基于压缩感知技术的SAR原始回波数据压缩方法研究
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摘要
合成孔径雷达(Synthetic Aperture Radar ,SAR)作为一种高分辨率成像雷达,正向多频段、多极化,超高分辨率以及多种工作模式方向发展,SAR原始数据量随之急剧增加,带来的是数据的传输和存储难题。对SAR原始数据进行有效压缩,是解决这一难题的一种有效方法。本文的工作就是对一种新的SAR原始数据压缩算法-基于压缩感知理论的SAR原始回波数据压缩算法进行研究。
本论文首先分析了SAR原始回波数据的特点,包括统计特性,频谱特性,相关特性,介绍了量化编码基本知识,回顾了两种经典的SAR原始回波数据压缩方法——分块自适应量化编码算法和将小波变换和分块自适应量化编码(BAQ)结合在一起的基于小波变换SAR原始回波数据压缩算法,利用两种方法对SAR原始回波进行压缩,给出了详细方案并对实验结果作出了评价。
接着重点介绍了最新的压缩感知理论,通过分析给出了SAR信号的稀疏表示形式,以及压缩感知理论运用于SAR原始回波数据的适用条件。在研究将压缩感知理论运用于SAR原始回波数据压缩时,考虑了原始数据的选取,测量值的获取,信号的恢复等问题。将SAR成像算法看作是一种变换基,和压缩感知理论相结合,实现了基于压缩感知理论的SAR原始回波数据压缩,并将其同传统的两种压缩方法作比较。
实验结果表明将压缩感知理论运用于SAR原始回波数据压缩,能够在有效降低SAR原始回波数据率的同时,依然能保留图像中大部分重要目标信息。
With the development of Synthetic aperture radar (SAR) into multi-band, multi-polarization, and varieties of modes of ultra-high resolution imaging radar, the main problem increased is the huge amount of raw data for transmission and storage. Effective compression on the SAR raw data, is one of the effective solutions to this problem. This dissertation is focus on a new SAR raw data compression algorithm - based on compressed sensing theory of SAR raw data compression.
Firstly, this dissertation analyzes the characteristics of SAR raw data, including statistical characteristics, spectrum, correlation, introduces the quantitative coding, reviews of two classic SAR raw data compression algorithms. In this dissertation, a block adaptive quantization coding algorithm and the wavelet transform with block adaptive quantization coding combination algorithm- SAR raw data compression based on wavelet transform are studied. Then carries on effective compression for SAR Raw data and gives a detailed appraisal by offering some evaluation results.
Then, the dissertation introduces latest theoretical-compressed sensing framework, analyzes the SAR signal’s sparse representation and conditions compressed sensing theory is used in the application of SAR raw data .In the study on the compression of SAR raw data compression based on compressed sensing, we consider the selection of the original data, data collection and recovery. SAR imaging algorithm will be seen as a transformation base here. Combining the SAR imaging algorithm and theory of compressed sensing, we achieve effective compression of SAR raw data compression based on compressed sensing with comparison with two classic algorithms.
Experimental results show that SAR raw data compression based on the compressed sensing, can remain most important retain target information of the image with efficient data reduction.
本论文首先分析了SAR原始回波数据的特点,包括统计特性,频谱特性,相关特性,介绍了量化编码基本知识,回顾了两种经典的SAR原始回波数据压缩方法——分块自适应量化编码算法和将小波变换和分块自适应量化编码(BAQ)结合在一起的基于小波变换SAR原始回波数据压缩算法,利用两种方法对SAR原始回波进行压缩,给出了详细方案并对实验结果作出了评价。
接着重点介绍了最新的压缩感知理论,通过分析给出了SAR信号的稀疏表示形式,以及压缩感知理论运用于SAR原始回波数据的适用条件。在研究将压缩感知理论运用于SAR原始回波数据压缩时,考虑了原始数据的选取,测量值的获取,信号的恢复等问题。将SAR成像算法看作是一种变换基,和压缩感知理论相结合,实现了基于压缩感知理论的SAR原始回波数据压缩,并将其同传统的两种压缩方法作比较。
实验结果表明将压缩感知理论运用于SAR原始回波数据压缩,能够在有效降低SAR原始回波数据率的同时,依然能保留图像中大部分重要目标信息。
With the development of Synthetic aperture radar (SAR) into multi-band, multi-polarization, and varieties of modes of ultra-high resolution imaging radar, the main problem increased is the huge amount of raw data for transmission and storage. Effective compression on the SAR raw data, is one of the effective solutions to this problem. This dissertation is focus on a new SAR raw data compression algorithm - based on compressed sensing theory of SAR raw data compression.
