基于Cloude-Pottier分解的全极化SAR数据非监督分类的算法和实验研究
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摘要
全极化SAR数据的地物分类是遥感领域中雷达极化的最重要的应用之一。全极化SAR数据的优势在于，利用极化目标分解方法可以识别地表覆盖物的散射机制，该散射机制对所有的全极化SAR数据均是稳定的，因此，它可以实现非监督的分类方法，即不需要地表覆盖物类型的先验知识数据库。
本文开展了全极化SAR数据非监督分类的算法和实验研究。基于目前全极化SAR数据的非监督分类研究最流行的方法，本文采用将目标分解算法和统计模型的聚类方法结合起来实现全极化SAR数据的非监督分类，其研究内容主要是开展目标分解方法研究提取全极化SAR数据有关散射机制的信息，并结合该散射机制的信息和统计模式识别的方法开展全极化SAR数据非监督分类算法的研究。就目标分解算法而言，本文提出对目前最流行的Cloude-Pottier目标分解方法的改进，将回波功率参数引入到Cloude-Pottier分解中，并给出了Cloude-Pottier分解结果的直观表达方式。就非监督分类算法而言，本文首先提出了基于复Wishart分布和最大似然估计算法的Wishart SPAN／H／α分类算法，通过引入回波功率参数在一定程度上避免分类初始化的错误，提高分类器的性能；然后，考虑到Wishart SPAN／H／α分类算法初始化参数所表征的散射机制的信息不完全，本文进一步提出了采用SPAN／H／α／A四个特征参数进行初始化的Wishart SPAN／H／α／A分类算法作为Wishart SPAN／H／α分类算法的改进；由于Wishart SPAN／H／α／A分类算法初始化后的类数较多，本文又进一步提出了基于Wishart检验统计的区域合并算法来减少分类的类别数，获得有效的分类结果。Wishart SPAN／H／α／A分类算法使用预先给定的类别数进行区域合并，这一类别数并不一定是最优的。事实上，目前全极化SAR研究提出的分类算法均采用固定的类别数，关于全极化SAR数据类别数估计的研究还没有开展。本文通过引入模式识别中的交叉验证算法，提出了基于Wishart分布的交叉验证对数似然的概念，用该似然函数进行全极化SAR数据的类别数估计，根据全极化SAR数据的内部结构直接获得最优的类别数。基于该似然函数，本文又提出了一种新的全极化SAR数据非监督分类算法，带类别数估计的Wishart SPAN／H／α／A分类算法，该算法自动地根据全极化数据估计最优的类别数，并采用该最优类别数作为分类结果的类别数，消除分类类别数与数据内部结构不匹配的情况下可能导致的过拟合或欠拟合现象。另外，对于分类器定量评估方面，本文提出了采用交叉验证算法进行全极化SAR数据非监督分类器评估的方法，并尝试利用交叉验证算法实现全极化SAR数据非监督分类结果的评估。
The land cover classification is one of the most important, applications inpolarimetry remote sensing. The main advantage of fully polarimetric SAR datais that it can use target decomposition algorithm to extract the informationof the scattering mechanisms, which are not data specific and can be used fortarget identification. Thus using fully polarimetric SAR data we can achieve anunsupervised classification without ground truth information.
In this paper, an improvement for the Cloude-Pottier decomposition is givenfor analysis. We use the backscattering power information to improve the per-formance of the Cloude-Pottier decomposition and the transform algorithm isalso given to represent directly the decomposition results. Several unsupervisedclassification algorithms are also proposed to improve the classification perfor-mance step by step. Firstly, the Wishart SPAN/H/αclassification is proposed tointroduce the backscattering power information to the Wishart H/α/A classifi-cation. Then in order to include the scattering information within the parameterA, the Wishart SPAN/H/α/A classification is given to use the four parametersSPAN/H/α/A for initialization, and the Wishart test statistic is applied to re-duce the number of classes. The Wishart SPAN/H/α/A classification uses apredefined number of classes to perform the classification scheme. In fact, all theunsupervised classifications for fully polarimetric SAR data proposed nowadaysuse fixed number of clusters to classify the data. It is more reasonable to deter-mine the number of clusters directly from data analysis. According to the CrossValidation theorem in pattern recognition, We proposed the Cross-Validationlog-likelihood based on the complex Wishart distribution to estimate the opti-mal number of classes to reveal the inner structure of fully polarimetric SARdata. Using the Cross-Validation log-likelihood, a new unsupervised classifica-tion method, the Wishart SPAN/H/α/A classification with an adaptive numberof classes, is also given for interpretation. The number of classes is automati-cally optimized to avoid overfitting and underfitting for the inner structure of fully polarimetric SAR data. Moreover, since the Cross Validation algorithm is aquantitative estimation of the classification performance, it also has the potentialcapability to perform the validation of unsupervised classification.
本文开展了全极化SAR数据非监督分类的算法和实验研究。基于目前全极化SAR数据的非监督分类研究最流行的方法，本文采用将目标分解算法和统计模型的聚类方法结合起来实现全极化SAR数据的非监督分类，其研究内容主要是开展目标分解方法研究提取全极化SAR数据有关散射机制的信息，并结合该散射机制的信息和统计模式识别的方法开展全极化SAR数据非监督分类算法的研究。就目标分解算法而言，本文提出对目前最流行的Cloude-Pottier目标分解方法的改进，将回波功率参数引入到Cloude-Pottier分解中，并给出了Cloude-Pottier分解结果的直观表达方式。就非监督分类算法而言，本文首先提出了基于复Wishart分布和最大似然估计算法的Wishart SPAN／H／α分类算法，通过引入回波功率参数在一定程度上避免分类初始化的错误，提高分类器的性能；然后，考虑到Wishart SPAN／H／α分类算法初始化参数所表征的散射机制的信息不完全，本文进一步提出了采用SPAN／H／α／A四个特征参数进行初始化的Wishart SPAN／H／α／A分类算法作为Wishart SPAN／H／α分类算法的改进；由于Wishart SPAN／H／α／A分类算法初始化后的类数较多，本文又进一步提出了基于Wishart检验统计的区域合并算法来减少分类的类别数，获得有效的分类结果。Wishart SPAN／H／α／A分类算法使用预先给定的类别数进行区域合并，这一类别数并不一定是最优的。事实上，目前全极化SAR研究提出的分类算法均采用固定的类别数，关于全极化SAR数据类别数估计的研究还没有开展。本文通过引入模式识别中的交叉验证算法，提出了基于Wishart分布的交叉验证对数似然的概念，用该似然函数进行全极化SAR数据的类别数估计，根据全极化SAR数据的内部结构直接获得最优的类别数。基于该似然函数，本文又提出了一种新的全极化SAR数据非监督分类算法，带类别数估计的Wishart SPAN／H／α／A分类算法，该算法自动地根据全极化数据估计最优的类别数，并采用该最优类别数作为分类结果的类别数，消除分类类别数与数据内部结构不匹配的情况下可能导致的过拟合或欠拟合现象。另外，对于分类器定量评估方面，本文提出了采用交叉验证算法进行全极化SAR数据非监督分类器评估的方法，并尝试利用交叉验证算法实现全极化SAR数据非监督分类结果的评估。
The land cover classification is one of the most important, applications inpolarimetry remote sensing. The main advantage of fully polarimetric SAR datais that it can use target decomposition algorithm to extract the informationof the scattering mechanisms, which are not data specific and can be used fortarget identification. Thus using fully polarimetric SAR data we can achieve anunsupervised classification without ground truth information.
In this paper, an improvement for the Cloude-Pottier decomposition is givenfor analysis. We use the backscattering power information to improve the per-formance of the Cloude-Pottier decomposition and the transform algorithm isalso given to represent directly the decomposition results. Several unsupervisedclassification algorithms are also proposed to improve the classification perfor-mance step by step. Firstly, the Wishart SPAN/H/αclassification is proposed tointroduce the backscattering power information to the Wishart H/α/A classifi-cation. Then in order to include the scattering information within the parameterA, the Wishart SPAN/H/α/A classification is given to use the four parametersSPAN/H/α/A for initialization, and the Wishart test statistic is applied to re-duce the number of classes. The Wishart SPAN/H/α/A classification uses apredefined number of classes to perform the classification scheme. In fact, all theunsupervised classifications for fully polarimetric SAR data proposed nowadaysuse fixed number of clusters to classify the data. It is more reasonable to deter-mine the number of clusters directly from data analysis. According to the CrossValidation theorem in pattern recognition, We proposed the Cross-Validationlog-likelihood based on the complex Wishart distribution to estimate the opti-mal number of classes to reveal the inner structure of fully polarimetric SARdata. Using the Cross-Validation log-likelihood, a new unsupervised classifica-tion method, the Wishart SPAN/H/α/A classification with an adaptive numberof classes, is also given for interpretation. The number of classes is automati-cally optimized to avoid overfitting and underfitting for the inner structure of fully polarimetric SAR data. Moreover, since the Cross Validation algorithm is aquantitative estimation of the classification performance, it also has the potentialcapability to perform the validation of unsupervised classification.
引文
[1] ENVISAT ASAR Product Handbook. European Space Agency, 2002.
