The Journal of Korean Institute of Electromagnetic Engineering and Science (한국전자파학회논문지)

The Korean Institute of Electromagnetic Engineering and Science (한국전자파학회)
권호 표지
DOI QR코드

DOI QR Code

Polarimetric SAR Image Classification Based on the Degree of Polarization and Co-Polarized Phase-Difference Statistics

편파화 정도와 동일 편파 위상 차를 이용한 SAR 영상 분류

  • Chang, Geba (Department of Electronic Information and Communication Engineering, Hongik University) ;
  • Oh, Yi-Sok (Department of Electronic Information and Communication Engineering, Hongik University)
  • 장지성 (홍익대학교 전자정보통신공학과) ;
  • 오이석 (홍익대학교 전자정보통신공학과)
  • Published : 2007.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposes a polarimetric SAR image classification technique based on the degree of poarization(DoP) and copolarized phase-difference(CPD) statistics. At first, the formulation for the DoP and CPD is derived. Then, the classification technique is verified with the SAR full polarimetric L-band data with consideration of exceptional cases. The technique has capability of classifying SAR data into four major classes, such as bare surface, short-vegetation canopy, tall-vegetation canopy, and village.

본 논문에서는 편파화 정도(Degree of Polarization: DoP)와 동일 편파 위상차(Co-polarized Phase-Difference: CPD)를 이용한 SAR 영상 분류법을 제안한다. 우선, 측정된 stokes 산란 operator로부터 DoP와 CPD를 얻는 계산식을 유도하고, SAR 영상 분류 과정을 설명한다. 다음에는 측정에서 얻은 완전 편파 L밴드 SAR 영상 데이터에 분류법을 적용하여 그 정확성을 검증하고, 예외 경우를 검토한다. 마지막으로 제안된 분류법으로 SAR 영상을 크게 4가지 그룹인 맨땅, 낮은 식물, 높은 식물, 주거 지역(마을)으로 분류한 결과를 보인다.

Keywords

References

  1. F. T. Ulaby, C. Elachi, Radar Polarimetry for Geoscience Applications, Artech House Remote Sensing Library, 1990
  2. S. R. Cloude, E. Pottier, 'An entropy based classification scheme for land applications of polarimetric SAR', IEEE Trans. Geosci. Remote Sensing, vol. 35, no. 1, pp. 68-78, Jan. 1997 https://doi.org/10.1109/36.551935
  3. A. Freeman, S. L. Durden, 'A three-component scattering model for polarimetric SAR data', IEEE Trans. Geosci. Remote Sensong, vol. 36, no. 3, pp. 963-973, May 1998 https://doi.org/10.1109/36.673687
  4. S. R. Cloude, E. Pottier, 'A review of target decomposition theorems in radar polarimetry', IEEE Trans., Geosci. Remote Sensing, vol. 34, no. 2, pp. 498-518, Mar. 1996 https://doi.org/10.1109/36.485127
  5. J. S. Lee, et al., 'Unsupervised classification using polarimetric decomposition and the complex wishart classifier', IEEE Trans. Geosci. Remote Sensing, vol. 37, no. 5, pp. 2249-2258, Sep. 1999 https://doi.org/10.1109/36.789621
  6. J. S. Lee, et al., 'Scattering-model-based speckle filtering of polarimetric SAR data', IEEE Trans. Geosci. Remote Sensing, vol. 44, no. 1, pp. 176- 187, Jan. 2006 https://doi.org/10.1109/TGRS.2005.859338
  7. F. T. Ulaby, K. Sarabandi, and A. Nashashibi, 'Statistical properties of the Mueller matrix of distributed targets', IEE Proceedings-F, vol. 139, no. 2, pp. 136-146, Apr. 1992
  8. G. Sinclair, 'The transmission and reception of elliptically polarized waves', Proc. IRE, pp. 148-151, 1950 https://doi.org/10.1109/JRPROC.1950.230106
  9. J. van Zyl, 'On the importance of polarization in radar scattering problems', Ph. D. dissertation, Calif. Inst. Technol., Pasadena, CA, Dec. 1985
  10. P. Elies, B. Le Jeune, P. Olivard, J. Cariou, and J. Lotrian, 'The application of de-polarization analysis to polarimetric characterization and classification of metallic and dielectric samples', J. Phys. D: Appl. Phys., pp. 2520-2529, 1997