Land Cover Classification of Quad-Polarimetric RADARSAT-2 SAR Image Based on Modified Subspace Method

REFERENCES

• Bagan, H., Yasuoka, Y., Endo, T., Wang, X., and Feng, Z. 2008. “Classification of airborne hyperspectral data based on the average learning subspace method.” IEEE Geoscience and Remote Sensing Letters, Vol. 5(No. 3): pp. 368–372. doi:10.1109/LGRS.2008.915941.
   Web of Science ®Google Scholar
• Bagan, H., Takeuchi, W., Kinoshita, T., Bao, Y., and Yamagata, Y. 2010. “Land cover classification and change analysis in the Horqin sandy land from 1975 to 2007.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 3(No. 2): pp. 168–177. doi:10.1109/JSTARS.2010.2046627.
   Web of Science ®Google Scholar
• Cloude, S.R., and Pottier, E. 1996. “A review of target decomposition theorems in radar polarimetry.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 34(No. 2): pp. 498–518. doi: 10.1109/36.485127.
   Web of Science ®Google Scholar
• Cloude, S.R., and Pottier, E. 1997. “An entropy based classification scheme for land application of polarimetric SAR.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 35(No. 1): pp. 68–78. doi: 10.1109/36.551935.
   Web of Science ®Google Scholar
• Ferro-Famil, L., Pottier, E., and Lee, J.S. 2001. “Unsupervised classification of multifrequency and fully polarimetric SAR images based on the H/A/alpha-Wishart classifier.” IEEE Transactions on Geoscience and Remote Sensin., Vol. 39(No. 11): pp. 2332–2342. doi: 10.1109/36.964969.
   Web of Science ®Google Scholar
• Freeman, A., and Durden, S.L. 1998. “A three-component scattering model for polarimetric SAR data.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 36(No. 3): pp. 963–973. doi: 10.1109/36.673687.
   Web of Science ®Google Scholar
• Haralick, R.M., Shanmugam, K., and Dinstein, I. 1973. “Textural features for image classification.” IEEE Transactions on Man and Cybernetics, Vol. SMC-3(No. 6): pp. 610–621. doi: 10.1109/TSMC.1973.4309314.
   Web of Science ®Google Scholar
• Holm, W.A., and Barnes, R.M. 1988. “On radar polarization mixed state decomposition theorems.” In Proceedings of the IEEE National Radar Conference, pp. 248–254. IEEE.
   Google Scholar
• Huynen, J.R. 1970. Phenomenological Theory of Radar Targets, PhD Thesis, Technical University, Delft. Rotterdam, The Netherlands: Bronder-Offset.
   Google Scholar
• Iijima, T., Genchi, H., and Mori, K. 1973. “A theory of character recognition by pattern matching method.” In Learning Systems and Intelligent Robots, pp. 437–450. Berlin: Springer. doi: 10.1007/978-1-4684-2106-4_22.
   Google Scholar
• Krogager, E. 1990. “New decomposition of the radar target scattering matrix.” Electronics Letters, Vol. 26(No. 18):, pp. 1525–1527. doi: 10.1049/el:19900979.
   Web of Science ®Google Scholar
• Laaksonen, J., and Oja, E. 1996. “Subspace dimension selection and averaged learning subspace method in handwritten digit classification.” Artificial Neural Networks—ICANN 96, LNCS 1112: pp. 227–232. Berlin: Springer. doi: 10.1007/3-540-61510-5_41.
   Google Scholar
• Lee, J.S., Grunes, M.R., and Kwok, R. 1994. “Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution.” International Journal of Remote Sensing, Vol. 15(No. 11): pp. 2299–2311. doi: 10.1080/01431169408954244.
   Web of Science ®Google Scholar
• Lee, J.S., Grunes, M.R., Ainsworth, T.L., Du, L.J., Schuler, D.L., and Cloude, S.R. 1999. “Unsupervised classification using polarimetric decomposition and the complex Wishart classifier.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 37(No. 51): pp. 2249–2258. doi: 10.1109/36.789621.
   Google Scholar
• Lee, J.S., Wen, J.H., Chen, K.S., Chen, A.J., and Ainsworth, T.L. 2009. “Improved sigma filter for speckle filtering of SAR data.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 47(No. 1): pp. 202–213. doi: 10.1109/TGRS.2008.2002881.
   Web of Science ®Google Scholar
• Matsunaga, T. 2000. “A study of document filtering using the subspace method of pattern recognition.” Systems and Computers in Japan, Vol. 31(No. 1): pp. 48–58. doi: 10.1002/(SICI)1520-684X(200001)31:1<48::AID-SCJ6>3.0.CO;2-F.
   Google Scholar
• Oja, E. 1983. Subspace Methods of Pattern Recognition. Letchworth, UK: Research Studies Press.
   Google Scholar
• Papathanassiou, K.P., and Cloude, S.R. 2001. “Single-baseline polarimetric SAR interferometry.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 39(No. 11): pp. 2352–2363. doi: 10.1109/36.964971.
   Web of Science ®Google Scholar
• Pottier, E., Ferro-Famil, L., Allain, S., Cloude, S.R., Hajnsek, I., Papathanassiou, K., Moreira, A., Williams, M., Minchella, A., Lavalle, M., and Desnos, Y.L. 2009. “Overview of the PolSARpro v4.0: The open source toolbox for polarimetric and interferometric polarimetric SAR data processing.” IEEE International Geoscience Remote Sensing Symposium (IGARSS), Vol. 4: pp. 936–939. doi: 10.1109/IGARSS.2009.5417532.
   Google Scholar
• Shimoni, M., Borghys, D., Heremans, R., Perneel, C., and Acheroy, M. 2009. “Fusion of PolSAR and PolInSAR data for land cover classification.” International Journal of Applied Earth Observation and Geoinformation, Vol. 11(No. 3): pp. 169–180. doi: 10.1016/j.jag.2009.01.004.
   Web of Science ®Google Scholar
• Watanabe, S., Lambert, P.F., and Kulikowski, C.A. 1967. “Evaluation and selection of variables in pattern recognition.” In Computer and Information Sciences II, edited by J. Tou, 91–122. New York, NY: Academic.
   Google Scholar
• Werner, C., Wegmüler, U., Strozzi, T., and Wiesmann, A. 2000. “Gamma SAR and interferometric processing software.” Paper presented at the Processing ERS-ENVISAT Symposium, Gothenburg, Sweden, October 16–20, 2000.
   Google Scholar
• Yamaguchi, Y., Moriyama, T., Ishido, M., and Yamada, H. 2005. “Four-component scattering models for polarimetric SAR image decomposition.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 43(No. 8): pp. 1699–1706. doi: 10.1109/TGRS.2005.852084.
   Web of Science ®Google Scholar
• Yang, S.Y., Wang, M., and Jiao, L.C. 2009. “Radar target recognition using contourlet packet transform and neural network approach.” Signal Processing, Vol. 89(No. 4): pp. 394–409. doi: 10.1016/j.sigpro.2008.09.015.
   Web of Science ®Google Scholar