[2] A. B. Agrawal and W. M. Boerner. Redevelopment of Kennaugh's target characteristic polarization state theory using the polarization transformation ratio formalism for the coherent case. IEEE Transactions on Geoscience and Remote Sensing, 27(1):2-14, January 1989.
[3] S. Allain, C. Lopez-Martinez, L. Ferro-Famil, and E. Pottier. New eigenvalue-based parameters for natural media characterization. In Proc. IGARSS 2005, volume 1, pages 25-29, 2005.
[4] A. Baraldi, L. Bruzzone, and P. Blonda. Quality assessment of classification and cluster maps without ground truth knowledge. IEEE Transactions on Geoscience and Remote Sensing, 43(4):857-873, April 2005.
[5] C. F. Barnes and J. Burki. Late-season rural land-cover estimation with polarimetric-SAR intensity pixel blocks and σ-tree-structured nearneighbor classifiers. IEEE Transactions on Geoscience and Remote Sensing, 44(9):2384-2392, September 2006.
[6] Z. Belhadj and M. Yahia. Unsupervised classification of polarimetric SAR images using neural nets. In 2004 International Conference on Information and Communication Technologies: From Theory to Applications, pages 335-337, 2004.
[7] M. Bernier, Y. Gauthier, S. Mermoz, I. Gherboudj, A. El Battay, and J. Khaldoune. Investigating polarimetric SAR data for cryospheric monitoring in a canadian environment. In Proc. IGARSS 2005, volume 1, pages 25-29, 2005.
[8] W. M. Boerner and et al. Principles and Applications of Imaging Radar, volume 2, chapter Polarimetry in Radar Remote Sensing: Basic and Applied Concepts. John Willey and Sons, New York, 1998.
[9] W. L. Cameron, L. K. Leung, and N. N. Youssef. Simulated polarimetric signatures of primitive geometrical shapes. IEEE Transactions on Geoscience and Remote Sensing, 34(3):793-803, 1996.
[10] F. Cao and W. Hong. A new classification method based on Cloude-Pottier eigenvalue/eigenvector decomposition. In Proc. IGARSS 2005, volume 1, pages 296-299, Seoul, Korea, July 2005.
[11] F. Cao, W. Hong, and Y. R. Wu. An improved Cloude-Pottier decomposition using HSV transform and H/α/SPAN for fully polarimetric SAR data. Chinese Journal of Electronics. Submitted.
[12] F. Cao, W. Hong, and Y. R. Wu. An improved for Cloude-Pottier decomposition using H/α/SPAN and the complex Wishart classification for polarimetric SAR classification. In Proc. 2006 CIE International Conference on Radar, I, pages 567-570, Shanghai, China, October 2006.
[13] F. Cao, W. Hong, and Y. R. Wu. An unsupervised classification method using Wishart H/α/SPAN algorithm. In Proc. EUSAR 2006, Dresdon, Germany, May 2006.
[14] F. Cao, W. Hong, Y. R. Wu, C. B. Ding, Y. P. Wang, and H. L. Peng. An unsupervised classification for fully polarimetric SAR data using SPAN/H/α/A and agglomerative hierarchical clustering. In Proc. 2007 海峡两岸遥测／遥感研讨会，国立中央大学，台湾，March 2007．Accepted．
[15] F. Cao, W. Hong, Y. R. Wu, and E. Pottier. An unsupervised segmentation with an adaptive number of clusters using the SPAN/H/α/A space and the Monte-Carlo Cross-Validation for fully polarimetric SAR data analysis. IEEE Transactions on Geoscience and Remote Sensing. Submitted.
[16] F. Cao, W. Hong, Y. R. Wu, and E. Pottier. An unsupervised classification for fully polarimetric SAR data using SPAN/H/α/A and complex Wishart segmentation. In Proc. POLinSAR 2007, Rome, Italy, January 2007. ESA.
[17] W. J. Carper. The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data. Photogrammetric Engineering and Remote Sensing, 56(4): 459-467, 1990.
[18] K. R. Castleman. Digital Image Processing. Prentice Hall, Englewood Cliffs, NJ, USA, 1996.
[19] C. T. Chen, K. S. Chen, and J. S. Lee. The use of fully polarimetric information for the fuzzy neural classification of SAR images. IEEE Transactions on Geoscience and Remote Sensing, 41(9):2089-2100, September 2003.
[20] K. S. Chen, W. P. Huang, D. H. Tsay, and F. Amar. Classification of multifrequency polarimetric SAR imagery using a dynamic learning neural network. IEEE Transactions on Geoscience and Remote Sensing, 34(3): 814-820, May 1996.
[21] S. R. Cloude. Uniqueness of target decomposition theorem in radar polarimetry. In W. M. Boerner, editor, Direct and Inverse Methods in Radar Polarimetry, pages 267-298. Kluwer Academic Publishers, 1992.
[22] S. R. Cloude. Concept of polarisation entropy in optical scattering. Optical Engineering, 34(6):1599-1610, 1995.
[23] S. R. Cloude and K. P. Papathanassiou. Coherence optimisation in polarimetric SAR interferometry. In Proc. IGARSS 1997, volume 4, pages 1932-1934, 1997.
[24] S. R. Cloude and K. P. Papathanassiou. Polarimetric optimization in radar interferometry. Electronic Letters, 33(13):1176-1178, 1997.
[25] S. R. Cloude and K. P. Papathanassiou. Polarimetric SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 36(5):1551-1565, 1998.
[26] S. R. Cloude and K. P. Papathanassiou. Three-stage inversion process for polarimetric SAR interferometry. IEE Proc. Radar, Sonar and Navigation, 150: 125-134, 2003.
[27] S. R. Cloude and E. Pottier. A review of target decomposition theorems in radar polarimetry. IEEE Transactions on Geoscience and Remote Sensing, 34(2): 498-518, November 1996.
[28] S. R. Cloude and E. Pottier. An entropy based classification scheme for land applications of polarimetric SAR. IEEE Transactions on Geoscience and Remote Sensing, 35(1):68-78, 1997.
[29] E. Colin, C. Titin-Schnaider, , and W. Tabbara. An interferometric coherence optimization method in radar polarimetry for high-resolution imagery. IEEE Transactions on Geoscience and Remote Sensing, 44(1):167-175, 2006.
[30] K. Conradsen, A. A. Nielsen, J. Schou, and H. Skriver. Change detection in polarimetric SAR data and the complex Wishart distribution. In Proc. IGARSS 2001, volume 6, pages 2628-2630, Sydney, Australia, July 2001.
[31] K. Conradsen, A. A. Nielsen, J. Schou, and H. Skriver. A test statistic in the complex Wishart distribution and its application to change detection in polarimetric SAR data. IEEE Transactions on Geoscience and Remote Sensing, 41(1):4-19, January 2003.
[32] J. R. Copeland. Radar target classification by polarization properties. In Proc. IRE, volume 48 of 7, pages 1290-1296, 1960.
[33] D. G. Corr, S. R. Cloude, L. Ferro-Famil, D. Hoekman, K. Partington, E. Pottier, and A. Rodrigues. A review of the applications of SAR polarimetry and polarimetric interferometry-an ESA funded study. In Proc. POLin-SAR 2003, Frascati, Italy, January 2003.
[34] I. G. Cumming and J. J. van Zyl. Feature utility in polarimetric radar image classification. In Proc. IGARSS 1989, pages 1841-1846, 1989.
[35] J. C. Curlander and R. N. McDonough. Synthetic Aperture Radar: Systems and Signal Processing. John Wiley and Sons, New York, 1991.
[36] G. A. Deschamps. Geometrical representation of the polarization plane electromagnetic wave. In Proc. IRE, volume 39 of 5, pages 540-544, 1951.
[37] Y. Dong, A. K. Milne, and B. C. Forster. Segmentation and classification of vegetated areas using polarimetric SAR image data. IEEE Transactions on Geoscience and Remote Sensing, 39(2):321-329, February 2001.
[38] R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. John Wiley and Sons, New York, 2nd edition, 2001.
[39] K. Ersahin, B. Scheuchl, and I. Cumming. Incorporating texture information into polarimetric radar classification using neural networks. In Proc. IGARSS 2004, volume 1, September 2004.
[40] A. Ferretti, C. Prati, and F. Rocca. Nonlinear subsidence rate estimation using permanent scatterer in differential SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 38(5):2202-2212, 2000.
[41] L. Ferro-Famil and E. Pottier. Urban area remote sensing from L-band PolSAR data using Time-Frequency techniques. Urban Remote Sensing Joint Event, pages 1-6, April 2007.
[42] L. Ferro-Famil, E. Pottier, and J. S. Lee. Unsupervised classification and analysis of natural scenes from polarimetric interferometric SAR data. In Proc. IGARSS 2001, Sydney, Australia, July 2001.
[43] L. Ferro-Famil, E. Pottier, and J. S. Lee. Unsupervised classification of multifrequency and fully polarimetrie SAR images based on the H/A/α Wishart classifier. IEEE Transactions on Geoscience and Remote Sensing, 39(11):2332-2342, November 2001.
[44] L. Ferro-Famil, E. Pottier, and J. S. Lee. Classification and interpretation of polarimetric SAR data. In Proc. IGARSS 2002, Toronto, Canada, June 2002.