Firstly, this dissertation analyzes the characteristics of SAR raw data, including statistical characteristics, spectrum, correlation, introduces the quantitative coding, reviews of two classic SAR raw data compression algorithms. In this dissertation, a block adaptive quantization coding algorithm and the wavelet transform with block adaptive quantization coding combination algorithm- SAR raw data compression based on wavelet transform are studied. Then carries on effective compression for SAR Raw data and gives a detailed appraisal by offering some evaluation results.
Then, the dissertation introduces latest theoretical-compressed sensing framework, analyzes the SAR signal’s sparse representation and conditions compressed sensing theory is used in the application of SAR raw data .In the study on the compression of SAR raw data compression based on compressed sensing, we consider the selection of the original data, data collection and recovery. SAR imaging algorithm will be seen as a transformation base here. Combining the SAR imaging algorithm and theory of compressed sensing, we achieve effective compression of SAR raw data compression based on compressed sensing with comparison with two classic algorithms.
Experimental results show that SAR raw data compression based on the compressed sensing, can remain most important retain target information of the image with efficient data reduction.
引文
1 Ronald Kwok, William T.K. Johnson. Block Adaptive Quantization of Magellan SAR Data. IEEE Transactions on Geosciences and Remote Sensing. 1989, 27(4): 375~383
2 U. Benz, K. Strodl, A. Moreira. A Comparison of Several Algorithms of SAR Raw Data Compression. IEEE Transactions on Geosciences and Remote Sensing. 1995, 33(5): 1266~1276
3曹鹏志,许荣庆,刘永坦.块浮点量化(BFPQ)在星载合成孔径雷达回波数据压缩中的应用.哈尔滨工业大学学报, 1997, 29(3): 91~95
4 T.Algra. Compression of Raw SAR Data Using Entropy-constrained Quantization. 2000 Internaitonal Geoscience and Remote Sensing Symposium, Honolulu, United States, 2000: 2660~2662
5 D. Lebedeff, P. Mathieu, M. Barlaud, C.Lambert-Nebout, and P.Bellemain. Adaptive Vector Quantization of Raw SAR Data. IEEE ICASSP, Detroit, United States, 1995: 2511~2514
6 Ling Ting, Wang Dongjin, Liu Falin. A Fast BAVQ Algorithm for SAR Raw Data Compression. CIE International Conference of Radar Proceedings, Shanghai, China, 2006
7王函,娄晓光. BAVQ压缩算法应用于SAR原始数据压缩——最佳矢量维数的选择方法.科学技术与工程. 2009, 9(14): 4024~4026
8杨云志,黄顺吉,王建国. SAR原始数据矢量量化的码书改进研究.现代雷达. 2007, 29(11): 60~63
9 Fischer, Jens, Benz, Ursula, Moreira, Alberto. Efficient SAR Raw Data Compression in Frequency Domain. IEEE IGARSS, Hamburg , Germany, 1999: 2261~2263
10宋鸿梅,王岩飞,潘志刚.基于FFT-BAQ的SAR原始数据压缩新算法.系统工程与电子技术. 2009, 31(11): 2613~2617
11宋鸿梅,王岩飞,潘志刚.基于DCT-TCQ的SAR原始数据压缩算法.电子与信息学报. 2010, 32(5): 1040~1044
12 D’Elia. C, Poqqi. G, Verdoliva. L. Compression of SAR Raw Data Through Range Focusing and Variable-Rate Trellis-Coded Quantization. IEEE Transactions on Geosciences Remote Sensing. 2000, 3(9): 1282~1288
