The primitive type attribute as recently reported in the spectrum parted linked image test algorithm is used for the first time in a 10-vehicle classification experiment. Attributed scattering centres (ASCs) from wideband polarimetric synthetic aperture radar imagery are surveyed and two highly complementary sets of attributes are compared in a nearest neighbour classifier. The classifier performance for each set of attributes is shown to be over 90% with 10° sub-apertures and 10 dB additive white Gaussian noise which had not been considered in earlier works. Using a common image formation and ASC extraction method to ensure that only the pixel attributes differed, two different sets of complementary attributes are compared under precisely the same conditions. In addition, the query sets of attributed images are formed from azimuth and elevation angles that are not in the set of training angles. The results show that the classifier performance degrades gracefully as the signal-to-noise ratio (SNR) decreases below 10 dB and that the sensitivity to aspect angle is nearly the same for all vehicle classes as the SNR approaches 10 dB and above. The primary limitation of the approach is the use of wide-band, wide-aperture, and polarimetric radar data.

References

1. 1)
  - 5. Solberg, A.: ‘Remote sensing of ocean oil-spill pollution’, Proc. IEEE, 2012, 100, (10), pp. 2931–2945 (doi: 10.1109/JPROC.2012.2196250).
2. 2)
  - 22. Fuller, D., Saville, M.A.: ‘A high-frequency multipeak model for wide-angle SAR imagery’, IEEE Trans. Geosci. Remote Sens., 2013, 51, (7), pp. 4279–4291 (doi: 10.1109/TGRS.2012.2226732).
3. 3)
  - 16. Potter, L., Moses, R.: ‘Attributed scattering centers for SAR ATR’, IEEE Trans. Image Process., 1997, 6, (1), pp. 79–91 (doi: 10.1109/83.552098).
4. 4)
  - 3. Wang, C., Yu, W., Wang, R., et al: ‘Unsupervised classification based on non-negative eigenvalue decomposition and Wishart classifier’, IET Radar Sonar Navig., 2014, 8, (8), pp. 957–964 (doi: 10.1049/iet-rsn.2014.0076).
5. 5)
  - 15. Dungan, K.E., Potter, L.: ‘Classifying transformation-variant attributed point patterns’, Pattern Recogn., 2010, 43, (11), pp. 3805–3816 (doi: 10.1016/j.patcog.2010.05.033).
6. 6)
  - 29. Yang, J., Zhang, D., Frangi, A., et al: ‘Two-dimensional PCA: a new approach to appearance-based face representation and recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2004, 6, pp. 131–137 (doi: 10.1109/TPAMI.2004.1261097).
7. 7)
  - 30. Cheriyadat, A., Bruce, L.M.: ‘Why principal component analysis is not an appropriate feature extraction method for hyperspectral data’. IGARSS ’03 Proc. 2003 IEEE Int. Geoscience and Remote Sensing Symp.,2003, vol. 6, pp. 3420–3422 (doi: 10.1109/IGARSS.2003.1294808).
8. 8)
  - 24. Bhalla, R., Moore, J., Ling, H.: ‘A global scattering center representation of complex targets using the shooting and bouncing ray technique’, IEEE Trans. Antennas Propag., 1997, 45, (12), pp. 1850–1856 (doi: 10.1109/8.650204).
9. 9)
  - 2. Wang, T.-L., Jin, Y.-Q.: ‘Post-earthquake building damage assessment using multi-mutual information from pre-event optical image and post-event SAR image’, IEEE Geosci. Remote Sensi. Lett., 2012, 9, (3), pp. 452–456 (doi: 10.1109/LGRS.2011.2170657).
10. 10)
  - 12. Tang, T., Su, Y.: ‘Object recognition based on feature matching of scattering centers in SAR imagery’. 2012 Fifth Int. Congress on Image and Signal Processing (CISP), October 2012, pp. 1073–1076.
11. 11)
  - 27. Liu, X., Huang, Y., Pei, J., et al: ‘Sample discriminant analysis for SAR ATR’, IEEE Geosci. Remote Sensi. Lett., 2014, 11, (12), pp. 2120–2124 (doi: 10.1109/LGRS.2014.2321164).
12. 12)
  - 6. Yu, J.H., Ge, L., Li, X.: ‘Radargrammetry for digital elevation model generation using Envisat reprocessed image and simulation image’, IEEE Geosci. Remote Sensi. Lett., 2014, 11, (9), pp. 1589–1593 (doi: 10.1109/LGRS.2014.2301731).
13. 13)
  - 26. Statnikov, A., Aliferis, C.F., Hardin, D.P., et al: ‘A gentle introduction to support vector machines in biomedicine: case studies’ (World Scientific Publishing Co., Inc., River Edge, NJ, USA, 2011, 1st edn.).
