基于成像的SAR原始数据压缩算法研究
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摘要
合成孔径雷达是自上世纪五十年代逐步发展起来的一种雷达成像技术,其高分辨率的成像能力,全天时、全天候的工作模式,多波段、多极化的发展趋势,促使其在民用、军用领域得到了广泛应用,极大方便了人们的生产生活,有利于国防建设,具有广阔的发展前景。目前,由于人们对信息的需求程度越来越高,SAR需要采集的回波数据日趋庞大,而现今有限的下传链路物理带宽严重影响了数据面向地面成像装备高效、及时的传输。介于此,SAR原始数据压缩技术应运而生。
本文从数据压缩的基本原理入手,在这一部分重点介绍用于熵压缩的量化理论以及用于评价压缩能力的性能指标。随后,讨论了SAR成像的基本原理,并分析验证了SAR原始数据的统计特性。基于SAR数据的统计特性,完成了三种典型的SAR原始数据压缩算法的原理阐述和仿真,并逐一进行了对比实验和分析。
现有SAR原始数据压缩算法主要分为标量量化、矢量量化和基于变换域的量化。本文选取的三种典型算法,即基于时域和标量量化的块自适应量化算法(BAQ)和幅度-相位算法(AP),以及基于变换域的FFT-BAQ算法。其中,BAQ算法是最基本的压缩算法,其分别针对数据的实、虚部做量化,运算简单,易于硬件实现。AP算法可以认为是BAQ算法的延伸,是特别针对保护数据的相位信息的压缩算法。在同比压缩下,实验结果表明,AP算法的相位保留程度大大优于BAQ算法,非常有利于后期成像处理。FFT-BAQ算法实际上是两个BAQ量化器和一个变换操作的组合,其利用数据经某种变换后表现出的能量更为集中的特点,对数据的高能量区执行多比特量化,低能量区执行少比特量化或零量化,通过变比特编码从而实现更好的保留数据原貌的功能。在同压缩比下,FFT-BAQ算法的信噪比明显高于BAQ算法,且成像结果更为清晰。
Due to its strong ability of high resolution in imaging, all 24 hours a day and any weather in working mode, and multi-band and multi-polar in developing trend, Synthetic Aperture Radar (SAR) has been extensively applied to kinds of fields, both in public and by military. As one kind of Radar imaging technique developed eventually since the fifty’s in last century, SAR facilities the life of human-beings and the national defense greatly in recent years, and definitely own a wider development prospect. In order to meet the growing needs of information, the amount of echo data increases rapidly, however, which is not tolerated by the physical bandwidth of download link. Therefore, SAR raw data compression technique emerges with the tide in ages.
This paper commences with the basis principle of data compression, focuses on the quantization theory used in entropy compression. Subsequently, we discuss the SAR imaging simply and analyze the raw data statistic character. Based on which, we choose and achieve three classical SAR raw data compression algorithms, all compared and analyzed in raw data domain and image domain.
There are three ways existing in SAR raw data compression field, scalar quantization, vector quantization and based-transform domain quantization. In this paper, we pick up three algorithms to represent our experiment, namely by Block Adaptive Quantization (BAQ), Amplitude-Phase (AP) and FFT-BAQ. The first two are performed in time domain and based on scalar quantization and the last one is performed in frequency domain. Among them, BAQ is the earliest and most primary algorithm, which aims to the real part and imagery part of 2-D complex raw data and be easy to apply on hardware. AP is very similar to BAQ in some extent except it aims at keeping the phase information. The experiment result presents that AP leading the ability of protecting phase information which will be beneficial for imaging later. FFT-BAQ consists of two BAQ and one operation of transform. This method makes advantage of data reconstructed by energy concentration after some transformation, so the lower frequency part can be quantized with multi-bit and the higher frequency part can be quantized with low-bit, namely the bit-rate variable compression. In the situation of the same compression ratio, FFT-BAQ can retain raw data better than BAQ and present high performance.