13 Giovanni Poggi et al. On-board Compression of SAR Data Through Range Focusing. IEEE Transactions on Geosciences Remote Sensing, 1999: 2247~2250
14 Giovnani Pogg ie al. Compression of SAR Data Via Range Focusing and Trellis Coded Quantization. IEEE IGARSS 2000, Honolulu, United States, 2000: 2642~2644
15曾尚春,朱兆达.一种SAR原始数据的变换域编码算法.遥感学报. 2008, 12(3): 392~397
16 Mei Zhou, Yunkai, Deng Zhimin, Zhang Lingli, Tang Chuanrong. A Comparison of Several Raw Data Compression Algorithms for Acquisition of Remotely Sensed Data. Geoinformatics 2008 and Joint Conference on GIS and Built Environment, Guangzhou, China, 2008
17 El Boustani, A. Brunham, K. Kinsner, W. An Optimal Wavelet for Raw SAR Data Compression. Canadian Conference on Electrical and Computer Engineering, Montreal, Canada, 2003: 2071~2074,
18 El-Boustani, A. Brunham, K. Kinsner, W. Investigation of Wavelets for Raw SAR Data Compression. 2003 IGARSS: Learning From Earth's Shapes and Colours, Toulouse, France, 2003: 1814~1816
19 El-Boustani, A.Turiel, A. Huot, E. Brunham, K. Kinsner, W. Wavelet Transform Based Compression Techniques for Raw SAR Data. 2002 IEEE Canadian Conference on Electrical and Computer Engineering, Winipeg and Manitoba, Canada, 2002: 857~862
20曾尚春,朱兆达.小波变换块自适应量化算法压缩SAR原始数据.遥感学报. 11(4): 481~486
21 E. Magli,G. Olmo,and B. Penna. Wavelet-based Compression of SAR Raw Data. IEEE IGARSS 2002, Toronto, Canada, 2002: 1129~1131
22 Tammana, Gowtham A. Zheng, Yuan F. Ewing. Robert L. Synthetic Aperture Radar Raw Data Compression Using Wavelet Packet Transform and Trellis Coded Quantization. IEEE International 48th Midwest Symposium on Circuits and Systems, Cincinnati, United States, 2005: 1705~1708
23 Mei Zhou, Yunkai Deng, Zhimin Zhang. A New Algorithm for SAR Raw Data Compression by Using Wavelet Packets. IEEE ICSP 2006, Guilin, China, 2007
24李霆,王东进,刘发林.小波变换的SAR原始数据网格编码量化压缩算法.现代雷达. 2008, 30(5): 53~56
25 V. Pascazio, G. Schirinzi. SAR Raw Data Compression by Subband Coding. IEEE Transactions on Geoscience and Remote Sensing. 2003, 41(5): 2071~2074
26胡晓新,王岩飞.基于小波包变换的SAR原始数据压缩.电子与信息学报, 28(8): 1476~1479
27 Donoho D L. Compressed Sensing. IEEE Transactions on Information Theory. 2006, 52(4): 1289~1306
28 Candes E, Romberg J, Tao T. Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information. IEEE Transactions on Information Theory. 2006, 52(2): 489~509
29 Baraniuk R. Compressive Sensing. IEEE Signal Processing Magazine. 2007, 24(4): 118~121
30 Baraniuk R, Steeghs P. Compressive Radar Imaging. IEEE 2007 Radar Conference, Waltham, United States, 2007: 128~133
31 Herman M, Strohmer T. Compressed Sensing Radar. IEEE International Conference on Acoustics, Speech and Signal Processing, Las Vegas, United States, 2008: 1509~1512
32 Tello M, Lopez-Dekker P, Mallorqui J J. A Novel Strategy for Radar Imaging Based on Compressive Sensing. International Geosciences and Remote Sensing Symposium, Boston, United States, 2008: 213~216