[45] Canada Centre for Remote Sensing. Advanced Radar Polarimetry Tutorial. 2006.
[46] A. Freeman. SAR calibration: An overview. IEEE Transaction on Geoscience and Remote Sensing, 30(6):1107-1121, November 1992.
[47] A. Freeman and S. Durden. A three component scattering model for polarimetric SAR data. IEEE Transactions on Geoscience and Remote Sensing, 36(3):963-973, May 1998.
[48] S. Fukuda and H. Hirosawa. Support Vector Machine classification of land cover: application to polarimetric SAR data. In Proc. IGARSS 2001, volume 1, pages 187-189, Sydney, Australia, July 2001.
[49] S. Fukuda, R. Katagiri, and H. Hirosawa. Unsupervised approach for polarimetric SAR image classification using Support Vector Machines. In Proc. IGARSS 2002, volume 5, pages 2599-2601, Toronto, Canada, June 2002.
[50] N. R. Goodman. Statistical analysis based on a certain multivariate complex Gaussian distribution (An introduction). Annals of Mathematical Statistics, 34: 152-177, 1963.
[51] G. De Grandi, J. S. Lee, D. Schuler, and E. Nezry. Texture and speckle statistics in polarimetric SAR synthesized images. IEEE Transactions on Geoscience and Remote Sensing, 41(9):2070-2088, September 2003.
[52] C. D. Graves. Radar polarization power scattering matrix. In Proc. IRE, volume 44 of 5, pages 248-252, 1956.
[53] S. Guillaso, L. Ferro-Famil, A. Reigber, and E. Pottier. Building characterization using L-band polarimetric interferometric SAR data. IEEE Geoscience and Remote Sensing Letters, 2(3):347-351, 2005.
[54] I. Hajnsek, E. Pottier, and S. R. Cloude. Inversion of surface parameters from polarimetric SAR. IEEE Transactions on Geoscience and Remote Sensing, 41(4): 727-744, 2003.
[55] A. Hanbury and J. Serra. A 3D-polar coordinate colour representation suitable for image analysis. Technical Report PRIP-TR-77, Pattern Recognition and Image Processing Group, Institute of Computer Aided Automation, Vienna University of Technology, A-1040 Vienna AUSTRIA, December 2002.
[56] R. Haralick and L. Shapiro. Survey of image segmentation techniques. Graphics image process, 29: 100-132, 1985.
[57] R. K. Hawkins, R. Touzi, and C. E. Livingstone. Calibration and use of CV-580 polarimetric SAR data. In Proc. 21st Canadian Symposium on Remote Sensing, Ottawa, Canada, 1999.
[58] M. Hellmann. A new approach for interpretation of SAR-data using polarimetric techniques. In Proc. IGARSS 1998, volume 4, pages 2195-2197, Seattle, July 1998.
[59] M. Helhnann. Classification Of Fully Polarimetric SAR Data For Cat-tographic Applications. PhD thesis, DLR, 1999.
[60] M. Hellmann, G. Jager, E. Kratzschmar, and M. Habermeyer. Classification of full polarimetric SAR-data using artificial neural networks and fuzzy algorithms. In Proc. IGARSS 1999, volume 4, pages 1995-1997, June 1999.
[61] M. Helhnann, G. Jager, and E. Pottier. Fuzzy clustering and interpretation of fully polarimetric SAR data. In Proc. IGARSS 2001, volume 6, pages 2790-2792, Sydney, Australia, July 2001.
[62] M. J. Hill, C. J. Ticehurst, J. S. Lee, M. R. Grunes, G. E. Donald, and D. Henry. Integration of optical and radar classifications for mapping pasture type in Western Australia. IEEE Transactions on Geoscience and Remote Sensing, 43(7):1665-1681, July 2005.
[63] D. H. Hoekman and M. J. Quiriones. Land cover type and biolnass classification using AirSAR data for evaluation of monitoring scenarios in the Columbian Amazon. IEEE Transactions on Geoscience and Remote Sensing, 38(2):685-696, March 2000.
[64] F. Holecz, E. Meier, J. Piesbergen, and D. Nuesch. Topographic effects on radar cross sections. In Proc. SAR Calibration Workshop, pages 23-28, ESTEC, Noordwijk, September 1993.
[65] J. R. Huynen. Phenomenological Theory Of Radar Targets. PhD thesis, Technical University of Delft, Netherlands, December 1970.
[66] J. R. Kender. Saturation, hue and normalised color: Calculation, digitization effects, and use. Technical report, Department of Computer Science, Carnegie-Mellon University, 1976.
[67] E. M. Kennaugh. Polarization properties of radar reflections. Master's thesis, The Ohio State University, Columbus, Ohio, March 1952.
[68] E. M. Kennaugh. Effects of the type of polarization on echo characteristics. Technical Report Technical Report 381-1 to 394-24, Antenna Laboratory, The Ohio State University Research Foundation, Columbus, Ohio, 1954.
[69] P. R. Kersten, J. S. Lee, and T. L. Ainsworth. Unsupervised classification of polarimetric synthetic aperture radar images using fuzzy clustering and EM clustering. IEEE Transactions on Geoscience and Remote Sensing, 43(3): 519-527, March 2005.
[70] K. U. Khan and J. Yang. Novel features for polarimetric SAR image classification by neural network. In Neural Networks and Brain, volume 1, pages 165-170. International Conference on ICNN& B '05, 2005.
[71] K. Kimura, Y. Yamaguchi, and H. Yamada. Pi-SAR image analysis using polarimetrie scattering parameters and total power. In Proc. IGARSS 2003, volume 1, pages 425-427, July 2003.
[72] K. Kimura, Y. Yamaguchi, and H. Yamada. Unsupervised land cover classification using H/α/TP space applied to POLSAR image analysis. IEICE Transactions on Communications, E87-B(6):1639-1647, June 2004.
[73] A. B. Kostinski and W. M. Boerner. On the foundations of radar polarimetry. IEEE Transactions on Antennas and Propagation, 34(12):1395-1404, 1986.
[74] Y. Kouskoulas, F. T. Ulaby, and L. E. Pierce. The Bayesian hierarchical classifier (BHC) and its application to short vegetation using multifrequency polarimetric SAR. IEEE Transactions on Geoscicnce and Remote Sensing, 42(2):469-477, February 2004.
[75] Luneburg. Principles of radar polarimetry. In Proc. Transactions on Electronic Theory, volume E78-C of 10, pages 1339-1345, 1995.
[76] P. Leducq, L. Ferro-Famil, and E. Pottier. Time-frequency analysis of polarimetric SAR images. In Proc. Radar Conference 2005, pages 109-112, European, 2005.
[77] J. S. Lee. Speckle suppression and analysis for synthetic aperture radar images. Optical Engineering, 25(5):636-643, May 1986.
[78] J. S. Lee, M. R. Grunes,, and E. Pottier. Quantitative comparison of classification capability: Fully polarimetric versus dual and single-polarization SAR. IEEE Transactions on Geoscience and Remote Sensing, 39(11):2343-2351, November 2002. 2001.
[79] J. S. Lee and M. R. Grunes. Unsupervised classification using polarimetric decomposition and complex Wishart classifier. In Proc. PIERS 1998, Baveno, Italy, July 1998.
[80] J. S. Lee and M. R. Grunes. Polarimetric SAR speckle filtering and terrain classification-an overview. In C. H. Chen, editor, Information Processing for Remote Sensing, pages 113-138, Singapore, 1999.
[81] J. S. Lee, M. R. Grunes, T. L. Ainsworth, L. Du, D. L. Shuler, and S. R. Cloude. Unsupervised classification of polarimetric SAR images by applying target decomposition and complex Wishart distribution. In Proc. 4th International Workshop on Radar Polarimetry, pages 196-202, Nantes, France, July 1998.
[82] J. S. Lee, M. R. Grunes, T. L. Ainsworth, L. J. Du, D. L. Schuler, and S. R. Cloude. Unsupervised classification using polarimetric decomposition and the complex Wishart classifier. IEEE Transactions on Geoscience and Remote Sensing, 37(5):2249-2257, September 1999.
[83] J. S. Lee, M. R. Grunes, and G. de Grandi. Polarimetric SAR speckle filtering and its implication for classification. IEEE Transaction on Geoscience and Remote Sensing, 37(5):2363-2373, September 1999.
[84] J. S. Lee, M. R. Grunes, and R. Kwok. Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution. International Journal of Remote Sensing, 15(11):2299-2311, 1994.
[85] J. S. Lee, M. R. Grunes, and S. A. Mango. Speckle reduction in multipolarization, multifrequency SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, 29:535-544, July 1991.
[86] J. S. Lee, M. R. Grunes, E. Pottier, and L. Ferro-Famil. Unsupervised terrain classification preserving polarimetric scattering characteristics. IEEE Transactions on Geoscience and Remote Sensing, 42(4):722-731, April 2004.
[87] J. S. Lee, M. R. Grunes, D. L. Schuler, E. Pottier, and L. Ferro-Famil. Scattering-model-based speckle filtering of polarimetric SAR data. IEEE Transactions on Geoscience and Remote Sensing, 44(1):176-187, January 2006.
[88] J. S. Lee, I. Jurkevich, P. Dewaele, P. Wambacq, and A. Oosterlinck. Speckle filtering of synthetic aperture radar images: A review. Remote Sensing Reviews, 8: 313-340, 1994.