本文从数据压缩的基本原理入手,在这一部分重点介绍用于熵压缩的量化理论以及用于评价压缩能力的性能指标。随后,讨论了SAR成像的基本原理,并分析验证了SAR原始数据的统计特性。基于SAR数据的统计特性,完成了三种典型的SAR原始数据压缩算法的原理阐述和仿真,并逐一进行了对比实验和分析。
现有SAR原始数据压缩算法主要分为标量量化、矢量量化和基于变换域的量化。本文选取的三种典型算法,即基于时域和标量量化的块自适应量化算法(BAQ)和幅度-相位算法(AP),以及基于变换域的FFT-BAQ算法。其中,BAQ算法是最基本的压缩算法,其分别针对数据的实、虚部做量化,运算简单,易于硬件实现。AP算法可以认为是BAQ算法的延伸,是特别针对保护数据的相位信息的压缩算法。在同比压缩下,实验结果表明,AP算法的相位保留程度大大优于BAQ算法,非常有利于后期成像处理。FFT-BAQ算法实际上是两个BAQ量化器和一个变换操作的组合,其利用数据经某种变换后表现出的能量更为集中的特点,对数据的高能量区执行多比特量化,低能量区执行少比特量化或零量化,通过变比特编码从而实现更好的保留数据原貌的功能。在同压缩比下,FFT-BAQ算法的信噪比明显高于BAQ算法,且成像结果更为清晰。
Due to its strong ability of high resolution in imaging, all 24 hours a day and any weather in working mode, and multi-band and multi-polar in developing trend, Synthetic Aperture Radar (SAR) has been extensively applied to kinds of fields, both in public and by military. As one kind of Radar imaging technique developed eventually since the fifty’s in last century, SAR facilities the life of human-beings and the national defense greatly in recent years, and definitely own a wider development prospect. In order to meet the growing needs of information, the amount of echo data increases rapidly, however, which is not tolerated by the physical bandwidth of download link. Therefore, SAR raw data compression technique emerges with the tide in ages.
This paper commences with the basis principle of data compression, focuses on the quantization theory used in entropy compression. Subsequently, we discuss the SAR imaging simply and analyze the raw data statistic character. Based on which, we choose and achieve three classical SAR raw data compression algorithms, all compared and analyzed in raw data domain and image domain.
There are three ways existing in SAR raw data compression field, scalar quantization, vector quantization and based-transform domain quantization. In this paper, we pick up three algorithms to represent our experiment, namely by Block Adaptive Quantization (BAQ), Amplitude-Phase (AP) and FFT-BAQ. The first two are performed in time domain and based on scalar quantization and the last one is performed in frequency domain. Among them, BAQ is the earliest and most primary algorithm, which aims to the real part and imagery part of 2-D complex raw data and be easy to apply on hardware. AP is very similar to BAQ in some extent except it aims at keeping the phase information. The experiment result presents that AP leading the ability of protecting phase information which will be beneficial for imaging later. FFT-BAQ consists of two BAQ and one operation of transform. This method makes advantage of data reconstructed by energy concentration after some transformation, so the lower frequency part can be quantized with multi-bit and the higher frequency part can be quantized with low-bit, namely the bit-rate variable compression. In the situation of the same compression ratio, FFT-BAQ can retain raw data better than BAQ and present high performance.
引文
[1]皮亦鸣等.合成孔径雷达成像原理[M].成都:电子科技大学出版社. 2007.3.
[2] Love A.. In memory of Carl A. Wiley[J]. Antennas and Propagation Society Newsletter, IEEE, Volume: 27 , Issue: 3 , pp: 17–18, 1985.
[3] Cutrona L., Leith E., Porcello L.. Coherent optical data processing[J]. Automatic Control, IRE Transactions on, Volume: 4 , Issue: 2 , pp: 137-149, 1959.
[4] Cutrona L., Leith E., Palermo, C., Porcello L.. Optical data processing and filtering systems[J]. Information Theory, IRE Transactions on, Volume: 6 , Issue: 3 , pp: 386–400, 1960.
[5]吴家安.数据压缩技术及应用[M].北京:科学出版社. 2009.1.
[6] Reeves A. H.. U.S. Patent No. 2272070, Feb. 3, 1942.
[7] Deloraine E. M.. Pulse Techniques in Line and Radio Communication[J]. Electrical Communication, Volume 33, pp: 183-194, Sept. 1956.