33 Yoon Y S, Amin M G. Compressed Sensing Technique for High Resolution Radar Imaging. The International Society for Optical Engineering, Orlando, United States, 2008
34 Sujit Bhattacharya, Thomas Blumensath, Bernard Mulgrew, Mike Davies. Fast Encoding of Synthetic Aperture Radar Raw Data Using CCompressed Sensing. 2007 IEEE/SP 14th Workshop on Statistical Signal Processing, Madison, United States, 2007: 448~452
35 Sujit Bhattacharya, Thomas Blumensath, Bernard Mulgrew, Mike Davies. Synthetic Aperture Radar Raw Data Encoding Using Compressed Sensing. IEEE Radar Conference, Rome, Italy, 2008
36 Wang Min. Raw SAR Data Compression by Structurally Random Matrix Based Compressive Sampling. 2009 Asia-Pacific Conference on Synthetic Aperture Radar, Xian, China, 2009: 1119~1122
37 Candes E, Tao T. Near Optimal Signal Recovery from Random Projections: Universal Encoding Strategies. IEEE Transactions on Information Theory. 2006, 52(12): 5406~5425
38 Candes E, Romberg J, Tao T. Stable Signal Recovery from Incomplete and Inaccurate Measurements. Communications on pure and Applied Mathematics, 2006, 59(8): 1207~1223
39 Tsaig Y, Donoho D. Extensions of Compressed Sensing. Signal Processing. 2006, 86(3): 549~571
40 Do T.T, Tran T.D, Lu Gan. Fast Compressive Sampling with Structurally Random Matrices. 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, Las Vegas, United States, 2008: 3369~3372
41 Donoho D L, Elad M, Temlyakov V. Stable Recovery of Sparse Overcomplete Representations in the Presence of Noise. IEEE Transactions on Information Theory. 2006, 52(1): 6~18
42 Kim S, Koh K, Lustig M, Boyd S, Gorinevsky D. An Interior-point Method for Large Scale L1 Regularized Least Squares. IEEE Journal of Selected Topics in Signal processing. 2007,1(4): 606~617
43 Fiqueiredo MAT, Nowak RD, Wright S J. Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and other Inverse Problems. IEEE Journal of Selected Topics in Signal Processing. 2007, l(4): 586~598
44 Tropp J, Gilbert A. Signal Recovery from Random Measurements Via Orthogonal Matching Pursuit. IEEE Transactions on Information Theory. 2007, 53(12): 4655~4666
45 Gabriel Rilling, Mike Davies, Bernard Mulgrew. Compressed Sensing based Compression of SAR Raw Data. SPARS’09 - Signal Processing with Adaptive Sparse Structured Representations, Saint Malo, France, 2009
2 U. Benz, K. Strodl, A. Moreira. A Comparison of Several Algorithms of SAR Raw Data Compression. IEEE Transactions on Geosciences and Remote Sensing. 1995, 33(5): 1266~1276
3曹鹏志,许荣庆,刘永坦.块浮点量化(BFPQ)在星载合成孔径雷达回波数据压缩中的应用.哈尔滨工业大学学报, 1997, 29(3): 91~95
4 T.Algra. Compression of Raw SAR Data Using Entropy-constrained Quantization. 2000 Internaitonal Geoscience and Remote Sensing Symposium, Honolulu, United States, 2000: 2660~2662
5 D. Lebedeff, P. Mathieu, M. Barlaud, C.Lambert-Nebout, and P.Bellemain. Adaptive Vector Quantization of Raw SAR Data. IEEE ICASSP, Detroit, United States, 1995: 2511~2514
6 Ling Ting, Wang Dongjin, Liu Falin. A Fast BAVQ Algorithm for SAR Raw Data Compression. CIE International Conference of Radar Proceedings, Shanghai, China, 2006