[89] J. S. Lee, D. L. Schuler, and T. L. Ainsworth. Polarimetric sar data compensation for terrain azimuth slope variation. IEEE Transactions on Geoscience and Remote Sensing, 38(5):2153-2163, 2000.
[90] J. S. Lee, D. L. Schuter, T. L Ainsworth, E. Krogager, D. Kasilingam, and W. M. Boerner. On the estimation of radar polarization orientation shifts induced by terrain slopes. IEEE Transactions on Geoscience and Remote Sensing, 40(1):30-41, 2002.
[91] E. Luneburg. Radar polarimetry: A revision of basic concepts. In H. Serbest and S. Cloude, editors, Direct and Inverse Electromagnetic Scattering, pages 257-275. Kluwer Academic Publishers, 1996.
[92] P. Lombardo and C. J. Oliver. Optimal classification of polarimetric SAR images using segmentation. In Proc. Radar Conference 2002, Long Beach, California, April 2002.
[93] P. Lombardo and C. J. Oliver. Optimal polarimetrie segmentation for the classification of agricultural areas. In EUSAR 2002, Koeln, Germany, June 2002.
[94] P. Lombardo, M. Sciotti, T. M. Pellizzeri, and M. Meloni. Optimum modelbased segmentation techniques for multifrequency polarimetric SAR images of urban areas. IEEE Transactions on Geoscience and Remote Sensing, 41(9):1959-1975, September 2003.
[95] A. Lopes, E. Nezry, R. Touzi, and H. Laur. Structure detection and statistical adaptive speckle filtering in SAR images. International Journal of Remote Sensing, 14(9):1735-1758, 1993.
[96] A. Lopes, R. Touzi, and E. Nezry. Adaptive speckle filters and scene heterogeneity. IEEE Transactions on Geoscience and Remote Sensing, 28(6):992 1000, November 1990.
[97] P. M. Mather. Computer Processing of Remotely-Sensed Images: an introduction. John Wiley and Sons, Chichester, West Sussex, England, 3rd edition, 2004.
[98] T. Mette, K. Papathanassiou, and I. Hajnsek. Biomass estimation from polarimetric SAR interferometry over heterogeneous forest terrain. In Proc. IGARSS 2004, volume 1, pages 20-24, 2004.
[99] C. J. Oliver and S. Quegan. Understanding Synthetic Aperture Radar Images. Artech House, Boston, London, 1998.
[100] K. P. Papathanassiou and S. R. Cloude. Single-baseline polarimetric SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(6):2352-2363, 2001.
[101] K. N. Plataniotis and A. N. Venetsanopoulos. Color Image Processing and Applications. Springer, 2000.
[102] E. Pottier. On Dr. J. R. Huynen's main contributions in the development of polarimetric radar techniques. In Proc. SPIE 1992, volume 1748, pages 72-85, San Diego, USA, July 1992.
[103] E. Pottier. The H/A/α polarimetric decomposition approach applied to PolSAR data processing. In Proc. PIERS 1998, pages 120-122, Baveno, Italy, July 1998.
[104] E. Pottier. Unsupervised classification scheme and topography derivation of PoISAR data based on the H/α/A polarimetric decomposition theorem. In Proc. 4th International Workshop on Radar Polarimetry, pages 535-548, Nantes, France, July 1998.
[105] E. Pottier and J. S. Lee. Application of the H/A/alpha polarimetric decomposition theorem for unsupervised classification of fully polarimetric SAR data based on the Wishart distribution. In Proc. Committee on Earth Observing Satellites SAR Workshop, Toulouse, France, October 1999.
[106] E. Pottier and J. S. Lee. Unsupervised classification scheme of PolSAR data based on the complex Wishart distribution and the H/A/α polarimetric decompostion theorem. In Proc. EUSAR 2000, May 2000.
[107] M. Qong. Coherence optimization using the polarization state conformation in PolInSAR. IEEE Geoscience and Remote Sensing Letter, 2(3):301-305, 2005.
[108] S. Quegan. A unified algorithm for phase and cross-talk calibration of polarimetric data-theory and observations. IEEE Transaction on Geoscience and Remote Sensing, 32(1):89-99, January 1994.
[109] R. K. Raney. Dual-polarized SAR and stokes parameters. IEEE Geoscience and Remote Sensing letters, 3(3):317-319, 2006.
[110] H. Rudolf. Increase Of Information By Polarimetric Radar Systems. 2000.
[111] F. Sadjadi. hnproved target classification using optimum polarimetric SAR signatures. IEEE Transactions on Aerospace and Electronic Systems, 38(1): 38-49, January 2002.
[112] L. Sagues, J. Lopez-Sanchez, J. Fortuny, X. Fabregas, A. Broquetas, and A. Sieber. Indoor experiments on polarimetric SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 38(2):671-684, 2000.
[113] J. Schou and H. Skriver. Restoration of polarimetric SAR images using simulated annealing. IEEE Transactions on Geoscience and Remote Sensing, 39(9):2005-2016, September 2001.
[114] J. Schou, H. Skriver, A. A. Nielsen, and K. Conradsen. CFAR edge detector for polarimetric SAR images. IEEE Transactions on Geoscience and Remote Sensing, 41(1):20-32, January 2003.
[115] D. L Schuler, T. L. Ainsworth, J. S. Lee, and G. D. Grandi. Topographic mapping using polarimetric SAR data. International Journal of Remote Sensing, 19(1):141-160, 1998.
[116] D. L. Schuler, J. S. Lee, and G. D. Grandi. Measurement of topography using polariemtric SAR image. IEEE Transactions on Geoscience and Remote Sensing, 34(5):1266-1277, 1996.
[117] T. Y. Shih. The reversibility of six geometric color spaces. Photogrammetric Engineering and Remote Sensing, 61(10):1223-1232, October 1995.
[118] G. Sinclair. Modification of the radar range equation for arbitrary targets and arbitrary polarization. Technical Report Report 302-19, Antenna Laboratory, The Ohio State University Research Foundation, 1948.
[119] G. Sinclair. The transmission and reception of elliptically polarized waves. In Proc. IRE, volume 38 of 2, pages 148-151, 1950.
[120] H. Skriver, A. A. Nielsen, and K. Conradsen. Evaluation of the Wishart test statistics for polarimetric SAR data. In Proc. IGARSS 2003, volume 2, pages 699-701, July 2003.
[121] H. Skriver, J. Schou, A. A. Nielsen, and K. Conradsen. Polarimetric edge detector based on the complex Wishart distribution. In Proc. IGARSS 2001, volume 7, pages 3149-3151, Sydney, Australia, July 2001.
[122] H. Skriver, J. Schou, A. A. Nielsen, and K. Conradsen. Polarimetric segmentation using Wishart test statistic. In Proc. IGARSS 2002, volume 2, pages 1011-1013, Toronto, Canada, June 2002.
[123] A. R. Smith. Color gamut transform pairs. Computer Graphics, 12(3):12-19, 1978.
[124] P. Smyth. Clustering using Monte-Carlo Cross-Validation. In Proc. 2nd International Conference on Knowledge Discovery and Data Mining, pages 126-133, Portland, Oregon, 1996. AAAI Press.
[125] J. C. Souyris, P. Imbo, R. Fjφrtoft, S. Mingot, and J. S. Lee. Compact polarimetry based on symmetry properties of geophysical media: The π/4 mode. IEEE Transactions on Geoscience and Remote Sensing, 43(3):634-648, 2005.
[126] M. S. Srivastava. On the complex Wishart distribution. Annals of Mathematical Statistics, 36:313-315, 1965.
[127] A. A. Swartz, H. A. Yueh, and et al. Optimal polarizations for achieving maximum contrast in radar images. Journal of Geophysical Research, 93(B12): 15252-15260, 1988.
[128] PolSARpro Team. PolSARpro v3.0 tutorial. http://earth.esa.int/polsarpro, 2006.
[129] S. Theodoridis and K. Koutroumbas. Pattern Recognition. China Machine Press, Beijing, P. R. China, 2nd edition, September 2003.
[130] R. Touzi. Target scattering decomposition in terms of roll-invariant target parameters. IEEE Tran.sactions on Geoscience and Remote Sensing, 45 (1): 73-84, January 2007.
[131] R. Touzi, C. E. Livingstone, J. R. C. Lafontaine, and T. I. Lukowski. Consideration of antenna gain and phase patterns for calibration of polarimetric SAR data. IEEE Transaction on Geoscience and Remote Sensing, 31(6): 1132-1145, November 1993.
[132] R. Touzi and A. Lopes. The principle of speckle filtering in polarimetric SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, 32(5): 1110-1114, September 1994.
[133] T. N. Tran, R. Wehrens, D. H. Hoekman, and L. M. C. Buydens. Initialization of Markov random field clustering of large remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 43(8):1912-1919, August 2005.
[134] L. Tsang, J. A. Kong, and R. T. Shin. Theory of Microwave Remote Sensing. John Wiley and Sons, New York, 1985.
[135] F. T. Ulaby and C. Elachi. Radar Polarimetry For Geoscience Applications. Artech House, 1990.