[8] Deloraine E. M.. Pulse Modulation[J]. Proceedings of the IRE, Volume: 27 , Issue: 6 , pp: 702-705, June 1949.
[9] Cutler C. C.. U.S. Patent No. 2 605361, June 29, 1950.
[10] Huffman D. A.. A Method for the Construction of Minimum-Redundancy Codes[J]. Proceedings of the IRE, Volume: 40 , Issue: 9 , pp: 1098–1101, September 1952.
[11] Max. J.. Quantizing for minimum distortion[J]. Information Theory, IRE Transactions on, Volume: 6, Issue: 1, pp: 7-12, March 1960.
[12] O’Neal J. B., Jr.. Predictive quantizing systems for the transmission of television signals[J]. The Bell System Technical Journal, pp: 689-721, May-June 1966.
[13] Habibi A., Hershel R. S.. A Unified Representation of Differential Pulse-Code Modulation (DPCM) and Transform Coding Systems[J]. Communications, IEEE Transactions on, Volume: 22, Issue: 5, pp: 692-696, May 1974.
[14] Mandelbrot B.. The Fractal Geometry of Nature[M]. San Francisco: Freeman, 1983.
[15] Ammeter H., Hazeghi K. Progress in fractal image data compression[J], Bulletin des Schweizerischen Elektrotechnischen Vereins & des Verbandes Schweizerischer Elektrizitaetswerke, Volume: 84, Issue: 7, pp: 11-16, 2 April 1993.
[16] Beaumont J.M.. Image data compression using fractal techniques[J]. BT Technology Journal, Volume: 9, Issue: 4, pp: 93-109, October 1991.
[17] Zhuang Wu, Bixi Yan. An effective fractal image compression algorithm[C]. 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), Volume: 7, pp: 139-143, 2010.
[18] Samavi S., Habibi, M., Shirani, S., Rowshanbin N.. Real time fractal image coder based on characteristic vector matching[J], Image and Vision Computing, Volume: 28, Issue: 11, pp: 1557-1568, Nov. 2010.
[19] ISO/IEC 1 5444-1. JPEG2000 image coding system[S], 2000.
[20] Shannon C. E.. A Mathematical Theory of Communication[J]. The Bell System Technical Journal, Volume: 27, pp: 379–423, 623–656, July, October, 1948.
[21] Shannon C. E.. Communication in the Presence of Noise[J]. Proceedings of the IEEE, Volume: 72, Issue: 9, September 1949.
[22] N.维纳,郝季仁译.控制论[M].北京:科学出版社. 1985.
[23]吴乐南.数据压缩[M].第二版.北京:电子工业出版社. 2005.10.
[24] Shannon C. E.. Coding theorems for a discrete source with a fidelity criterion[C]. Information and Decision Processes, R. E. Machol. Ed. New York: McGraw-Hill, pp:93, 1960.
[25] Berger T.. Rate Distortion Theory: a mathematical basis for data compression[M]. Englewood Cliffs, N.J.: Prentice-Hall, 1971.
[26] Berger T.. Optimum Quantizers and Permutation Codes[J]. Information Theory, IEEE Transaction on, Volume: 18, Issue: 6, pp: 759-765, Nov. 1972.
[27] [美] David Salomon,吴乐南等译. Data Compression: The Complete Reference. Second Edition/数据压缩原理与应用[M].北京:电子工业出版社. 2003.9.
[28] Buzo A., Gray A., Jr., Gray R., Markel J.. Speech coding based upon vector quantization[J]. Acoustics, Speech and Signal Processing, IEEE Transactions on, Volume: 28 , Issue: 5 , pp: 562–574, 1980.
[29] Linde Y., Buzo A., Gray R. M.. An algorithm for vector quantization design[J]. Communications, IEEE Transaction on, Volume: 28 , Issue: 1, pp: 84-95, 1980.
[30] Wong D.Y., Juang B.H., Gray A.H., Jr.. An 800 BPS speech compression system based on vector quantization[C]. IEEE International Symposium on Information Theory. Abstracts of Papers, pp:147, 1981.
[31] Makhoul John, Roucos Salim, Gish Herbert. Vector quantization in speech coding[J]. Proceedings of the IEEE, Volume: 73, Issue: 11, pp: 1551-1588, Nov. 1985.
