volume | PIER Journals

Abstract

This paper addresses the problem of designing statistical features for the extraction of building-up areas (BAs) from highresolution polarimetric synthetic aperture radar (PolSAR) imagery. The idea is to represent a building-up area by the distribution of its mid-level components, called intermediates, which are statistical patterns unsupervisedly learnt from PolSAR images. More precisely, by analyzing the structural properties and the polarimetric characteristics exhibited in various terrain types, we propose two kinds of midlevel features for small regions: the cluster based statistical feature (CSF) and the scattering mechanism based statistical feature (SMSF). In detail, for the CSF, the intermediates are the K-mean clusters with Wishart distance of the PolSAR images; for the SMSF, the intermediates are the scattering mechanism categories obtained by relying on a four-component decomposition with deorientation of the PolSAR images. In contrast with existing features for describing BAs, the proposed features, i.e., CSF and SMSF, capture more complex context information of BAs. We compare the proposed features with those based on the Gaussian Markov random field (GMRF) models, which have been proven to be suitable for BAs mapping. Experimental results on RADARSAT-2 datasets demonstrate the effectiveness of the proposed features.

1. Soergel, U., Radar Remote Sensing of Urban Areas, 1st Ed., Springer, Heidelberg, 2010.

2. Stasolla, M. and P. Gamba, "Spatial indexes for the extraction of formal and informal human settlements from high-resolution SAR images," IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 1, No. 2, 98-106, 2008.
doi:10.1109/JSTARS.2008.921099

3. Evans, D. L. and J. J. Van Zyl, "Polarimetric imaging radar: Analysis tools and applications," Progress In Electromagnetics Research, Vol. 103, 371-389, 1990.

4. Jin, Y.-Q., "Polarimetric scattering modeling and information retrieval of SAR remote sensing --- A review of FDU work," Progress In Electromagnetics Research, Vol. 104, 333-384, 2010.
doi:10.2528/PIER10020101

5. Ferro-Famil, L. and E. Pottier, "Dual frequency polarimetric SAR data classification and analysis," Progress In Electromagnetics Research, Vol. 31, 247-272, 2001.
doi:10.2528/PIER00081601

6. Kong, J. A., S. H. Yueh, H. H. Lim, R. T. Shin, and J. J. Van Zyl, "Classification of earth terrain using polarimetric synthetic aperture radar images ," Progress In Electromagnetics Research, Vol. 3, 327-370, 1990.

7. Hu, H. T., Urban land-cover mapping with high-resolution spaceborne SAR data , Ph.D. dissertation, Geoiniformatics KTH, Sweden, Nov. 2010.

8. Niu, X., Multitemporal spaceborne polarimetric SAR data for urban land cover mapping , Ph.D. dissertation, Geoiniformatics KTH, Sweden, Feb. 2011.

9. Reigber, A., M. Jager, W. He, L. Ferro-Famil, and O. Hellwich, "Detection and classification of urban structures based on high-resolution SAR imagery," Urban Remote Sensing Joint Event, Paris, France, Apr. 11-13, 2007.

10. Chellappa, R. and S. Chatterjee, "Classification of textures using gaussian markov random fields," IEEE. Trans. Acoustics, Speech and Signal Processing, Vol. 33, 956-963, 1984.

11. Corbane, C., F. Faure, N. Baghdadi, N. Villeneuve, and M. Petit, "Rapid urban mapping using SAR/optical imagery synergy," Sensors, Vol. 8, No. 11, 7125-7143, 2008.
doi:10.3390/s8117125

12. Corbane, C., N. Baghdadi, X. Descombes, G. Wilson, N. Villeneuve, and M. Petit, "Comparative study on the performance of multiparameter SAR data for operational urban areas extraction using textural features," IEEE Geosci. Remote Sens. Letters, Vol. 6, No. 4, 728-732, 2009.
doi:10.1109/LGRS.2009.2024225

13. Yueh, S. H., J. A. Kong, J. K. Jao, R. T. Shin, H. A. Zebker, T. Le Toan, and H. Ottl, "K-distribution and polarimetric terrain radar clutter," Progress In Electromagnetics Research, Vol. 03, 237-275, 1990.

14. Lee, J. S., K. W. Hoppel, S. A. Mango, and A. R. Miller, "Intensity and phase statistics of multilook polarimetric and interferometric SAR imagery," IEEE Trans. Geosci. Remote Sens., Vol. 32, 1017-1028, Sept. 1994.

15. Lee, J. S., M. R. Grunes, and G. De Grandi, "Polarimetric SAR speckle filtering and its impact on terrain classification," IEEE Trans. Geosci. Remote Sens., Vol. 37, No. 5, 2363-2373, 1999.
doi:10.1109/36.789635

16. Freeman, A. and S. Durden, "A three-component scattering model for polarimetric SAR data," IEEE Trans. Geosci. Remote Sens., Vol. 36, No. 3, 963-973, 1998.
doi:10.1109/36.673687

17. Yamaguchi, Y., T. Moriyama, M. Ishido, and H. Yamada, "Four component scattering model for polarimetric SAR image decomposition," IEEE Trans. Geosci. Remote Sens., Vol. 43, No. 8, 1699-1706, 2005.
doi:10.1109/TGRS.2005.852084

18. Yamaguchi, Y., Y. Yajima, and H. Yamada, "A four-component decomposition of POLSAR images based on the coherency matrix," IEEE Geosci. Remote Sens. Lett., Vol. 3, No. 3, 292-296, 2006.
doi:10.1109/LGRS.2006.869986

19. Yajima, Y., Y. Yamaguchi, R. Sato, H. Yamada, and W. M. Boerner, "POLSAR image analysis of wetlands using a modified four-component scattering power decomposition," IEEE Trans. Geosci. Remote Sens., Vol. 46, No. 6, 1667-1673, 2008.
doi:10.1109/TGRS.2008.916326

20. An, W. T., Y. Cui, and J. Yang, "Three-component model-based decomposition for polarimetric SAR data," IEEE Trans. Geosci. Remote Sens., Vol. 48, No. 6, 2732-2739, 2010.
doi:10.1109/TGRS.2010.2041242

21. An, W. T., C. H. Xia, X. Z. Yuan, Y. Cui, and J. Yang, "Four-component decomposition of polarimetric SAR images with deorientation," IEEE Geosci. Remote Sens. Lett., Vol. 8, No. 6, 1090-1094, 2011.
doi:10.1109/LGRS.2011.2157078

22. Lee, J. S., M. R. Grunes, E. Pottier, and L. F. Famil, "Unsupervised terrain classification preserving polarimetric scattering characteristics," IEEE Trans. Geosci. Remote Sens, Vol. 42, No. 4, 722-731, 2004.
doi:10.1109/TGRS.2003.819883

23. Chinchor, N., "MUC-4 evaluation metrics," Proc. of the Fourth Message Understanding Conference, McLean, Virginia, Jun. 16-18, 1992.

24. Chang, C. C. and C. J. Lin, "LIBSVM: A library for support vector machines," 2001, Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.

Prof. Wen Yang

Dr. Ying Liu

Prof. Gui-Song Xia

Dr. Xin Xu