基于多角度合成SAR图像的目标识别性能分析
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摘要
合成孔径雷达(synthetic aperture radar,SAR)在合成孔径累积时间内仅在小范围方位角获取目标的后向散射特性,导致SAR图像对观测方位向的变化极其敏感。通过图像非相干合成方法将不同方位向上的多幅同目标SAR图像合成单幅特征更明显的SAR图像,通过二维主成分分析方法提取特征和k-近邻分类方法实现目标识别。在两组不同数据集上对识别性能进行分析。实验结果表明,多角度SAR的识别率比单一角度更高。多角度SAR对观测平台俯视角的变化具有较强的鲁棒性。
Synthetic aperture radar(SAR) receives the backscatter of target within only a small range of azimuth during the synthetic aperture accumulation time, which makes the SAR image extremely sensitive to the change in observation azimuth. In this study, an SAR image with more obvious features is composed of multiple SAR images obtained at different azimuths by means of non-coherent composition, and then 2 DPCA is used to extract features and the k-nearest neighbor method is used for target recognition.Finally the recognition performance is analyzed on two different datasets. The experimental results show that the multi-aspect SAR has a higher recognition rate than the single aspect, and has strong robustness to the change in depression angle.
Synthetic aperture radar(SAR) receives the backscatter of target within only a small range of azimuth during the synthetic aperture accumulation time, which makes the SAR image extremely sensitive to the change in observation azimuth. In this study, an SAR image with more obvious features is composed of multiple SAR images obtained at different azimuths by means of non-coherent composition, and then 2 DPCA is used to extract features and the k-nearest neighbor method is used for target recognition.Finally the recognition performance is analyzed on two different datasets. The experimental results show that the multi-aspect SAR has a higher recognition rate than the single aspect, and has strong robustness to the change in depression angle.
引文
[1] 盖旭刚, 陈晋汶, 韩俊,等. 合成孔径雷达的现状与发展趋势[J]. 飞航导弹, 2011(3):82-86.
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[3] Moreira A, Prats-Iraola P, Younis M, et al. A tutorial on synthetic aperture radar[J]. IEEE Geoscience & Remote Sensing Magazine, 2013, 1(1):6-43.
[4] 洪文. 圆迹SAR成像技术研究进展[J]. 雷达学报, 2012, 1(2):124-135.
[5] 向卫力, 李晓辉, 周勇胜,等. 一种鲁棒的多尺度稀疏表示SAR目标识别方法[J]. 中国科学院大学学报, 2017, 34(1):99-105.
[6] O′Sullivan J A, Devore M D, Kedia V, et al. SAR ATR performance using a conditionally Gaussian model[J]. IEEE Transactions on Aerospace & Electronic Systems, 2001, 37(1):91-108.
[7] He Z, Lu J, Kuang G. A fast SAR target recognition approach using PCA features[C]//International Conference on Image and Graphics. Sichuan: IEEE, 2007:580-585.
[8] Tao M, Zhou F, Liu Y, et al. Tensorial independent component analysis-based feature extraction for polarimetric SAR data classification[J]. IEEE Transactions on Geoscience & Remote Sensing, 2015, 53(5):2 481-2 495.
[9] Anagnostopoulos G C. SVM-based target recognition from synthetic aperture radar images using target region outline descriptors[J]. Nonlinear Analysis, 2009, 71(12):2 934-2 939.
[10] Cui Z, Cao Z, Yang J, et al. SAR target recognition using nonnegative matrix factorization with L1/2 constraint[C]//Radar Conference. Cincinnati: IEEE, 2014:382-386.
[11] Huan R H, Zhang P, Pan Y. SAR target recognition using PCA, ICA and Gabor wavelet decision fusion[J]. Journal of Remote Sensing, 2012, 16(2):262-274.
[12] Lin C, Peng F, Wang B H, et al. Research on PCA and KPCA self-fusion based MSTAR SAR automatic target recognition algorithm[J]. Journal of Electronic Science and Technology, 2012, 10(4):352-357.
[13] Chen S, Wang H. SAR target recognition based on deep learning[C]//International Conference on Data Science and Advanced Analytics. IEEE, 2015:541-547.