[32]刘永坦等.雷达成像技术[M].哈尔滨:哈尔滨工业大学出版社. 1999.
[33]丁鹭飞.雷达原理[M].西安:西北电讯工程学院出版社. 1984.
[34]保铮,邢孟道,王彤.雷达成像技术[M].北京:电子工业出版社. 2005.
[35] North D.O.. Analysis of Factors which Determine Signal-to-Noise Discrimination in Pulsed Carrier Systems[J], Proceedings of IEEE, Volume: 51, Issue: 7, pp: 1016-1027, 1963(重印).
[36]潘志刚.低比特率合成孔径雷达数据压缩算法研究[D].北京:中国科学院电子学研究所博士学位论文. 2006.
[37] Ulaby F. T., Moore R. K., Fung A. K.. Microwave Remote Sensing: Active and Passive[M]. Volume: 2, New York: Artech, 1986.
[38] Collins F. A., Sicking C. J.. Properties of low precision analog-to-digital converters[J]. Aerospace and Electronic Systems, IEEE Transactions on, Volume: AES-12, Issue: 5, pp: 643-646, Sept. 1976.
[39] Kwok R, Johnson W. Block adaptive quantization magellan SAR data[J]. Geoscience and Remote Sensing, IEEE Transactions on, Volume: 27, Issue: 4, pp: 375-383, 1989. (1989, 27(4): 375～383.)
[40] Curlander J. C., McDonough R.. Synthetic aperture radar system and signal processing[M]. In Wiley Series in Remote Sensing, New York: Wiley, 1991.
[41]祁海明,禹卫东,田旭文,袁新哲.星载SAR原始数据BAQ压缩算法性能评估[J].现代雷达. Volume: 29, Issue: 11, 2007.11.
[42] [美]Leopold J. Cantafio.南京电子技术研究所译.星载雷达手册[M].北京:电子工业出版社. 2005.7.
[43] Ursula Benz, Klaus Strodl, Alberto Moreira. A comparison of several algorithms for SAR raw data compression[J]. Geoscience and remote sensing, IEEE Transactions on, Volume: 33, Issue: 5, Sept. 1995.
[44] Lambert-Nebout C., Besson O., Massonnet D., Rogron B.. A Comparative Study of SAR Data Compression Schemes[C]. NASA. Goddard Space Flight Center, The 1994 Space and Earth Science Data Compression Workshop, US, pp: 81-91, 1994.
[45]赵宇鹏.合成孔径雷达原始数据实用压缩算法研究[D].北京:中国科学院电子学研究所硕士学位论文. 2003.
[46]余楚才. SAR图像数据压缩技术研究[D].成都:电子科技大学硕士学位论文. 2009.
[47]杨云志, SAR数据压缩技术基本模型及其实现研究[D].成都:电子科技大学博士学位论文. 2006.
[48] William A. Pearlman, George H. Senge.. Optimal Quantization of the Rayleigh Probability Distribution[J]. Communications, IEEE Transactions on, Volumw: 27, Issue: 1, Jan. 1979.
[49]姚世超,王岩飞,张冰尘,廖蜀燕.合成孔径雷达原始数据幅相压缩算法[J].电子与信息学报. Volume: 24, Issue: 11, 2002.11.
[50]张文超,王岩飞,潘志刚.合成孔径雷达原始数据压缩AP算法幅相比特分配研究[J].电子与信息学报. Volume: 30, Issue: 4, 2008.4.
[51]宋鸿梅,王岩飞,潘志刚.基于FFT-BAQ的SAR原始数据压缩新算法[J].系统工程与电子技术. Volume: 31, Issue: 11, Nov. 2009.
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[55]岳海霞.合成孔径雷达回波信号模拟研究[D].北京:中国科学院电子学研究所博士学位论文. 2005.
[56]胡粲彬,刘方,周军红,基于位平面特征的SAR图像筛选[J].计算机应用. Volume: 29, Issue: 11, Nov.2009.
[57] Brunham K., El Boustani A., Kinsner W.. A study of bit planes for the compression of raw synthetic aperture radar data[C]. Electrical and Computer Engineering, 2002. IEEE CCECE, 2002. Canadian Conference on, Washington DC: IEEE press, Volume: 1, pp: 347-352, 2002.
