基于局部纹理特征的合成孔径雷达变体目标自动识别算法

摘要/Abstract

摘要： 提出了一种针对变体的识别算法,利用变体与原目标局部纹理之间的相似性进行识别。首先,提出了一种基于清晰边缘的合成孔径雷达(Synthetic aperture radar,SAR)图像配准算法;然后使用结合伽柏(Gabor)变换,局部二值模式(Local binary pattern,LBP)和空间区域直方图的纹理特征来描述SAR图像;最后用基于大特征的直方图序列的匹配做识别。基于MSTAR S2的试验结果证明了本算法的有效性。

关键词: 信息处理技术, 合成孔径雷达, SAR自动目标识别, 局部纹理特征, SAR目标变体

Abstract: A Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) algorithm for recognizing target variants is developed. This algorithm uses the local texture similarity between the variant and the original target for recognition. First, a SAR image registration algorithm based on clear edges is proposed. Then, the texture characteristic, which is obtained by combining the Gabor transform, LBP and spatial domain histogram, is employed to describe the SAR image. Finally, histogram sequence matching based on the large characteristic is used to perform recognition. The effectiveness of the proposed algorithm is verified by experimental results on MSTAR S2.

Key words: information processing, synthetic aperture radar(SAR), SAR automatic target recognition, local texture characteristic, SAR target variant

中图分类号:

TN911.73

尹奎英, 金林, 刘宏伟, 王英华. 基于局部纹理特征的合成孔径雷达变体目标自动识别算法 [J]. , 2012, (03): 743-748.

YIN Kui-ying, JIN Lin, LIU Hong-wei, WANG Ying-hua. SAR variant target automatic recognition algorithm based on local texture characteristic[J]. , 2012, (03): 743-748.

参考文献

[1] Ross T D, Bradley J J, Hudson L J, et al. SAR ATR-so what's the problem?-an MSTAR perspective［C］//Algorithm for Synthetic Aperture Radar ImageryⅥ, Orlando FL,1999,3721:662-672.
[2] Yuan C,Casasent D P. A new SVM for distorted SAR object classification［C］//Proc of SPIE on Optical Pattern Recognition XVI, Bellingham WA,2005,5816:10-22.
[3] Zhao Q, Principe J C. Support vector machines for SAR automatic target recognition［J］. IEEE Trans on Aerospace and Electronic Systems,2001,37(2):643-654.
[4] Bryant M L. Target signature manifold methods applied to the MSTAR database: preliminary results［C］//Proc of SPIE on Synthetic Aperture Radar Imagery VIII,USA,2001,4382:389-394.
[5] Sun Y J, Liu Z P, Todorovic S, et al. Adaptive boosting for SAR automatic target recognition［J］. IEEE Trans on Aerospace and Electronic Systems, 2007:43(1):112-125.
[6] Blacknell D, Nicholas S, QinetiQ A. Morphological models for SAR ATR［J］. Algorithm for Synthetic a Perture Radar Imagery Ⅸ,2002,4727:69-79.
[7] Huan Ruo-hong, Yang Ru-liang, Yue-Jin. A new method for synthetic aperture radar images feature extraction and target recognition［J］. Journal of Electronics and Information Technology,2008,30(3):554-558.
[8] Han Ping, Wu Ren-biao, Wang Zhao-hua, et al. SAR automatic target recognition based on KPCA criterion［J］. Journal of Electronics and Information Technology,2003,25(10):1297-1301.
[9] Hu Li-ping, Liu Hong-wei, Wu Shun-jun. SAR target feature extraction and recognition based on two-stage 2DPCA［J］. Journal of Electronics and Information Technology,2008,30(7):1723-1726.
[10] Yin Kui-ying, Hu Li-ping, Liu Hong-wei, et al. A composite enhancing and denoising algorithm for SAR images［J］. Journal of Xidian University,2010,37(2):224-230.
[11] Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990,12(7):629-639.
[12] Otsu N. A threshold selection method from Gray2level histogram［J］. IEEE Transactions on Systems, Man, and Cybernetics, 1979,9(1):62-66.
[13] 尹奎英,刘宏伟,金林.快速的Otsu双阈值SAR图像分割法［J］.吉林大学学报:工学版,2011,41(6):1760-1765. Yin Kui-ying, Liu Hong-wei, Jin Lin. Fast SAR image segmentation method based on Ostu adaptive double threshold［J］. Journal of Jilin University(Engineering and Technology Edition),2011,41(6):1760-1765.
[14] 张文超,山世光,张洪明,等. 基于局部Gabor变化直方图序列的人脸描述与识别［J］. 软件学报,2006,17(12):2508-2517. Zhang Wen-Chao, Shan Shi-Guang, Zhang Hong-ming,et al. Histogram sequence of local gabor binary pattern for face description and identification［J］. Journal of Software,2006,17(12):2508-2517.
[15] Xie X D, Lam K M. Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image［J］. IEEE Trans on Image Processing,2006,15(9):2481-2492.
[16] Ojala T, Pietikäinen M, Mäenpää T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns［J］. IEEE Trans on Pattern Analysis and Machine Intelligence,2002,24(7):971-987.

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