Since inverse synthetic aperture radar (ISAR) imaging is a valuable technique in the identification of space satellites, it can potentially detect interesting components of space satellites in ISAR images to further conduct identification. This study proposes a novel method, defined as feature probabilistic estimation (FPE), to detect interesting components of space satellites based on ISAR image registration. In FPE, area feature registration is provoked to establish the relationship between space satellites and off-line templates of interesting components, followed by detection accuracy based on weighted Gaussian probabilistic density function. Electromagnetic simulations with different aspects, interesting components' structures and scenery noise demonstrate the efficiency and robustness of the proposed FPE, compared with the normalised cross coefficient.

References

1. 1)
  - H. Bay , A. Ess , T. Tuytelaars , L.V. Gool . SURF: speeded up robust features. Comput. Vis. Image Underst. , 3 , 346 - 359
2. 2)
  - 4. Bentoutou, Y., Taleb, N., Bounoua, A., Kpalma, K., Ronsin, J.: ‘Feature based registration of satellite images’. 2007 15th Int. Conf. Digital Signal Processing, Cardiff, July 2007, pp. 419–422.
3. 3)
  - 1. Ulusoy, I., Yuruk, H.: ‘New method for the fusion of complementary information from infrared and visual images or object detection’, IET Image Process., 2010, 5, (1), pp. 36–48 (doi: 10.1049/iet-ipr.2009.0374).
4. 4)
  - 15. Jain, A.K., Ratha, N.K., Lakshmanan, S.: ‘Object detection using Gabor fllters’, Pattern Recognit., 1997, 30, (2), pp. 295–309 (doi: 10.1016/S0031-3203(96)00068-4).
5. 5)
  - 3. Sirmacek, B., Unsalan, C.: ‘Urban area detection using local feature points and spatial voting’, IEEE Geosci. Remote Sens. Lett., 2010, 7, (1), pp. 146–150 (doi: 10.1109/LGRS.2009.2028744).
6. 6)
  - D.G. Lowe . Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis , 2 , 91 - 110
7. 7)
  - 26. Mahmood, A., Khan, S.: ‘Correlation-coefficient-based fast template matching through partial elimination’, IEEE Trans. Image Process., 2012, 21, (4), pp. 2099–2108 (doi: 10.1109/TIP.2011.2171696).
8. 8)
  - 27. Li, Z., Qiu, H.: ‘Fast image matching based on correlation coefficient’, Trans. Beijing Inst. Technol., 2007, 27, (11), pp. 998–1000.
9. 9)
  - 6. Yang, Z., Cohen, F.S.: ‘Image registration and object recognition using affine invariants and convex hulls’, IEEE Trans. Image Process., 2002, 8, (7), pp. 934–946 (doi: 10.1109/83.772236).
10. 10)
  - 9. Sirmacek, B., Unsalan, C.: ‘A probabilistic framework to detect buildings in aerial and satellite images’, IEEE Trans. Geosci. Remote Sens., 2011, 49, (1), pp. 211–221 (doi: 10.1109/TGRS.2010.2053713).
11. 11)
  - M. Martorella . Novel approach for ISAR image cross-range scaling. IEEE Trans. Aerosp. Electron. Syst. , 1 , 281 - 294
12. 12)
  - C.M. Yeh , J. Xu , Y. Peng , X. Xia , X. Wang . Rotational motion estimation for ISAR via triangle pose difference on two range–Doppler images. IET Proc. Radar Sonar Navig. , 4 , 528 - 536
13. 13)
  - 16. Chum, O., Matas, J.: ‘Optimal randomized RANSAC’, IEEE Trans. Pattern Anal. Mach. Intell., 2008, 30, (8), pp. 1472–1482 (doi: 10.1109/TPAMI.2007.70787).
14. 14)
  - 25. Bilal, M., Masud, S.: ‘Efficient computation of correlation coefficient using negative reference in template matching applications’, IET Image Process., 2012, 6, (2), pp. 179–204.
15. 15)
  - 18. Wu, Y.H., Verd, S.: ‘Functional properties of minimum mean-square error and mutual information’, IEEE Trans. Inf. Theory., 2012, 58, (3), pp. 1289–1301 (doi: 10.1109/TIT.2011.2174959).
16. 16)
  - 7. Wu, J.Y., Lim, K.B., Tan, M.H.Y.: ‘Spectral technique to recognise occluded objects’, IET Image Process., 2012, 6, (2), pp. 160–170 (doi: 10.1049/iet-ipr.2010.0505).
17. 17)
  - 7. Wang, S.H., You, H.J., Fu, K.: ‘BFSIFT: a novel method to find feature matches for SAR image registration’, IEEE Geosci. Remote Sens. Lett., 2012, 9, (4), pp. 649–653 (doi: 10.1109/LGRS.2011.2177437).
18. 18)
  - 21. Peng, S.B., Xu, J., Peng, Y.N., Xiang, J.B.: ‘ISAR rotation velocity estimation based on phase slope difference of two prominent scatterers on complex image’, IET Radar, Sonar, Navig., 2011, 5, (9), pp. 1002–1009 (doi: 10.1049/iet-rsn.2010.0255).
19. 19)
  - 14. Harris, C., Stephens, M.: ‘A combined corner and edge detector’. Proc. Fourth Alvey Visual Conf., 1988, pp. 147–151.
20. 20)
  - 13. Yeh, C.M., Xu, J., Peng, Y.N., Wang, X.T., Yang, J., Xia, X.G.: ‘Cross-range scaling for ISAR via optical flow analysis’, IEEE A&E Syst. Mag., 2012, 27, (2), pp. 14–22 (doi: 10.1109/MAES.2012.6163609).
21. 21)
  - J.L. Walker . Range-Doppler imaging of rotating objects. IEEE Trans. Aerosp. Elecron. Syst. , 1 , 23 - 52
22. 22)
  - A. Mohan , C. Papageorgiou , T. Poggio . Example-based object detection in images by components. IEEE Trans. Pattern Anal. Mach. Intell. , 4 , 349 - 361
23. 23)
  - 2. Stansbery, E.G., Kessler, D.J., Tracy, T.E., Matney, M.J., Stanley, J.F.: ‘Characterization of the orbital debris environment from Haystack radar measurements’, Adv. Space Res., 1995, 16, (11), pp. 5–16 (doi: 10.1016/0273-1177(95)98748-D).
24. 24)
  - 17. Raguram, R., Chum, O., Pollefeys, M., Matas, J., Frahm, J.: ‘USAC: a universal framework for random sample consensus’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (8), pp. 2022–2038 (doi: 10.1109/TPAMI.2012.257).
25. 25)
  - 19. Lammers, U.H.W., Marr, R.A.: ‘Doppler imaging based on radar target precession’, IEEE Trans. Aerosp. Electron. Syst., 1993, 29, (1), pp. 166–173 (doi: 10.1109/7.249122).
26. 26)
  - 1. Mehrholz, D.: ‘Radar techniques for the characterization of meter-sized objects in space’, Adv. Space Res., 2001, 28, (9), pp. 1259–1268 (doi: 10.1016/S0273-1177(01)00395-7).
27. 27)
  - 5. Leung, M.K., Yang, Y.H.: ‘A region based approach for Human body motion analysis’, Pattern Recognit., 1987, 20, (3), pp. 321–339 (doi: 10.1016/0031-3203(87)90007-0).

Interesting components detection for space satellites from inverse synthetic aperture radar image via feature probabilistic estimation

References

Related content