[58] Zhihai He. Peak transform for efficient image representation and coding[J]. Image Processing, IEEE Transactions on, Volume: 16, Issue: 7, July 2007.
[2] Love A.. In memory of Carl A. Wiley[J]. Antennas and Propagation Society Newsletter, IEEE, Volume: 27 , Issue: 3 , pp: 17–18, 1985.
[3] Cutrona L., Leith E., Porcello L.. Coherent optical data processing[J]. Automatic Control, IRE Transactions on, Volume: 4 , Issue: 2 , pp: 137-149, 1959.
[4] Cutrona L., Leith E., Palermo, C., Porcello L.. Optical data processing and filtering systems[J]. Information Theory, IRE Transactions on, Volume: 6 , Issue: 3 , pp: 386–400, 1960.
[5]吴家安.数据压缩技术及应用[M].北京:科学出版社. 2009.1.
[6] Reeves A. H.. U.S. Patent No. 2272070, Feb. 3, 1942.
[7] Deloraine E. M.. Pulse Techniques in Line and Radio Communication[J]. Electrical Communication, Volume 33, pp: 183-194, Sept. 1956.
[8] Deloraine E. M.. Pulse Modulation[J]. Proceedings of the IRE, Volume: 27 , Issue: 6 , pp: 702-705, June 1949.
[9] Cutler C. C.. U.S. Patent No. 2 605361, June 29, 1950.
[10] Huffman D. A.. A Method for the Construction of Minimum-Redundancy Codes[J]. Proceedings of the IRE, Volume: 40 , Issue: 9 , pp: 1098–1101, September 1952.
[11] Max. J.. Quantizing for minimum distortion[J]. Information Theory, IRE Transactions on, Volume: 6, Issue: 1, pp: 7-12, March 1960.
[12] O’Neal J. B., Jr.. Predictive quantizing systems for the transmission of television signals[J]. The Bell System Technical Journal, pp: 689-721, May-June 1966.
[13] Habibi A., Hershel R. S.. A Unified Representation of Differential Pulse-Code Modulation (DPCM) and Transform Coding Systems[J]. Communications, IEEE Transactions on, Volume: 22, Issue: 5, pp: 692-696, May 1974.
[14] Mandelbrot B.. The Fractal Geometry of Nature[M]. San Francisco: Freeman, 1983.
[15] Ammeter H., Hazeghi K. Progress in fractal image data compression[J], Bulletin des Schweizerischen Elektrotechnischen Vereins & des Verbandes Schweizerischer Elektrizitaetswerke, Volume: 84, Issue: 7, pp: 11-16, 2 April 1993.
[16] Beaumont J.M.. Image data compression using fractal techniques[J]. BT Technology Journal, Volume: 9, Issue: 4, pp: 93-109, October 1991.
[17] Zhuang Wu, Bixi Yan. An effective fractal image compression algorithm[C]. 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), Volume: 7, pp: 139-143, 2010.
[18] Samavi S., Habibi, M., Shirani, S., Rowshanbin N.. Real time fractal image coder based on characteristic vector matching[J], Image and Vision Computing, Volume: 28, Issue: 11, pp: 1557-1568, Nov. 2010.
[19] ISO/IEC 1 5444-1. JPEG2000 image coding system[S], 2000.
[20] Shannon C. E.. A Mathematical Theory of Communication[J]. The Bell System Technical Journal, Volume: 27, pp: 379–423, 623–656, July, October, 1948.
[21] Shannon C. E.. Communication in the Presence of Noise[J]. Proceedings of the IEEE, Volume: 72, Issue: 9, September 1949.
[22] N.维纳,郝季仁译.控制论[M].北京:科学出版社. 1985.
[23]吴乐南.数据压缩[M].第二版.北京:电子工业出版社. 2005.10.
[24] Shannon C. E.. Coding theorems for a discrete source with a fidelity criterion[C]. Information and Decision Processes, R. E. Machol. Ed. New York: McGraw-Hill, pp:93, 1960.