[136] F. T. Ulaby, R. K. Moore, and A. K. Fung. Microwave Remote Sensing, Active and Passive, volume 2 of Radar remote sensing and surface scattering and emission theory. Artech House, Norwood, Massachusetts, 1986.
[137] J. J. van der Sanden and D. H. Hoekman. Multiband polarimetric SAR in support of tropical forest resources assessment. In Proc. IGARSS 1995, volume 2, pages 1207-1209, 1995.
[138] J. J. van Zyl. Unsupervised classification of scattering behaviour using radar polarimetry data. IEEE Transactions on Geoscience and Remote Sensing, 27(1):36-45, 1989.
[139] J. J. van Zyl. Calibration of polarimetric radar images using only image parameters and trihedral corner reflector responses. IEEE Transaction on Geoscience and Remote Sensing, 28(3):337-348, May 1990.
[140] J. J. van Zyl and C. F. Burnette. Baysian classification of polarimetric SAR images using adaptive a priori probability. International Journal of Remote Sensing, 13(5):835-840, 1992.
[141] J. J. van Zyl, H. A. Zeber, and C. Elachi. Imaging radar polarization signatures: Theory and observation. Radio Science, 22:529-543, 1987.
[142] G. Vasile, E. Trouve, J.S. Lee, and V. Buzuloiu. Intensity-Driven Adaptive-Neighborhood technique for polarimetric and interferometric SAR parameters estimation. IEEE Transactions on Geoscience and Remote Sensing, 44(6): 1609-1621, 2006.
[143] A. R. Webb. Statistical Pattern Recognition. John Wiley and Sons, New York, 2nd edition, 2002.
[144] R. G. White. A simulated annealing algorithm for SAR and MTI image cross-section estimation. In Proc. SPIE 1994, volume 2243, pages 231-241, 1994.
[145] F. Xu and Y. Q. Jin. Deorientation theory of polarimetric scattering targets and application to terrain surface classification. IEEE Transactions on Geoscience and Remote Sensing, 43(10):2351-2364, October 2005.
[146] M. Yahia and Z. Belhadj. Unsupervised classification of polarimetric SAR images using neural networks. In Proc. IGARSS 2003, volume 1, pages 203-205, July 2003.
[147] J. Yang, Y. N. Peng, Y. Yamaguchi, and H. Yamada. On Huynen's decomposition of a Kennaugh matrix. IEEE Geoscience and Remote Sensing Letters, 3(3):369-372, July 2006.
[148] J. Yang, Y. Yamaguchi, S. M. Lin, and W.M. Boerner. Numerical methods for solving the optimal problem of contrast enhancement. IEEE Transactions on Geoscience and Remote Sensing, 38(2):965-971, 2000.
[149] J. Yang, Y. Yamaguchi, and H. Yamada. Co-null of targets and co-null Abelian group. Electronics Letters, 35(12), June 1999.
[150] J. Yang, Y. Yamaguchi, and H. Yamada. Development of target null theory. IEEE Transactions on Geoscienee and Remote Sensing, 39(2):300-308, 2001.
[151] J. Yang, Y. Yamaguchi, H. Yamada, M. Sengoku, and S. M. Lin. Stable decomposition of Mueller matrix. IEICE Transactions on Communications, E81-B(6): 1261-1268, June 1998.
[152] J. Yang, Y. Yamaguchi, H. Yamada, M. Sengoku, and S. M. Lin. Optimal problem for contrast enhancement in polarimetric radar remote sensing. IEICE Transactions on Communications, E82-B(1):174-183, January 1999.
[153] H. A. Zebker and Y. L. Lou. Phase calibration of imaging radar polarimeter Stokes matrices. IEEE Transaction on Geoscience and Remote Sensing, 28(2): 246-252, March 1990.
[154] H. A. Zebker and J. J. van Zyl. Imaging radar polarimetry: A review. Proc. IEEE, 79(11): 1583-1606, November 1991.
[155] H. A. Zebker, J. J. van Zyl, S. L. Durden, and L. Norikane. Calibrated imaging radar polarimetry: Technique, examples and applications. IEEE Transactions on Geoscience and Remote Sensing, 29(5):942-961, 1991.
[156] H. A. Zebker, J. J. van Zyl, and D. N. Held. Imaging radar polarimetry from wave synthesis. Journal of Geophysical Research, 92:683-701, 1987.
[157] 曹芳，洪文，吴一戎．基于Cloude-Pottier目标分解和聚合的层次聚类算法的全极化SAR数据的非监督分类算法研究．电子学报．在审．
[158] 曹芳，洪文，吴一戎．雷达极化中的目标分解理论和方法研究．电子与信息学报，26：244-250，2004．
[2] A. B. Agrawal and W. M. Boerner. Redevelopment of Kennaugh's target characteristic polarization state theory using the polarization transformation ratio formalism for the coherent case. IEEE Transactions on Geoscience and Remote Sensing, 27(1):2-14, January 1989.
[3] S. Allain, C. Lopez-Martinez, L. Ferro-Famil, and E. Pottier. New eigenvalue-based parameters for natural media characterization. In Proc. IGARSS 2005, volume 1, pages 25-29, 2005.
[4] A. Baraldi, L. Bruzzone, and P. Blonda. Quality assessment of classification and cluster maps without ground truth knowledge. IEEE Transactions on Geoscience and Remote Sensing, 43(4):857-873, April 2005.
[5] C. F. Barnes and J. Burki. Late-season rural land-cover estimation with polarimetric-SAR intensity pixel blocks and σ-tree-structured nearneighbor classifiers. IEEE Transactions on Geoscience and Remote Sensing, 44(9):2384-2392, September 2006.
[6] Z. Belhadj and M. Yahia. Unsupervised classification of polarimetric SAR images using neural nets. In 2004 International Conference on Information and Communication Technologies: From Theory to Applications, pages 335-337, 2004.
[7] M. Bernier, Y. Gauthier, S. Mermoz, I. Gherboudj, A. El Battay, and J. Khaldoune. Investigating polarimetric SAR data for cryospheric monitoring in a canadian environment. In Proc. IGARSS 2005, volume 1, pages 25-29, 2005.
[8] W. M. Boerner and et al. Principles and Applications of Imaging Radar, volume 2, chapter Polarimetry in Radar Remote Sensing: Basic and Applied Concepts. John Willey and Sons, New York, 1998.
[9] W. L. Cameron, L. K. Leung, and N. N. Youssef. Simulated polarimetric signatures of primitive geometrical shapes. IEEE Transactions on Geoscience and Remote Sensing, 34(3):793-803, 1996.
[10] F. Cao and W. Hong. A new classification method based on Cloude-Pottier eigenvalue/eigenvector decomposition. In Proc. IGARSS 2005, volume 1, pages 296-299, Seoul, Korea, July 2005.
[11] F. Cao, W. Hong, and Y. R. Wu. An improved Cloude-Pottier decomposition using HSV transform and H/α/SPAN for fully polarimetric SAR data. Chinese Journal of Electronics. Submitted.
[12] F. Cao, W. Hong, and Y. R. Wu. An improved for Cloude-Pottier decomposition using H/α/SPAN and the complex Wishart classification for polarimetric SAR classification. In Proc. 2006 CIE International Conference on Radar, I, pages 567-570, Shanghai, China, October 2006.
[13] F. Cao, W. Hong, and Y. R. Wu. An unsupervised classification method using Wishart H/α/SPAN algorithm. In Proc. EUSAR 2006, Dresdon, Germany, May 2006.
[14] F. Cao, W. Hong, Y. R. Wu, C. B. Ding, Y. P. Wang, and H. L. Peng. An unsupervised classification for fully polarimetric SAR data using SPAN/H/α/A and agglomerative hierarchical clustering. In Proc. 2007 海峡两岸遥测／遥感研讨会，国立中央大学，台湾，March 2007．Accepted．
[15] F. Cao, W. Hong, Y. R. Wu, and E. Pottier. An unsupervised segmentation with an adaptive number of clusters using the SPAN/H/α/A space and the Monte-Carlo Cross-Validation for fully polarimetric SAR data analysis. IEEE Transactions on Geoscience and Remote Sensing. Submitted.
[16] F. Cao, W. Hong, Y. R. Wu, and E. Pottier. An unsupervised classification for fully polarimetric SAR data using SPAN/H/α/A and complex Wishart segmentation. In Proc. POLinSAR 2007, Rome, Italy, January 2007. ESA.
[17] W. J. Carper. The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data. Photogrammetric Engineering and Remote Sensing, 56(4): 459-467, 1990.
[18] K. R. Castleman. Digital Image Processing. Prentice Hall, Englewood Cliffs, NJ, USA, 1996.
[19] C. T. Chen, K. S. Chen, and J. S. Lee. The use of fully polarimetric information for the fuzzy neural classification of SAR images. IEEE Transactions on Geoscience and Remote Sensing, 41(9):2089-2100, September 2003.
[20] K. S. Chen, W. P. Huang, D. H. Tsay, and F. Amar. Classification of multifrequency polarimetric SAR imagery using a dynamic learning neural network. IEEE Transactions on Geoscience and Remote Sensing, 34(3): 814-820, May 1996.
