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| Citation: | Sun Xiang, Song Hongjun, Wang Robert, Li Ning. POA Correction Method Using High-resolution Full-polarization SAR Image[J]. Journal of Radars, 2018, 7(4): 465-474. doi: 10.12000/JR18026
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POA Correction Method Using High-resolution Full-polarization SAR Image
doi: 10.12000/JR18026
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Abstract
Polarimetric decomposition in urban areas is important for monitoring the speed of city expansion and studying its ecological environmental influence. Using fully Polarimetric Synthetic Aperture Radar (PolSAR) is a method for consistent observation of large-range urban changes. In the last two decades, most research on decomposition methods have stated that Polarization Orientation Angle (POA) would affect the results of decomposition by overestimating the volume scattering contribution of urban areas. The available deorientation methods cannot rotate built-up areas with large POAs. This paper proposes an algorithm for decomposition of high-resolution urban area images based on a POA correction method. First, for high-resolution images of built-up areas, the POA changes radically pixel by pixel. An approximate assessment of urban areas can be accomplished using POA randomness. Then, to search for the true POA of large dominant POA areas (most built-up regions), the linear approximation method is used to locate POAs that can minimize cross-polarized terms. Thereby, the inaccurate decomposition that occurs by the deviation of POA can be fixed, and the accuracy of results improves. The fully PolSAR data of the Dujiangyan area in Sichuan Province, China are used to confirm the algorithm’s effectiveness. The data are acquired by an X-band airborne SAR sensor designed by the Institute of Electronics, China Academy of Sciences (IECAS). -
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References
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- Figure 1. Polarization orientation angle
- Figure 2. Polarimetric image of the Dujiangyan area, China
- Figure 3. The flowchart of POA correction method
- Figure 4. The images used to count OP in the first part of the flowchart
- Figure 6. Optical map of the Dujiangyan area, China
- Figure 5. Statistical graph of heterogeneous parameter
- Figure 7. Distribution diagram of POA in district A, B and C
- Figure 8. Distribution diagram of heterogeneous parameter in district A, B and C
- Figure 9. Approximate results of distinguished built-up areas with different threshold values of the heterogeneous parameter. (a)–(f) Show the results with the thresholds of 7 to 12, respectively
- Figure 10. POA of the Dujiangyan area calculated by the traditional method and the new method
- Figure 12. T33 of districts A, B and C after rotation using POAs calculated by the traditional method and the new method
- Figure 13. Pauli decomposition results and results of decomposition using POA correction method proposed in this paper of districts A, B and C, respectively
- Figure 11. Result of Dujiangyan image decomposition using POA correction method proposed in this paper