JGIS  Vol.2 No.2 , April 2010
Double Polarization SAR Image Classification based on Object-Oriented Technology
ABSTRACT
This paper proposed to use double polarization synthetic aperture radar (SAR) image to classify surface feature, based on DEM. It takes fully use of the polarization information and external information. This pa-per utilizes ENVISAT ASAR APP double-polarization data of Poyang lake area in Jiangxi Province. Com-pared with traditional pixel-based classification, this paper fully uses object features (color, shape, hierarchy) and accessorial DEM information. The classification accuracy improves from the original 73.7% to 91.84%. The result shows that object-oriented classification technology is suitable for double polarization SAR’s high precision classification.

Cite this paper
nullX. Liu, Y. Li, W. Gao and L. Xiao, "Double Polarization SAR Image Classification based on Object-Oriented Technology," Journal of Geographic Information System, Vol. 2 No. 2, 2010, pp. 113-119. doi: 10.4236/jgis.2010.22017.
References
[1]   L. P. You, “Object Oriented Classification Method from High Resolution Remote Sensing Imagery [D],” Master thesis, Fujian Normal University, 2007.

[2]   C. B. Ursula, H. Peter, W. Gregor, et al., “Multi-Resolution, Object-Oriented Fuzzy Analysis of Remote Sensing Data for GIS-Ready Information [J].” ISPRS Journal of Photogrammetry & Remote Sensing, Vol. 58, 2004, pp. 239-258.

[3]   G. Z. Shen and J. J. Liao, “An Object Oriented Methodology for Automatic Analysis of Inundate Extent Using Multi-Polarized SAR Image [J],” Remote Sensing Technology And Application, Vol. 22, No. 1, 2007, pp. 79-82.

[4]   T. Michael, E. Thomas and D. Stefan, “Object-oriented Detection of Settlement Areas from TerraSAR-X DATA, Initial Reports [R],” Remote Sensing-New Challenges of High Resolution, 2008, pp. 242-248.

[5]   F. L. Chen, C. Wang and H. Zhang, “The Analysis of Single Polarization Synthetic Aperture Radar Images for the Application of Land-Use and Land-Cover Change [J],” Remote Sensing Technology and Application, Vol. 23, No. 3, 2008, pp. 289-293.

[6]   J. Qian, Q. M. Zhou and Q. Hou, “Comparison of Pixel- Based and Object-Oriented Classification Methods for Extracting Built-up Areas IN AridZone [C],” ISPRS Workshop on Updating Geo-Spatial Databases with Imagery & The 5th ISPRS Workshop on DMGISs, 2007, pp. 163-171.

[7]   L. Stanistaw, “Object-Oriented Classification of Landsat ETM+ Satellite Image [J],” Journal of Water and Land Development. No. 10, 2006, pp. 91-106.

[8]   Q. L. Tan, Z. G. Liu and W. Shen, “An Algorithm for Object-Oriented Multi-Scale Remote Sensing Image Segmentation [J],” Journal of Beijing Jiaotong University, Vol. 31, No. 4, 2007, pp. 111-114.

[9]   F. L. DU, Q. G. Tian and X. Q. Xia, “Object-Oriented Image Classification Analysis and Evaluation [J],” Remote Sensing Technology and Application, Vol. 19, No. 1, 2004, pp. 20-23.

[10]   C. Y. Zhou, “Object Oriented Information Extraction Technology from High Resolution Remote Sensing Imagery [D],” Master thesis of Shan Dong University of Science and Technology, 2006.

[11]   H. M. Zhang, Z. Z. Bian, et al., “A Novel Multi-Resolution Fuzzy Segmentation Method on MR Image [J],” Journal of Computer Science and Technology, Vol. 18, No. 5, 2003, pp. 659-666.

 
 
Top