[21] S. R. Cloude. Uniqueness of target decomposition theorem in radar polarimetry. In W. M. Boerner, editor, Direct and Inverse Methods in Radar Polarimetry, pages 267-298. Kluwer Academic Publishers, 1992.
[22] S. R. Cloude. Concept of polarisation entropy in optical scattering. Optical Engineering, 34(6):1599-1610, 1995.
[23] S. R. Cloude and K. P. Papathanassiou. Coherence optimisation in polarimetric SAR interferometry. In Proc. IGARSS 1997, volume 4, pages 1932-1934, 1997.
[24] S. R. Cloude and K. P. Papathanassiou. Polarimetric optimization in radar interferometry. Electronic Letters, 33(13):1176-1178, 1997.
[25] S. R. Cloude and K. P. Papathanassiou. Polarimetric SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 36(5):1551-1565, 1998.
[26] S. R. Cloude and K. P. Papathanassiou. Three-stage inversion process for polarimetric SAR interferometry. IEE Proc. Radar, Sonar and Navigation, 150: 125-134, 2003.
[27] S. R. Cloude and E. Pottier. A review of target decomposition theorems in radar polarimetry. IEEE Transactions on Geoscience and Remote Sensing, 34(2): 498-518, November 1996.
[28] S. R. Cloude and E. Pottier. An entropy based classification scheme for land applications of polarimetric SAR. IEEE Transactions on Geoscience and Remote Sensing, 35(1):68-78, 1997.
[29] E. Colin, C. Titin-Schnaider, , and W. Tabbara. An interferometric coherence optimization method in radar polarimetry for high-resolution imagery. IEEE Transactions on Geoscience and Remote Sensing, 44(1):167-175, 2006.
[30] K. Conradsen, A. A. Nielsen, J. Schou, and H. Skriver. Change detection in polarimetric SAR data and the complex Wishart distribution. In Proc. IGARSS 2001, volume 6, pages 2628-2630, Sydney, Australia, July 2001.
[31] K. Conradsen, A. A. Nielsen, J. Schou, and H. Skriver. A test statistic in the complex Wishart distribution and its application to change detection in polarimetric SAR data. IEEE Transactions on Geoscience and Remote Sensing, 41(1):4-19, January 2003.
[32] J. R. Copeland. Radar target classification by polarization properties. In Proc. IRE, volume 48 of 7, pages 1290-1296, 1960.
[33] D. G. Corr, S. R. Cloude, L. Ferro-Famil, D. Hoekman, K. Partington, E. Pottier, and A. Rodrigues. A review of the applications of SAR polarimetry and polarimetric interferometry-an ESA funded study. In Proc. POLin-SAR 2003, Frascati, Italy, January 2003.
[34] I. G. Cumming and J. J. van Zyl. Feature utility in polarimetric radar image classification. In Proc. IGARSS 1989, pages 1841-1846, 1989.
[35] J. C. Curlander and R. N. McDonough. Synthetic Aperture Radar: Systems and Signal Processing. John Wiley and Sons, New York, 1991.
[36] G. A. Deschamps. Geometrical representation of the polarization plane electromagnetic wave. In Proc. IRE, volume 39 of 5, pages 540-544, 1951.
[37] Y. Dong, A. K. Milne, and B. C. Forster. Segmentation and classification of vegetated areas using polarimetric SAR image data. IEEE Transactions on Geoscience and Remote Sensing, 39(2):321-329, February 2001.
[38] R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. John Wiley and Sons, New York, 2nd edition, 2001.
[39] K. Ersahin, B. Scheuchl, and I. Cumming. Incorporating texture information into polarimetric radar classification using neural networks. In Proc. IGARSS 2004, volume 1, September 2004.
[40] A. Ferretti, C. Prati, and F. Rocca. Nonlinear subsidence rate estimation using permanent scatterer in differential SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 38(5):2202-2212, 2000.
[41] L. Ferro-Famil and E. Pottier. Urban area remote sensing from L-band PolSAR data using Time-Frequency techniques. Urban Remote Sensing Joint Event, pages 1-6, April 2007.
[42] L. Ferro-Famil, E. Pottier, and J. S. Lee. Unsupervised classification and analysis of natural scenes from polarimetric interferometric SAR data. In Proc. IGARSS 2001, Sydney, Australia, July 2001.
[43] L. Ferro-Famil, E. Pottier, and J. S. Lee. Unsupervised classification of multifrequency and fully polarimetrie SAR images based on the H/A/α Wishart classifier. IEEE Transactions on Geoscience and Remote Sensing, 39(11):2332-2342, November 2001.
[44] L. Ferro-Famil, E. Pottier, and J. S. Lee. Classification and interpretation of polarimetric SAR data. In Proc. IGARSS 2002, Toronto, Canada, June 2002.
[45] Canada Centre for Remote Sensing. Advanced Radar Polarimetry Tutorial. 2006.
[46] A. Freeman. SAR calibration: An overview. IEEE Transaction on Geoscience and Remote Sensing, 30(6):1107-1121, November 1992.
[47] A. Freeman and S. Durden. A three component scattering model for polarimetric SAR data. IEEE Transactions on Geoscience and Remote Sensing, 36(3):963-973, May 1998.
[48] S. Fukuda and H. Hirosawa. Support Vector Machine classification of land cover: application to polarimetric SAR data. In Proc. IGARSS 2001, volume 1, pages 187-189, Sydney, Australia, July 2001.
[49] S. Fukuda, R. Katagiri, and H. Hirosawa. Unsupervised approach for polarimetric SAR image classification using Support Vector Machines. In Proc. IGARSS 2002, volume 5, pages 2599-2601, Toronto, Canada, June 2002.
[50] N. R. Goodman. Statistical analysis based on a certain multivariate complex Gaussian distribution (An introduction). Annals of Mathematical Statistics, 34: 152-177, 1963.
[51] G. De Grandi, J. S. Lee, D. Schuler, and E. Nezry. Texture and speckle statistics in polarimetric SAR synthesized images. IEEE Transactions on Geoscience and Remote Sensing, 41(9):2070-2088, September 2003.
[52] C. D. Graves. Radar polarization power scattering matrix. In Proc. IRE, volume 44 of 5, pages 248-252, 1956.
[53] S. Guillaso, L. Ferro-Famil, A. Reigber, and E. Pottier. Building characterization using L-band polarimetric interferometric SAR data. IEEE Geoscience and Remote Sensing Letters, 2(3):347-351, 2005.
[54] I. Hajnsek, E. Pottier, and S. R. Cloude. Inversion of surface parameters from polarimetric SAR. IEEE Transactions on Geoscience and Remote Sensing, 41(4): 727-744, 2003.
[55] A. Hanbury and J. Serra. A 3D-polar coordinate colour representation suitable for image analysis. Technical Report PRIP-TR-77, Pattern Recognition and Image Processing Group, Institute of Computer Aided Automation, Vienna University of Technology, A-1040 Vienna AUSTRIA, December 2002.
[56] R. Haralick and L. Shapiro. Survey of image segmentation techniques. Graphics image process, 29: 100-132, 1985.
[57] R. K. Hawkins, R. Touzi, and C. E. Livingstone. Calibration and use of CV-580 polarimetric SAR data. In Proc. 21st Canadian Symposium on Remote Sensing, Ottawa, Canada, 1999.
[58] M. Hellmann. A new approach for interpretation of SAR-data using polarimetric techniques. In Proc. IGARSS 1998, volume 4, pages 2195-2197, Seattle, July 1998.
[59] M. Helhnann. Classification Of Fully Polarimetric SAR Data For Cat-tographic Applications. PhD thesis, DLR, 1999.
[60] M. Hellmann, G. Jager, E. Kratzschmar, and M. Habermeyer. Classification of full polarimetric SAR-data using artificial neural networks and fuzzy algorithms. In Proc. IGARSS 1999, volume 4, pages 1995-1997, June 1999.
[61] M. Helhnann, G. Jager, and E. Pottier. Fuzzy clustering and interpretation of fully polarimetric SAR data. In Proc. IGARSS 2001, volume 6, pages 2790-2792, Sydney, Australia, July 2001.
[62] M. J. Hill, C. J. Ticehurst, J. S. Lee, M. R. Grunes, G. E. Donald, and D. Henry. Integration of optical and radar classifications for mapping pasture type in Western Australia. IEEE Transactions on Geoscience and Remote Sensing, 43(7):1665-1681, July 2005.
[63] D. H. Hoekman and M. J. Quiriones. Land cover type and biolnass classification using AirSAR data for evaluation of monitoring scenarios in the Columbian Amazon. IEEE Transactions on Geoscience and Remote Sensing, 38(2):685-696, March 2000.
[64] F. Holecz, E. Meier, J. Piesbergen, and D. Nuesch. Topographic effects on radar cross sections. In Proc. SAR Calibration Workshop, pages 23-28, ESTEC, Noordwijk, September 1993.
[65] J. R. Huynen. Phenomenological Theory Of Radar Targets. PhD thesis, Technical University of Delft, Netherlands, December 1970.
[66] J. R. Kender. Saturation, hue and normalised color: Calculation, digitization effects, and use. Technical report, Department of Computer Science, Carnegie-Mellon University, 1976.
[67] E. M. Kennaugh. Polarization properties of radar reflections. Master's thesis, The Ohio State University, Columbus, Ohio, March 1952.
