghsoudi, "Improving the accuracy of urban land cover classification using Radarsat-2 PolSAR data," IEEE J. Selected Topics in Appl. Earth Observ. Remote Sens., vol. 7, no. 4, pp. 1394–1401, 2014. [ DOI:10.1109/JSTARS.2013.2273074] 3. [3] I. Khosravi, and M. Mohammad-Beigi, "Multiple classifier systems for hyperspectral remote sensing data classification," J. the Indian Society of Remote Sens., 42(2), pp. 423–428, 2014. [ DOI:10.1007/s12524-013-0327-7] 4. [4] Y. Maghsoudi, M. J. Collins, and D. G. Leckie, "Radarsat-2 polarimetric SAR data for Boreal forest classification using SVM and a wrapper feature selector," IEEE J. Selected Topics in Appl. Earth Observ. Remote Sens., vol. 6, no. 3, pp. 1531–1538, 2013. [ DOI:10.1109/JSTARS.2013.2259219] 5. [5] Y. Maghsoudi, M. J. Collins, and D. G. Leckie, "Polarimetric classification of Boreal forest using nonparametric feature selection and multiple classifiers," Int. J. Appl. Earth Observ. Geoinf., vol. 19, pp. 139–150, 2012. [ DOI:10.1016/j.jag.2012.04.015] 6. [6] S. Khalid, T. Khalil, and S. Nasreen, "A survey of feature selection and feature extraction techniques in machine learning," IEEE Science and Information Conference (SAI), pp. 372–378, 2014. [ DOI:10.1109/SAI.2014.6918213] 7. [7] R. Polikar, "Ensemble based systems in decision making," IEEE Circuits and Systems Mag., pp. 21–24, 2006. [ DOI:10.1109/MCAS.2006.1688199] 8. [8] J. A. Benediktsson, and I. Kanellopoulos, "Classification of multi-source and hyperspectral data based on decision fusion," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 3, pp. 1367–1377, 1999. [ DOI:10.1109/36.763301] 9. [9] J. Ham, Y. Chen, M. M. Crawford, and J. Gosh, "Investigation of the random forest framework for classification of hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 492–501, 2005. [ DOI:10.1109/TGRS.2004.842481] 10. [10] B. Waske, and J. A. Benediktsson, "Fusion of support vector machines for classification of multisensor data," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 12, pp. 3858–3866, 2007. [ DOI:10.1109/TGRS.2007.898446] 11. [11] X. Ceamanos, B. Waske, J. A. Benediktsson, J. Chanussot, M. Fauvel, and J. R. Sveinsson, "A classifier ensemble based on fusion of support vector machines for classifying hyperspectral data," Int. J. Image and Data Fusion, 1(4), pp. 293–307, 2010. [ DOI:10.1080/19479832.2010.485935] 12. [12] T. C. Malleswara Rao, G. Jai Sankar, and T. Roopesh Kumar, "A hierarchical hybrid SVM method for classification of remotely sensed data," J. the Indian Society of Remote Sens., 40(2), pp. 191–200, 2012. [ DOI:10.1007/s12524-011-0149-4] 13. [13] B. Bigdeli, F. Samadzadegan, and P. Reinartz, "A decision fusion method based on multiple support vector machine system for fusion of hyperspectral and LIDAR data," Int. J. Image and Data Fusion, 5(3), pp. 196–209, 2014. [ DOI:10.1080/19479832.2014.919964] 14. [14] C. Jiong, C. Yilun, and J. Yang, "A novel supervised classification scheme based on Adaboost for Polarimetric SAR," 9th International Conference on Signal Processing, 2008. ICSP 2008, pp. 2400–2403. 15. [15] R. Min, X. Yang, and Z. Zhao, "Application of AdaBoost in polarimetric SAR image classification," 2009 IEEE Radar Conference, pp. 1–4. 16. [16] B. Waske, and M. Braun, "Classifier ensemble for land cover mapping using multi-temporal SAR imagery," ISPRS J. Photogrammetry and Remote Sens., vol. 65, no. 5, pp. 450–457, 2009. [ DOI:10.1016/j.isprsjprs.2009.01.003] 17. [17] L. Zhang, X. Wang, M. Li, and W. M. Moon, "Classification of fully polarimetric SAR images based on ensemble learning and feature integration," Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International, pp. 2758–2761, 2014. 18. [18] Y. Wang, Y, Zhang, T. Zhuoc, and M. Liao, "Ensemble learning based on multi-features fusion and selection for polarimetric SAR image classification," Signal Processing (ICSP), 2014 12th International Conference on, pp. 734–737, 2014. 19. [19] S. Uhlmann and S. Kiranyaz, "Evaluation of classifiers for polarimetric SAR classification," Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International, pp. 775–778, 2013. [ DOI:10.1109/IGARSS.2013.6721272] 20. [20] L. Xu, J. Li, and A. Brenning, "A comparative study of different classification techniques for marine oil spill identification using RADARSAT-1 imagery," Remote Sens. Environ., 141, pp. 14–23, 2014. [ DOI:10.1016/j.rse.2013.10.012] 21. [21] P. Du, A. Samata, P. Gamba, X. Xiea, "Polarimetric SAR image classification by Boosted Multiple-Kernel Extreme Learning Machines with polarimetric and spatial features," Int. J. Remote Sens., 35:23, pp. 7978–7990. [ DOI:10.1080/2150704X.2014.978952] 22. [22] G. J. Briem, J. A. Benediktsson, and J. R. Sveinsson, "Multiple classifiers applied to multisource remote sensing data," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 10, pp. 2291–2292, 2002. [ DOI:10.1109/TGRS.2002.802476] 23. [23] L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. [ DOI:10.1023/A:1010933404324] 24. [24] J. A. Benediktsson, J. Chanussot, and M. Fauvel, "Multiple classifier systems in remote sensing: from basics to recent developments," MCS'07 Proceedings of the 7th international conference on multiple classifier systems. Heidelberg, Germany: Springer. [ DOI:10.1007/978-3-540-72523-7_50] 25. [25] J. S. Lee, M. R. Grunes, and G. de Grandi, "Polarimetric SAR speckle filtering and its implication for classification," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 5, pp. 2363–2373, 1999. [ DOI:10.1109/36.789635] 26. [26] RG. Congalton, and K. Green, "Assessing the accuracy of remotely sensed data principles and practices," 2nd edition, Taylor & Francis Group, 2009.
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