7王函,娄晓光. BAVQ压缩算法应用于SAR原始数据压缩——最佳矢量维数的选择方法.科学技术与工程. 2009, 9(14): 4024~4026
8杨云志,黄顺吉,王建国. SAR原始数据矢量量化的码书改进研究.现代雷达. 2007, 29(11): 60~63
9 Fischer, Jens, Benz, Ursula, Moreira, Alberto. Efficient SAR Raw Data Compression in Frequency Domain. IEEE IGARSS, Hamburg , Germany, 1999: 2261~2263
10宋鸿梅,王岩飞,潘志刚.基于FFT-BAQ的SAR原始数据压缩新算法.系统工程与电子技术. 2009, 31(11): 2613~2617
11宋鸿梅,王岩飞,潘志刚.基于DCT-TCQ的SAR原始数据压缩算法.电子与信息学报. 2010, 32(5): 1040~1044
12 D’Elia. C, Poqqi. G, Verdoliva. L. Compression of SAR Raw Data Through Range Focusing and Variable-Rate Trellis-Coded Quantization. IEEE Transactions on Geosciences Remote Sensing. 2000, 3(9): 1282~1288
13 Giovanni Poggi et al. On-board Compression of SAR Data Through Range Focusing. IEEE Transactions on Geosciences Remote Sensing, 1999: 2247~2250
14 Giovnani Pogg ie al. Compression of SAR Data Via Range Focusing and Trellis Coded Quantization. IEEE IGARSS 2000, Honolulu, United States, 2000: 2642~2644
15曾尚春,朱兆达.一种SAR原始数据的变换域编码算法.遥感学报. 2008, 12(3): 392~397
16 Mei Zhou, Yunkai, Deng Zhimin, Zhang Lingli, Tang Chuanrong. A Comparison of Several Raw Data Compression Algorithms for Acquisition of Remotely Sensed Data. Geoinformatics 2008 and Joint Conference on GIS and Built Environment, Guangzhou, China, 2008
17 El Boustani, A. Brunham, K. Kinsner, W. An Optimal Wavelet for Raw SAR Data Compression. Canadian Conference on Electrical and Computer Engineering, Montreal, Canada, 2003: 2071~2074,
18 El-Boustani, A. Brunham, K. Kinsner, W. Investigation of Wavelets for Raw SAR Data Compression. 2003 IGARSS: Learning From Earth's Shapes and Colours, Toulouse, France, 2003: 1814~1816
19 El-Boustani, A.Turiel, A. Huot, E. Brunham, K. Kinsner, W. Wavelet Transform Based Compression Techniques for Raw SAR Data. 2002 IEEE Canadian Conference on Electrical and Computer Engineering, Winipeg and Manitoba, Canada, 2002: 857~862
20曾尚春,朱兆达.小波变换块自适应量化算法压缩SAR原始数据.遥感学报. 11(4): 481~486
21 E. Magli,G. Olmo,and B. Penna. Wavelet-based Compression of SAR Raw Data. IEEE IGARSS 2002, Toronto, Canada, 2002: 1129~1131
22 Tammana, Gowtham A. Zheng, Yuan F. Ewing. Robert L. Synthetic Aperture Radar Raw Data Compression Using Wavelet Packet Transform and Trellis Coded Quantization. IEEE International 48th Midwest Symposium on Circuits and Systems, Cincinnati, United States, 2005: 1705~1708
23 Mei Zhou, Yunkai Deng, Zhimin Zhang. A New Algorithm for SAR Raw Data Compression by Using Wavelet Packets. IEEE ICSP 2006, Guilin, China, 2007
24李霆,王东进,刘发林.小波变换的SAR原始数据网格编码量化压缩算法.现代雷达. 2008, 30(5): 53~56
25 V. Pascazio, G. Schirinzi. SAR Raw Data Compression by Subband Coding. IEEE Transactions on Geoscience and Remote Sensing. 2003, 41(5): 2071~2074
26胡晓新,王岩飞.基于小波包变换的SAR原始数据压缩.电子与信息学报, 28(8): 1476~1479
27 Donoho D L. Compressed Sensing. IEEE Transactions on Information Theory. 2006, 52(4): 1289~1306
28 Candes E, Romberg J, Tao T. Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information. IEEE Transactions on Information Theory. 2006, 52(2): 489~509