[68] E. M. Kennaugh. Effects of the type of polarization on echo characteristics. Technical Report Technical Report 381-1 to 394-24, Antenna Laboratory, The Ohio State University Research Foundation, Columbus, Ohio, 1954.
[69] P. R. Kersten, J. S. Lee, and T. L. Ainsworth. Unsupervised classification of polarimetric synthetic aperture radar images using fuzzy clustering and EM clustering. IEEE Transactions on Geoscience and Remote Sensing, 43(3): 519-527, March 2005.
[70] K. U. Khan and J. Yang. Novel features for polarimetric SAR image classification by neural network. In Neural Networks and Brain, volume 1, pages 165-170. International Conference on ICNN& B '05, 2005.
[71] K. Kimura, Y. Yamaguchi, and H. Yamada. Pi-SAR image analysis using polarimetrie scattering parameters and total power. In Proc. IGARSS 2003, volume 1, pages 425-427, July 2003.
[72] K. Kimura, Y. Yamaguchi, and H. Yamada. Unsupervised land cover classification using H/α/TP space applied to POLSAR image analysis. IEICE Transactions on Communications, E87-B(6):1639-1647, June 2004.
[73] A. B. Kostinski and W. M. Boerner. On the foundations of radar polarimetry. IEEE Transactions on Antennas and Propagation, 34(12):1395-1404, 1986.
[74] Y. Kouskoulas, F. T. Ulaby, and L. E. Pierce. The Bayesian hierarchical classifier (BHC) and its application to short vegetation using multifrequency polarimetric SAR. IEEE Transactions on Geoscicnce and Remote Sensing, 42(2):469-477, February 2004.
[75] Luneburg. Principles of radar polarimetry. In Proc. Transactions on Electronic Theory, volume E78-C of 10, pages 1339-1345, 1995.
[76] P. Leducq, L. Ferro-Famil, and E. Pottier. Time-frequency analysis of polarimetric SAR images. In Proc. Radar Conference 2005, pages 109-112, European, 2005.
[77] J. S. Lee. Speckle suppression and analysis for synthetic aperture radar images. Optical Engineering, 25(5):636-643, May 1986.
[78] J. S. Lee, M. R. Grunes,, and E. Pottier. Quantitative comparison of classification capability: Fully polarimetric versus dual and single-polarization SAR. IEEE Transactions on Geoscience and Remote Sensing, 39(11):2343-2351, November 2002. 2001.
[79] J. S. Lee and M. R. Grunes. Unsupervised classification using polarimetric decomposition and complex Wishart classifier. In Proc. PIERS 1998, Baveno, Italy, July 1998.
[80] J. S. Lee and M. R. Grunes. Polarimetric SAR speckle filtering and terrain classification-an overview. In C. H. Chen, editor, Information Processing for Remote Sensing, pages 113-138, Singapore, 1999.
[81] J. S. Lee, M. R. Grunes, T. L. Ainsworth, L. Du, D. L. Shuler, and S. R. Cloude. Unsupervised classification of polarimetric SAR images by applying target decomposition and complex Wishart distribution. In Proc. 4th International Workshop on Radar Polarimetry, pages 196-202, Nantes, France, July 1998.
[82] J. S. Lee, M. R. Grunes, T. L. Ainsworth, L. J. Du, D. L. Schuler, and S. R. Cloude. Unsupervised classification using polarimetric decomposition and the complex Wishart classifier. IEEE Transactions on Geoscience and Remote Sensing, 37(5):2249-2257, September 1999.
[83] J. S. Lee, M. R. Grunes, and G. de Grandi. Polarimetric SAR speckle filtering and its implication for classification. IEEE Transaction on Geoscience and Remote Sensing, 37(5):2363-2373, September 1999.
[84] J. S. Lee, M. R. Grunes, and R. Kwok. Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution. International Journal of Remote Sensing, 15(11):2299-2311, 1994.
[85] J. S. Lee, M. R. Grunes, and S. A. Mango. Speckle reduction in multipolarization, multifrequency SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, 29:535-544, July 1991.
[86] J. S. Lee, M. R. Grunes, E. Pottier, and L. Ferro-Famil. Unsupervised terrain classification preserving polarimetric scattering characteristics. IEEE Transactions on Geoscience and Remote Sensing, 42(4):722-731, April 2004.
[87] J. S. Lee, M. R. Grunes, D. L. Schuler, E. Pottier, and L. Ferro-Famil. Scattering-model-based speckle filtering of polarimetric SAR data. IEEE Transactions on Geoscience and Remote Sensing, 44(1):176-187, January 2006.
[88] J. S. Lee, I. Jurkevich, P. Dewaele, P. Wambacq, and A. Oosterlinck. Speckle filtering of synthetic aperture radar images: A review. Remote Sensing Reviews, 8: 313-340, 1994.
[89] J. S. Lee, D. L. Schuler, and T. L. Ainsworth. Polarimetric sar data compensation for terrain azimuth slope variation. IEEE Transactions on Geoscience and Remote Sensing, 38(5):2153-2163, 2000.
[90] J. S. Lee, D. L. Schuter, T. L Ainsworth, E. Krogager, D. Kasilingam, and W. M. Boerner. On the estimation of radar polarization orientation shifts induced by terrain slopes. IEEE Transactions on Geoscience and Remote Sensing, 40(1):30-41, 2002.
[91] E. Luneburg. Radar polarimetry: A revision of basic concepts. In H. Serbest and S. Cloude, editors, Direct and Inverse Electromagnetic Scattering, pages 257-275. Kluwer Academic Publishers, 1996.
[92] P. Lombardo and C. J. Oliver. Optimal classification of polarimetric SAR images using segmentation. In Proc. Radar Conference 2002, Long Beach, California, April 2002.
[93] P. Lombardo and C. J. Oliver. Optimal polarimetrie segmentation for the classification of agricultural areas. In EUSAR 2002, Koeln, Germany, June 2002.
[94] P. Lombardo, M. Sciotti, T. M. Pellizzeri, and M. Meloni. Optimum modelbased segmentation techniques for multifrequency polarimetric SAR images of urban areas. IEEE Transactions on Geoscience and Remote Sensing, 41(9):1959-1975, September 2003.
[95] A. Lopes, E. Nezry, R. Touzi, and H. Laur. Structure detection and statistical adaptive speckle filtering in SAR images. International Journal of Remote Sensing, 14(9):1735-1758, 1993.
[96] A. Lopes, R. Touzi, and E. Nezry. Adaptive speckle filters and scene heterogeneity. IEEE Transactions on Geoscience and Remote Sensing, 28(6):992 1000, November 1990.
[97] P. M. Mather. Computer Processing of Remotely-Sensed Images: an introduction. John Wiley and Sons, Chichester, West Sussex, England, 3rd edition, 2004.
[98] T. Mette, K. Papathanassiou, and I. Hajnsek. Biomass estimation from polarimetric SAR interferometry over heterogeneous forest terrain. In Proc. IGARSS 2004, volume 1, pages 20-24, 2004.
[99] C. J. Oliver and S. Quegan. Understanding Synthetic Aperture Radar Images. Artech House, Boston, London, 1998.
[100] K. P. Papathanassiou and S. R. Cloude. Single-baseline polarimetric SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(6):2352-2363, 2001.
[101] K. N. Plataniotis and A. N. Venetsanopoulos. Color Image Processing and Applications. Springer, 2000.
[102] E. Pottier. On Dr. J. R. Huynen's main contributions in the development of polarimetric radar techniques. In Proc. SPIE 1992, volume 1748, pages 72-85, San Diego, USA, July 1992.
[103] E. Pottier. The H/A/α polarimetric decomposition approach applied to PolSAR data processing. In Proc. PIERS 1998, pages 120-122, Baveno, Italy, July 1998.
[104] E. Pottier. Unsupervised classification scheme and topography derivation of PoISAR data based on the H/α/A polarimetric decomposition theorem. In Proc. 4th International Workshop on Radar Polarimetry, pages 535-548, Nantes, France, July 1998.
[105] E. Pottier and J. S. Lee. Application of the H/A/alpha polarimetric decomposition theorem for unsupervised classification of fully polarimetric SAR data based on the Wishart distribution. In Proc. Committee on Earth Observing Satellites SAR Workshop, Toulouse, France, October 1999.
[106] E. Pottier and J. S. Lee. Unsupervised classification scheme of PolSAR data based on the complex Wishart distribution and the H/A/α polarimetric decompostion theorem. In Proc. EUSAR 2000, May 2000.
[107] M. Qong. Coherence optimization using the polarization state conformation in PolInSAR. IEEE Geoscience and Remote Sensing Letter, 2(3):301-305, 2005.
[108] S. Quegan. A unified algorithm for phase and cross-talk calibration of polarimetric data-theory and observations. IEEE Transaction on Geoscience and Remote Sensing, 32(1):89-99, January 1994.
[109] R. K. Raney. Dual-polarized SAR and stokes parameters. IEEE Geoscience and Remote Sensing letters, 3(3):317-319, 2006.
[110] H. Rudolf. Increase Of Information By Polarimetric Radar Systems. 2000.
[111] F. Sadjadi. hnproved target classification using optimum polarimetric SAR signatures. IEEE Transactions on Aerospace and Electronic Systems, 38(1): 38-49, January 2002.