[25] Berger T.. Rate Distortion Theory: a mathematical basis for data compression[M]. Englewood Cliffs, N.J.: Prentice-Hall, 1971.
[26] Berger T.. Optimum Quantizers and Permutation Codes[J]. Information Theory, IEEE Transaction on, Volume: 18, Issue: 6, pp: 759-765, Nov. 1972.
[27] [美] David Salomon,吴乐南等译. Data Compression: The Complete Reference. Second Edition/数据压缩原理与应用[M].北京:电子工业出版社. 2003.9.
[28] Buzo A., Gray A., Jr., Gray R., Markel J.. Speech coding based upon vector quantization[J]. Acoustics, Speech and Signal Processing, IEEE Transactions on, Volume: 28 , Issue: 5 , pp: 562–574, 1980.
[29] Linde Y., Buzo A., Gray R. M.. An algorithm for vector quantization design[J]. Communications, IEEE Transaction on, Volume: 28 , Issue: 1, pp: 84-95, 1980.
[30] Wong D.Y., Juang B.H., Gray A.H., Jr.. An 800 BPS speech compression system based on vector quantization[C]. IEEE International Symposium on Information Theory. Abstracts of Papers, pp:147, 1981.
[31] Makhoul John, Roucos Salim, Gish Herbert. Vector quantization in speech coding[J]. Proceedings of the IEEE, Volume: 73, Issue: 11, pp: 1551-1588, Nov. 1985.
[32]刘永坦等.雷达成像技术[M].哈尔滨:哈尔滨工业大学出版社. 1999.
[33]丁鹭飞.雷达原理[M].西安:西北电讯工程学院出版社. 1984.
[34]保铮,邢孟道,王彤.雷达成像技术[M].北京:电子工业出版社. 2005.
[35] North D.O.. Analysis of Factors which Determine Signal-to-Noise Discrimination in Pulsed Carrier Systems[J], Proceedings of IEEE, Volume: 51, Issue: 7, pp: 1016-1027, 1963(重印).
[36]潘志刚.低比特率合成孔径雷达数据压缩算法研究[D].北京:中国科学院电子学研究所博士学位论文. 2006.
[37] Ulaby F. T., Moore R. K., Fung A. K.. Microwave Remote Sensing: Active and Passive[M]. Volume: 2, New York: Artech, 1986.
[38] Collins F. A., Sicking C. J.. Properties of low precision analog-to-digital converters[J]. Aerospace and Electronic Systems, IEEE Transactions on, Volume: AES-12, Issue: 5, pp: 643-646, Sept. 1976.
[39] Kwok R, Johnson W. Block adaptive quantization magellan SAR data[J]. Geoscience and Remote Sensing, IEEE Transactions on, Volume: 27, Issue: 4, pp: 375-383, 1989. (1989, 27(4): 375～383.)
[40] Curlander J. C., McDonough R.. Synthetic aperture radar system and signal processing[M]. In Wiley Series in Remote Sensing, New York: Wiley, 1991.
[41]祁海明,禹卫东,田旭文,袁新哲.星载SAR原始数据BAQ压缩算法性能评估[J].现代雷达. Volume: 29, Issue: 11, 2007.11.
[42] [美]Leopold J. Cantafio.南京电子技术研究所译.星载雷达手册[M].北京:电子工业出版社. 2005.7.
[43] Ursula Benz, Klaus Strodl, Alberto Moreira. A comparison of several algorithms for SAR raw data compression[J]. Geoscience and remote sensing, IEEE Transactions on, Volume: 33, Issue: 5, Sept. 1995.
[44] Lambert-Nebout C., Besson O., Massonnet D., Rogron B.. A Comparative Study of SAR Data Compression Schemes[C]. NASA. Goddard Space Flight Center, The 1994 Space and Earth Science Data Compression Workshop, US, pp: 81-91, 1994.
[45]赵宇鹏.合成孔径雷达原始数据实用压缩算法研究[D].北京:中国科学院电子学研究所硕士学位论文. 2003.
[46]余楚才. SAR图像数据压缩技术研究[D].成都:电子科技大学硕士学位论文. 2009.
[47]杨云志, SAR数据压缩技术基本模型及其实现研究[D].成都:电子科技大学博士学位论文. 2006.
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