[14] Zhan R H, Tian Z Z, Hu J M, et al. SAR automatic target recognition based on deep convolutional neural network[C]//2016 International Conference on Artificial Intelligence: Techniques and Applications. AITA, 2016:170-178.
[15] Brendel G F, Horowitz L L. Benefits of aspect diversity for SAR ATR: fundamental and experimental results[C]//Algorithms for Synthetic Aperture Radar Imagery VII. SPIE, 2000:567-578.
[16] Huan R H, Pan Y. Target recognition for multi-aspect SAR images with fusion strategies[J]. Progress in Electromagnetics Research, 2013, 134(1):267-288.
[17] Cao Z J, Xu L Y, Feng J L. Automatic target recognition with joint sparse representation of heterogeneous multi-view SAR images over a locally adaptive dictionary[J]. Signal Processing, 2016, 126(1):27-34.
[18] Yang J, Zhang D, Frangi A F, et al. Two-dimensional PCA: a new approach to appearance-based face representation and recognition[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2004, 26(1):131-137.
[19] Cover T, Hart P. Nearest neighbor pattern classification[J]. IEEE Transactions on Information Theory, 1967, 13(1):21-27.
[20] Solberg A H S, Jain A K, Taxt T. Multisource classification of remotely sensed data: fusion of Landsat TM and SAR images[J]. IEEE Transactions on Geoscience & Remote Sensing, 1994, 32(4):768-778.
[21] 阳方林, 郭红阳, 杨风暴. 像素级图像融合效果的评价方法研究[J]. 测试技术学报, 2002, 16(4):276-279.
[22] 李航. 统计学习方法[M]. 北京:清华大学出版社, 2012.
[23] Amoon M, Rezai-Rad G A. Automatic target recognition of synthetic aperture radar (SAR) images based on optimal selection of Zernike moments features[J]. Computer Vision IET, 2013, 8(2):77-85.
[24] Dong G, Kuang G. Classification on the monogenic scale space: application to target recognition in SAR image[J]. IEEE Transactions on Image Processing, 2015, 24(8):2 527-2 539.
[25] Anagnostopoulos G C. SVM-based target recognition from synthetic aperture radar images using target region outline descriptors[J]. Nonlinear Analysis, 2009, 71(12):2 934-2 939.
[26] Mishra A K. Validation of PCA and LDA for SAR ATR[C]//2008 IEEE Region 10 Conference. Hyderabad: IEEE, 2009:1-6.
[27] Cui Z Y, Cao Z J, Yang J Y. Target recognition in synthetic aperture radar images via non-negative matrix factorization[J], IET Radar Sonar & Navigation, 2015, 9(9):1 376-1 385.
[28] Zhang H, Nasrabadi N M, Zhang Y, et al. Multi-view automatic target recognition using joint sparse representation[J]. Aerospace & Electronic Systems IEEE Transactions, 2012, 48(3):2 481-2 497.
[2] 张红. 高分辨率SAR图像目标识别[M]. 北京:科学出版社, 2009.
[3] Moreira A, Prats-Iraola P, Younis M, et al. A tutorial on synthetic aperture radar[J]. IEEE Geoscience & Remote Sensing Magazine, 2013, 1(1):6-43.
[4] 洪文. 圆迹SAR成像技术研究进展[J]. 雷达学报, 2012, 1(2):124-135.
[5] 向卫力, 李晓辉, 周勇胜,等. 一种鲁棒的多尺度稀疏表示SAR目标识别方法[J]. 中国科学院大学学报, 2017, 34(1):99-105.
[6] O′Sullivan J A, Devore M D, Kedia V, et al. SAR ATR performance using a conditionally Gaussian model[J]. IEEE Transactions on Aerospace & Electronic Systems, 2001, 37(1):91-108.
[7] He Z, Lu J, Kuang G. A fast SAR target recognition approach using PCA features[C]//International Conference on Image and Graphics. Sichuan: IEEE, 2007:580-585.
