A new scheme for pixel-based polarimetric synthetic aperture radar (PolSAR) classification of the urban area was proposed. First, the characteristic of urban backscattering was analysed and it was found that the backscattering of buildings is very sensitive to the orientation of buildings. Second, by utilising Euler rotation to the polarimetric coherency matrix, a sequence of data with different rotation angles was simulated. Then a polarimetric statistical feature vector would be extracted from the simulated data. At last, the feature vector together with four components decomposition result would be put into a multiple layer perceptron neural network to get the classification result. The proposed scheme can improve the accuracy of urban area classification in a PolSAR image and be verified by using AIRSAR image data of San Francisco.

References

1. 1)
  - 5. Iwasa, S., Susaki, J.: ‘Classification of building area using azimuth angle and density indices derived from polarimetric SAR’. Urban Remote Sensing Event, Munich, Germany, April 2011, pp. 269–272.
2. 2)
  - 9. Qinghui, F., Deliang, X., Chongyuan, F.: ‘New polarimetric coherence parameter for built-up areas in PolSAR imagery’, Electron. Lett., 2018, 54, (3), pp. 159–161 (doi: 10.1049/el.2017.3299).
3. 3)
  - 2. Shang, F., Hirose, A.: ‘Polarimetric-basis transformation for land classification in PolInSAR’, Electron. Lett., 2013, 49, (1), pp. 69–70 (doi: 10.1049/el.2012.3376).
4. 4)
  - 3. Lee, K.-Y., Oh, Y., Kim, Y.: ‘Phase-difference of urban area in polarimetric SAR images’, Electron. Lett, 2012, 48, (21), pp. 1367–1368 (doi: 10.1049/el.2012.1698).
5. 5)
  - 8. Yamaguchi, Y., Sato, A., Boerner, W.-M., et al: ‘Four-component scattering power decomposition with rotation of coherency matrix’, Trans. Geosci. Remote Sens., 2010, 49, (6), pp. 2251–2258 (doi: 10.1109/TGRS.2010.2099124).
6. 6)
  - 4. Lee, J.S., Ainsworth, T.L.: ‘The effect of orientation angle compensation on coherency matrix and polarimetric target decompositions’, Trans. Geosci. Remote Sens., 2011, 49, (1), pp. 53–64 (doi: 10.1109/TGRS.2010.2048333).
7. 7)
  - 7. Yamaguchi, Y., Yajima, Y., Yamada, H., et al: ‘A four-component decomposition of POLSAR images based on the coherency matrix’, Geosci. Remote Sens. Lett., 2006, 3, (3), pp. 292–296 (doi: 10.1109/LGRS.2006.869986).
8. 8)
  - 3. Franceschetti, G., Iodice, A., Riccio, D.: ‘A canonical problem in electromagnetic backscattering from buildings’, Trans. Geosci. Remote Sens., 2002, 40, (8), pp. 1787–1801 (doi: 10.1109/TGRS.2002.802459).
9. 9)
  - 6. Chen, S.W., Ohki, M., Shimada, M., et al: ‘Deorientation effect investigation for model-based decomposition over oriented built-up areas’, Geosci. Remote Sens. Lett., 2013, 10, (2), pp. 273–277 (doi: 10.1109/LGRS.2012.2203577).

Urban area classification with polarimetric statistical features of simulated data in PolSAR images

References

Related content