14. 14)
  - 17. Bhalla, R., Ling, H.: ‘Three-dimensional scattering center extraction using the shooting and bouncing ray technique’, IEEE Trans. Antennas Propag., 1996, 44, (11), pp. 1445–1453 (doi: 10.1109/8.542068).
15. 15)
  - 18. Jackson, J., Rigling, B., Moses, R.: ‘Canonical scattering feature models for 3D and bistatic SAR’, IEEE Tans. Aerosp. Electron. Syst., 2010, 46, (2), pp. 525–541 (doi: 10.1109/TAES.2010.5461639).
16. 16)
  - 15. Guo, Z., Li, S.: ‘One-dimensional frequency-domain features for aircraft recognition from radar range profiles’, IEEE Trans. Aerosp. Electron. Syst., 2010, 46, (4), pp. 1880–1892 (doi: 10.1109/TAES.2010.5595601).
17. 17)
  - 10. Xing, X., Ji, K., Zou, H., et al: ‘Ship classification in Terrasar-X images with feature space based sparse representation’, IEEE Geosci. Remote Sensi. Lett., 2013, 10, (6), pp. 1562–1566 (doi: 10.1109/LGRS.2013.2262073).
18. 18)
  - 19. Ertin, E., Moses, R.: ‘Through-the-wall SAR attributed scattering center feature estimation’, IEEE Trans. Geosci. Remote Sens., 2009, 47, (5), pp. 1338–1348 (doi: 10.1109/TGRS.2008.2008999).
19. 19)
  - 1. Byun, Y., Choi, J., Han, Y.: ‘An area-based image fusion scheme for the integration of SAR and optical satellite imagery’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2013, 6, (5), pp. 2212–2220 (doi: 10.1109/JSTARS.2013.2272773).
20. 20)
  - 13. Farmer, J., Saville, M.A.: ‘Effects of local peak detection on attributed scattering centre extraction in SAR’, IET Electron. Lett., 2015, 51, (8), pp. 646–648 (doi: 10.1049/el.2015.0388).
21. 21)
  - 31. Dungan, K.E., Austin, C., Nehrbass, J., et al: ‘Civilian vehicle radar data domes’, Proc. SPIE, Algorithms Synth. Aperture Radar Imagery XVII, 2010, 7699, pp. 1–12.
22. 22)
  - 25. Park, J.-I., Park, S.-H., Kim, K.-T.: ‘New discrimination features for SAR automatic target recognition’, IEEE Geosci. Remote Sensi. Lett., 2013, 10, (3), pp. 476–480 (doi: 10.1109/LGRS.2012.2210385).
23. 23)
  - 23. Ross, T.D., Bradley, J.J., Hudson, L.J., et al: ‘SAR ATR: So what's the problem? an MSTAR perspective’, Proc. SPIE, Algorithms Synth. Aperture Radar Imagery XVII, 1999, 3721, pp. 662–672.
24. 24)
  - 21. Dungan, K.E., Potter, L.: ‘Classifying vehicles in wide-angle radar using pyramid match hashing’, IEEE J. Sel. Top. Signal Process., 2011, 5, (3), pp. 577–591 (doi: 10.1109/JSTSP.2010.2085420).
25. 25)
  - 22. Zhou, J., Zhiguang, S., Cheng, X., Fu, Q.: ‘Automatic target recognition of SAR images based on global scattering center model’, IEEE Trans. Geosci. Remote Sens., 2011, 49, (10), pp. 3713–3729 (doi: 10.1109/TGRS.2011.2162526).
26. 26)
  - 7. Huan, R., Pan, Y.: ‘Decision fusion strategies for SAR image target recognition’, IET Radar Sonar Navig., 2011, 5, (7), pp. 747–755 (doi: 10.1049/iet-rsn.2010.0319).
27. 27)
  - 11. Bhanu, B.: ‘Recognizing occluded objects in SAR images’, IEEE Tans. Aerosp. Electron. Syst., 2001, 37, (1), pp. 316–328 (doi: 10.1109/7.913694).
28. 28)
  - 28. Ye, J., Janardan, R., Liand, Q.: ‘Two-dimensional linear discriminant analysis’, Adv. Neural Inf. Process. Syst., 2005, 17, pp. 1569–1576.
29. 29)
  - 14. Saville, M.A., Jackson, J., Fuller, D.: ‘Rethinking vehicle classification with wide-angle polarimetric SAR’, , IEEE Aerosp. Electron. Syst. Mag., 2014, 29, (1), pp. 41–49 (doi: 10.1109/MAES.2014.130057).
30. 30)
  - 4. Chen, S., Wang, H., Xu, F., et al: ‘Automatic recognition of isolated buildings on single-aspect SAR image using range detector’, IEEE Geosci. Remote Sensi. Lett., 2015, 12, (2), pp. 219–223 (doi: 10.1109/LGRS.2014.2327125).
31. 31)
  - 7. Luettel, T., Himmelsbach, M., Wuensche, H.-J.: ‘Autonomous ground vehicles –concepts and a path to the future’, Proc. IEEE, 2012, 100, (Special Centennial Issue), pp. 1831–1839 (doi: 10.1109/JPROC.2012.2189803).

Commercial vehicle classification from spectrum parted linked image test-attributed synthetic aperture radar imagery

References

Related content