29 Baraniuk R. Compressive Sensing. IEEE Signal Processing Magazine. 2007, 24(4): 118~121
30 Baraniuk R, Steeghs P. Compressive Radar Imaging. IEEE 2007 Radar Conference, Waltham, United States, 2007: 128~133
31 Herman M, Strohmer T. Compressed Sensing Radar. IEEE International Conference on Acoustics, Speech and Signal Processing, Las Vegas, United States, 2008: 1509~1512
32 Tello M, Lopez-Dekker P, Mallorqui J J. A Novel Strategy for Radar Imaging Based on Compressive Sensing. International Geosciences and Remote Sensing Symposium, Boston, United States, 2008: 213~216
33 Yoon Y S, Amin M G. Compressed Sensing Technique for High Resolution Radar Imaging. The International Society for Optical Engineering, Orlando, United States, 2008
34 Sujit Bhattacharya, Thomas Blumensath, Bernard Mulgrew, Mike Davies. Fast Encoding of Synthetic Aperture Radar Raw Data Using CCompressed Sensing. 2007 IEEE/SP 14th Workshop on Statistical Signal Processing, Madison, United States, 2007: 448~452
35 Sujit Bhattacharya, Thomas Blumensath, Bernard Mulgrew, Mike Davies. Synthetic Aperture Radar Raw Data Encoding Using Compressed Sensing. IEEE Radar Conference, Rome, Italy, 2008
36 Wang Min. Raw SAR Data Compression by Structurally Random Matrix Based Compressive Sampling. 2009 Asia-Pacific Conference on Synthetic Aperture Radar, Xian, China, 2009: 1119~1122
37 Candes E, Tao T. Near Optimal Signal Recovery from Random Projections: Universal Encoding Strategies. IEEE Transactions on Information Theory. 2006, 52(12): 5406~5425
38 Candes E, Romberg J, Tao T. Stable Signal Recovery from Incomplete and Inaccurate Measurements. Communications on pure and Applied Mathematics, 2006, 59(8): 1207~1223
39 Tsaig Y, Donoho D. Extensions of Compressed Sensing. Signal Processing. 2006, 86(3): 549~571
40 Do T.T, Tran T.D, Lu Gan. Fast Compressive Sampling with Structurally Random Matrices. 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, Las Vegas, United States, 2008: 3369~3372
41 Donoho D L, Elad M, Temlyakov V. Stable Recovery of Sparse Overcomplete Representations in the Presence of Noise. IEEE Transactions on Information Theory. 2006, 52(1): 6~18
42 Kim S, Koh K, Lustig M, Boyd S, Gorinevsky D. An Interior-point Method for Large Scale L1 Regularized Least Squares. IEEE Journal of Selected Topics in Signal processing. 2007,1(4): 606~617
43 Fiqueiredo MAT, Nowak RD, Wright S J. Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and other Inverse Problems. IEEE Journal of Selected Topics in Signal Processing. 2007, l(4): 586~598
44 Tropp J, Gilbert A. Signal Recovery from Random Measurements Via Orthogonal Matching Pursuit. IEEE Transactions on Information Theory. 2007, 53(12): 4655~4666
45 Gabriel Rilling, Mike Davies, Bernard Mulgrew. Compressed Sensing based Compression of SAR Raw Data. SPARS’09 - Signal Processing with Adaptive Sparse Structured Representations, Saint Malo, France, 2009