[112] L. Sagues, J. Lopez-Sanchez, J. Fortuny, X. Fabregas, A. Broquetas, and A. Sieber. Indoor experiments on polarimetric SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 38(2):671-684, 2000.
[113] J. Schou and H. Skriver. Restoration of polarimetric SAR images using simulated annealing. IEEE Transactions on Geoscience and Remote Sensing, 39(9):2005-2016, September 2001.
[114] J. Schou, H. Skriver, A. A. Nielsen, and K. Conradsen. CFAR edge detector for polarimetric SAR images. IEEE Transactions on Geoscience and Remote Sensing, 41(1):20-32, January 2003.
[115] D. L Schuler, T. L. Ainsworth, J. S. Lee, and G. D. Grandi. Topographic mapping using polarimetric SAR data. International Journal of Remote Sensing, 19(1):141-160, 1998.
[116] D. L. Schuler, J. S. Lee, and G. D. Grandi. Measurement of topography using polariemtric SAR image. IEEE Transactions on Geoscience and Remote Sensing, 34(5):1266-1277, 1996.
[117] T. Y. Shih. The reversibility of six geometric color spaces. Photogrammetric Engineering and Remote Sensing, 61(10):1223-1232, October 1995.
[118] G. Sinclair. Modification of the radar range equation for arbitrary targets and arbitrary polarization. Technical Report Report 302-19, Antenna Laboratory, The Ohio State University Research Foundation, 1948.
[119] G. Sinclair. The transmission and reception of elliptically polarized waves. In Proc. IRE, volume 38 of 2, pages 148-151, 1950.
[120] H. Skriver, A. A. Nielsen, and K. Conradsen. Evaluation of the Wishart test statistics for polarimetric SAR data. In Proc. IGARSS 2003, volume 2, pages 699-701, July 2003.
[121] H. Skriver, J. Schou, A. A. Nielsen, and K. Conradsen. Polarimetric edge detector based on the complex Wishart distribution. In Proc. IGARSS 2001, volume 7, pages 3149-3151, Sydney, Australia, July 2001.
[122] H. Skriver, J. Schou, A. A. Nielsen, and K. Conradsen. Polarimetric segmentation using Wishart test statistic. In Proc. IGARSS 2002, volume 2, pages 1011-1013, Toronto, Canada, June 2002.
[123] A. R. Smith. Color gamut transform pairs. Computer Graphics, 12(3):12-19, 1978.
[124] P. Smyth. Clustering using Monte-Carlo Cross-Validation. In Proc. 2nd International Conference on Knowledge Discovery and Data Mining, pages 126-133, Portland, Oregon, 1996. AAAI Press.
[125] J. C. Souyris, P. Imbo, R. Fjφrtoft, S. Mingot, and J. S. Lee. Compact polarimetry based on symmetry properties of geophysical media: The π/4 mode. IEEE Transactions on Geoscience and Remote Sensing, 43(3):634-648, 2005.
[126] M. S. Srivastava. On the complex Wishart distribution. Annals of Mathematical Statistics, 36:313-315, 1965.
[127] A. A. Swartz, H. A. Yueh, and et al. Optimal polarizations for achieving maximum contrast in radar images. Journal of Geophysical Research, 93(B12): 15252-15260, 1988.
[128] PolSARpro Team. PolSARpro v3.0 tutorial. http://earth.esa.int/polsarpro, 2006.
[129] S. Theodoridis and K. Koutroumbas. Pattern Recognition. China Machine Press, Beijing, P. R. China, 2nd edition, September 2003.
[130] R. Touzi. Target scattering decomposition in terms of roll-invariant target parameters. IEEE Tran.sactions on Geoscience and Remote Sensing, 45 (1): 73-84, January 2007.
[131] R. Touzi, C. E. Livingstone, J. R. C. Lafontaine, and T. I. Lukowski. Consideration of antenna gain and phase patterns for calibration of polarimetric SAR data. IEEE Transaction on Geoscience and Remote Sensing, 31(6): 1132-1145, November 1993.
[132] R. Touzi and A. Lopes. The principle of speckle filtering in polarimetric SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, 32(5): 1110-1114, September 1994.
[133] T. N. Tran, R. Wehrens, D. H. Hoekman, and L. M. C. Buydens. Initialization of Markov random field clustering of large remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 43(8):1912-1919, August 2005.
[134] L. Tsang, J. A. Kong, and R. T. Shin. Theory of Microwave Remote Sensing. John Wiley and Sons, New York, 1985.
[135] F. T. Ulaby and C. Elachi. Radar Polarimetry For Geoscience Applications. Artech House, 1990.
[136] F. T. Ulaby, R. K. Moore, and A. K. Fung. Microwave Remote Sensing, Active and Passive, volume 2 of Radar remote sensing and surface scattering and emission theory. Artech House, Norwood, Massachusetts, 1986.
[137] J. J. van der Sanden and D. H. Hoekman. Multiband polarimetric SAR in support of tropical forest resources assessment. In Proc. IGARSS 1995, volume 2, pages 1207-1209, 1995.
[138] J. J. van Zyl. Unsupervised classification of scattering behaviour using radar polarimetry data. IEEE Transactions on Geoscience and Remote Sensing, 27(1):36-45, 1989.
[139] J. J. van Zyl. Calibration of polarimetric radar images using only image parameters and trihedral corner reflector responses. IEEE Transaction on Geoscience and Remote Sensing, 28(3):337-348, May 1990.
[140] J. J. van Zyl and C. F. Burnette. Baysian classification of polarimetric SAR images using adaptive a priori probability. International Journal of Remote Sensing, 13(5):835-840, 1992.
[141] J. J. van Zyl, H. A. Zeber, and C. Elachi. Imaging radar polarization signatures: Theory and observation. Radio Science, 22:529-543, 1987.
[142] G. Vasile, E. Trouve, J.S. Lee, and V. Buzuloiu. Intensity-Driven Adaptive-Neighborhood technique for polarimetric and interferometric SAR parameters estimation. IEEE Transactions on Geoscience and Remote Sensing, 44(6): 1609-1621, 2006.
[143] A. R. Webb. Statistical Pattern Recognition. John Wiley and Sons, New York, 2nd edition, 2002.
[144] R. G. White. A simulated annealing algorithm for SAR and MTI image cross-section estimation. In Proc. SPIE 1994, volume 2243, pages 231-241, 1994.
[145] F. Xu and Y. Q. Jin. Deorientation theory of polarimetric scattering targets and application to terrain surface classification. IEEE Transactions on Geoscience and Remote Sensing, 43(10):2351-2364, October 2005.
[146] M. Yahia and Z. Belhadj. Unsupervised classification of polarimetric SAR images using neural networks. In Proc. IGARSS 2003, volume 1, pages 203-205, July 2003.
[147] J. Yang, Y. N. Peng, Y. Yamaguchi, and H. Yamada. On Huynen's decomposition of a Kennaugh matrix. IEEE Geoscience and Remote Sensing Letters, 3(3):369-372, July 2006.
[148] J. Yang, Y. Yamaguchi, S. M. Lin, and W.M. Boerner. Numerical methods for solving the optimal problem of contrast enhancement. IEEE Transactions on Geoscience and Remote Sensing, 38(2):965-971, 2000.
[149] J. Yang, Y. Yamaguchi, and H. Yamada. Co-null of targets and co-null Abelian group. Electronics Letters, 35(12), June 1999.
[150] J. Yang, Y. Yamaguchi, and H. Yamada. Development of target null theory. IEEE Transactions on Geoscienee and Remote Sensing, 39(2):300-308, 2001.
[151] J. Yang, Y. Yamaguchi, H. Yamada, M. Sengoku, and S. M. Lin. Stable decomposition of Mueller matrix. IEICE Transactions on Communications, E81-B(6): 1261-1268, June 1998.
[152] J. Yang, Y. Yamaguchi, H. Yamada, M. Sengoku, and S. M. Lin. Optimal problem for contrast enhancement in polarimetric radar remote sensing. IEICE Transactions on Communications, E82-B(1):174-183, January 1999.
[153] H. A. Zebker and Y. L. Lou. Phase calibration of imaging radar polarimeter Stokes matrices. IEEE Transaction on Geoscience and Remote Sensing, 28(2): 246-252, March 1990.
[154] H. A. Zebker and J. J. van Zyl. Imaging radar polarimetry: A review. Proc. IEEE, 79(11): 1583-1606, November 1991.
[155] H. A. Zebker, J. J. van Zyl, S. L. Durden, and L. Norikane. Calibrated imaging radar polarimetry: Technique, examples and applications. IEEE Transactions on Geoscience and Remote Sensing, 29(5):942-961, 1991.
[156] H. A. Zebker, J. J. van Zyl, and D. N. Held. Imaging radar polarimetry from wave synthesis. Journal of Geophysical Research, 92:683-701, 1987.
[157] 曹芳，洪文，吴一戎．基于Cloude-Pottier目标分解和聚合的层次聚类算法的全极化SAR数据的非监督分类算法研究．电子学报．在审．
[158] 曹芳，洪文，吴一戎．雷达极化中的目标分解理论和方法研究．电子与信息学报，26：244-250，2004．