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Sciences in Cold and Arid Regions ›› 2016, Vol. 8 ›› Issue (3): 263-267.doi: 10.3724/SP.J.1226.2016.00263

• ARTICLES • Previous Articles    

Classification of full-polarization ALOS-PALSAR imagery using SVM in arid area of Dunhuang

JunZhan Wang, JianJun Qu, WeiMin Zhang, KeCun Zhang   

  1. Dunhuang Gobi and Desert Ecology and Environment Research Station, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
  • Received:2016-02-12 Revised:2016-04-22 Published:2018-11-23
  • Contact: Mr.JunZhan Wang,Cold and Arid Regions Environmental and Engineering Research Institute,Chinese Academy of Sciences.No.320,West Donggang Road,Lanzhou,Gansu 730000,China.E-mail:cani04@163.com E-mail:cani04@163.com
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (41401408,41371027).The authors would like to thank all the experts and editors.

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Abstract: Classification is an important process in interpretation of synthetic aperture radar (SAR) imagery.As an advanced instrument for remote sensing,the polarimetric SAR has been applied widely in many fields.The main aim of this paper is to explore the ability of the full-polarization SAR data in classification.The study area is a part of Dunhuang,Gansu Province,China.An L-band full-polarization image of Dunhuang which includes quad-polarization modes was acquired by the ALOS-PALSAR (Advanced Land Observing Satellite-the Phased Array type L-band Synthetic Aperture Radar).Firstly,new characteristic information was extracted by the difference operation,ratio operation,and principal component transform based on the full-polarization (HH,HV or VH,VV) modes SAR data.Then the single-,dual-,full-polarization SAR data and new SAR characteristic information were used to analyze quantitatively the classification accuracy based on the Support Vector Machines (SVM).The results show that classification overall accuracy of single-polarization SAR data is poor,and the highest is 56.53% of VV polarization.The classification overall accuracy of dual-polarization SAR is much better than single-polarization,the highest is 74.77% of HV & VV polarization data.The classification overall accuracy of full-polarization SAR is 80.21%,adding the difference characteristic information,ratio characteristic information and the first principal component (PC1) respectively,the overall accuracy increased by 3.09%,3.38%,4.14% respectively.When the full-polarization SAR data in combination with the all characteristic information,the classification overall accuracy reached to 91.01%.The full-polarization SAR data in combination with the band math characteristic information or the PC1 can greatly improve classification accuracy.

Key words: full-polarization, PALSAR, classification, the Support Vector Machines (SVM)

Ainsworth TL, Kelly JP, Lee JS, 2009. Classification comparisons between dual-pol, compact polarimetric and quad-pol SAR imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 64(5):464-471. DOI:10.1016/j.isprsjprs.2008.12.008.
Bastin L, 1997. Comparison of fuzzy c-means classification, linear mixture modeling and MLC probabilities as tools for unmixing coarse pixels. International Journal of Remote Sensing, 18(17):3629-3648. DOI:10.1080/014311697216847.
Cloude SR, Pottier E, 1996. A review of target decomposition theorems in radar polarimetry. IEEE Transactions on Geoscience and Remote Sensing, 34(2):498-518.DOI:10.1109/36.485127.
Cloude SR, Pottier E, 1997. An entropy based classification scheme for land applications of polarimetric SAR. IEEE Transactions on Geoscience and Remote Sensing, 35(1):68-78.DOI:10.1109/36.551935.
Cortes C, Vapnik VN, 1995. Support vector networks. Machine Learning, 20(3):273-297.
Dong Y, Forster B, Ticehurst C, 1998. A new decomposition of radar polarization signatures. IEEE Transactions on Geoscience and Remote Sensing, 36(3):933-939. DOI:10.1109/36.673684.
Emerson CW, Lam NS, Quattrochi DA, 2005. A comparison of local variance, fractal dimension, and Moran's I as aids to multispectral image classification. Int. J. Remote Sens., 26(8):1575-1588.
Ferro-Famil L, Pottier E, Lee JS, 2001. Unsupervised classification of multifrequency and fully polarimetric SAR images based on the H/A/Alpha-Wishart classifier. IEEE Transactions on Geoscience and Remote Sensing, 39(11):2332-2342. DOI:10.1109/36.964969.
Georgios CA, 2009. SVM-based target recognition from synthetic aperture radar images using target region outline descriptors.Nonlinear Analysis, 71(12):e2934-e2939.
Giacinto G, Roli F, Bruzzone L, 2000. Combination of neural and statistical algorithms for supervised classification of remote-sensing images. Pattern Recognition Letters, 21(5):385-397.
Lee JS, Grunes MR, Pottier E, 2001. Quantitative comparison of classification capability:Fully polarimetric versus dual-and single-polarization SAR. IEEE Transactions on Geoscience and Remote Sensing, 39(11):2343-2351. DOI:10.1109/36.964970.
Macrì Pellizzeri T, 2003. Classification of polarimetric SAR images of suburban areas using joint annealed segmentation and "H/A/a" polarimetric decomposition". ISPRS Journal of Photogrammetry & Remote Sensing, 58(1-2):55-70. DOI:10.1016/S0924-2716(03)00017-0.
Melgani F, Bruzzone L, 2004. Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42(8):1778-1790. DOI:10.1109/TGRS.2004.831865.
Triloki P, Dharmendra S, Tanuja S, 2010. Advanced fractal approach for unsupervised classification of SAR images. Advances in Space Research, 45(1):1338-1349.
Vapnik VN, 1995. The Nature of Statistical Learning Theory, Springer Verlag. New York, pp. 1-50.
Wen X, Zhang H, Zhang J, et al., 2009. Multi-scale modeling for classification of SAR imagery using hybrid EM algorithm and genetic algorithm. Progress in Natural Science, 19(8):1033-1036. DOI:10.1016/j.pnsc.2009.01.003.
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