Target Aspect Estimation in SAR Imagery Based on Hough Transform and Major Axis Extraction

Xu Mu; Wang Xue-song; Xiao Shun-ping

doi:10.3724/SP.J.1146.2005.00562

Volume 29 Issue 2

Jan. 2011

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Article Navigation > Journal of Electronics & Information Technology > 2007 > 29(2): 370-374

Xu Mu, Wang Xue-song, Xiao Shun-ping. Target Aspect Estimation in SAR Imagery Based on Hough Transform and Major Axis Extraction[J]. Journal of Electronics & Information Technology, 2007, 29(2): 370-374. doi: 10.3724/SP.J.1146.2005.00562

Citation:

Xu Mu, Wang Xue-song, Xiao Shun-ping. Target Aspect Estimation in SAR Imagery Based on Hough Transform and Major Axis Extraction[J]. Journal of Electronics & Information Technology, 2007, 29(2): 370-374. doi: 10.3724/SP.J.1146.2005.00562

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Target Aspect Estimation in SAR Imagery Based on Hough Transform and Major Axis Extraction

doi: 10.3724/SP.J.1146.2005.00562

Received Date: 2005-05-18
Rev Recd Date: 2005-11-22
Publish Date: 2007-02-19

Abstract

Abstract

A novel method of target aspect estimation in SAR imagery based on Hough transform and major axis extraction is proposed here, which fully exploits the information contained in the major axis of the target. Firstly, the accurate contour of the target in SAR imagery is extracted after the image segmentation based on Markov Random Field(MRF)model. Secondly, the straight lines on the contour are found by Hough transform and the major axis is extracted by the detection of feature points on the contour. Finally, a fine estimation can be acquired by the combination of estimation results from both Hough transform and major axis extraction. The validity of the method is indicated by experimental results with real SAR data sets.

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References(1)

References

[1] Ross Timothy D, Bradley Jeffrey J, and Hudson Lanson J, et al.. SAR ATR-So What's the problem?-An MSTAR perspective[A]. In: Algorithm for Synthetic Aperture Radar Imagery VI[C], Orlando, FL, SPIE, 1999, 3721: 662-672. [2] Grinnell J, and Bhanu B. Recognition of articulated and occluded objects[J].IEEE Trans. on Pattern Analysis and Machine Intelligence.1999, 21(7):603-613 [3] Principe J, Zhao Q, and Xu X. A novel ATR classifier exploiting pose information. In: Proceedings of Image Understanding Workshop, Monterey, CA, 1998: 833-836. [4] Principe J, Xu D, and Fisher III. Pose estimation in SAR using an information theoretic criterion[J].SPIE.1998, 3370:218-229 [5] Meth R. Target/ Shadow segmentation and aspect estimation in synthetic aperture radar imagery [A][J].SPIE.1998, 3370:188-196 [6] Voicu L, Patton R, and Harley R M. Multi-criterion vehicle pose estimation for SAR ATR. SPIE, 1999, 3321: 497-506. [7] Geman S and Geman D. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images[J].IEEE Trans. on Pattern Analysis and Machine Intelligence.1984, 6:721-741 [8] 孙即祥. 数字图像处理. 石家庄: 河北教育出版社, 1993, 260-263. [9] Rosenfeld A and Johnston E. Angle detection on digital curves[J].IEEE Trans. on Computers.1973, 22(9):875-878 [10] Williams D J and Shah M. A fast algorithm for active contour and curvation estimation[J].CVGIP Image Understanding.1992, 55(1):14-26 [11] Weisenseel R A, Karl W, and David A, et al.. Markov random field segmentation methods for SAR target chips. In: Algorithms for synthetic aperture radar imagery V. SPIE, 1999, 3721: 462-473. [12] Power G J and Weisenseel R A. ATR subsystem performance measures using manual segmentation of SAR target chips. In: Algorithms for synthetic aperture radar imagery VI, SPIE, 1999, 3721: 685-692. [13] Frery A C. Segmentation algorithms: a quantitative assessment. In: International Geoscience and Remote Sensing Symposium. Hamburgo, Alemanha, 1999: 1265-1267.

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