[8] Tao M, Zhou F, Liu Y, et al. Tensorial independent component analysis-based feature extraction for polarimetric SAR data classification[J]. IEEE Transactions on Geoscience & Remote Sensing, 2015, 53(5):2 481-2 495.
[9] Anagnostopoulos G C. SVM-based target recognition from synthetic aperture radar images using target region outline descriptors[J]. Nonlinear Analysis, 2009, 71(12):2 934-2 939.
[10] Cui Z, Cao Z, Yang J, et al. SAR target recognition using nonnegative matrix factorization with L1/2 constraint[C]//Radar Conference. Cincinnati: IEEE, 2014:382-386.
[11] Huan R H, Zhang P, Pan Y. SAR target recognition using PCA, ICA and Gabor wavelet decision fusion[J]. Journal of Remote Sensing, 2012, 16(2):262-274.
[12] Lin C, Peng F, Wang B H, et al. Research on PCA and KPCA self-fusion based MSTAR SAR automatic target recognition algorithm[J]. Journal of Electronic Science and Technology, 2012, 10(4):352-357.
[13] Chen S, Wang H. SAR target recognition based on deep learning[C]//International Conference on Data Science and Advanced Analytics. IEEE, 2015:541-547.
[14] Zhan R H, Tian Z Z, Hu J M, et al. SAR automatic target recognition based on deep convolutional neural network[C]//2016 International Conference on Artificial Intelligence: Techniques and Applications. AITA, 2016:170-178.
[15] Brendel G F, Horowitz L L. Benefits of aspect diversity for SAR ATR: fundamental and experimental results[C]//Algorithms for Synthetic Aperture Radar Imagery VII. SPIE, 2000:567-578.
[16] Huan R H, Pan Y. Target recognition for multi-aspect SAR images with fusion strategies[J]. Progress in Electromagnetics Research, 2013, 134(1):267-288.
[17] Cao Z J, Xu L Y, Feng J L. Automatic target recognition with joint sparse representation of heterogeneous multi-view SAR images over a locally adaptive dictionary[J]. Signal Processing, 2016, 126(1):27-34.
[18] Yang J, Zhang D, Frangi A F, et al. Two-dimensional PCA: a new approach to appearance-based face representation and recognition[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2004, 26(1):131-137.
[19] Cover T, Hart P. Nearest neighbor pattern classification[J]. IEEE Transactions on Information Theory, 1967, 13(1):21-27.
[20] Solberg A H S, Jain A K, Taxt T. Multisource classification of remotely sensed data: fusion of Landsat TM and SAR images[J]. IEEE Transactions on Geoscience & Remote Sensing, 1994, 32(4):768-778.
[21] 阳方林, 郭红阳, 杨风暴. 像素级图像融合效果的评价方法研究[J]. 测试技术学报, 2002, 16(4):276-279.
[22] 李航. 统计学习方法[M]. 北京:清华大学出版社, 2012.
[23] Amoon M, Rezai-Rad G A. Automatic target recognition of synthetic aperture radar (SAR) images based on optimal selection of Zernike moments features[J]. Computer Vision IET, 2013, 8(2):77-85.
[24] Dong G, Kuang G. Classification on the monogenic scale space: application to target recognition in SAR image[J]. IEEE Transactions on Image Processing, 2015, 24(8):2 527-2 539.
[25] Anagnostopoulos G C. SVM-based target recognition from synthetic aperture radar images using target region outline descriptors[J]. Nonlinear Analysis, 2009, 71(12):2 934-2 939.
[26] Mishra A K. Validation of PCA and LDA for SAR ATR[C]//2008 IEEE Region 10 Conference. Hyderabad: IEEE, 2009:1-6.
[27] Cui Z Y, Cao Z J, Yang J Y. Target recognition in synthetic aperture radar images via non-negative matrix factorization[J], IET Radar Sonar & Navigation, 2015, 9(9):1 376-1 385.
[28] Zhang H, Nasrabadi N M, Zhang Y, et al. Multi-view automatic target recognition using joint sparse representation[J]. Aerospace & Electronic Systems IEEE Transactions, 2012, 48(3):2 481-2 497.