栅格编码量化在合成孔径雷达数据压缩中的应用
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摘要
数据压缩技术是合成孔径雷达(SAR)必须具备的关键技术之一。随着SAR分辨率、测绘带宽等性能指标的不断提高，SAR输出的数据率大大增加。尤其对于未来的星载SAR，将从目前仅获取雷达原始数据向同时完成星上实时成像的技术水平发展。这样，数据压缩算法就需要满足对SAR的原始数据和图像数据同时做压缩处理的需求。
论文在回顾了以往SAR使用的原始数据压缩算法后，引入了一种新的数据压缩编码算法：栅格编码量化(TCQ)。TCQ算法充分利用了卷积码的特性，采用信号空间扩展的方法来增大量化信号的欧式距离，从而达到提高量化性能的目的。
本文针对未来星载SAR星上实时成像的技术发展，提出了采用统一的TCQ算法对SAR原始数据和实时成像图像数据做压缩处理的思想，并设计了具有更多状态数的栅格用于编码试验。这一思想的理论基础在于，TCQ作为一种通用的信源编码算法，可以对各种数据源做编码处理；而以往在SAR中应用的BAQ、BFPQ等算法都是针对SAR原始数据的统计特性而设计的，具有一定的局限性。通过对SAR原始数据的压缩试验证明，TCQ算法较BAQ算法在压缩数据的信噪比上有较大的提高。同时，采用具有更多状态数的栅格设计方案，编码数据的性能指标较国外文献中的设计方案也有了进一步的提高。论文还采用TCQ算法对SAR图像进行了压缩试验，结果表明，经过TCQ算法压缩后的图像具有较高的信噪比，而且图像失真小，可以作为今后星载、机载SAR平台进行数据压缩的一种可选择的技术方案。
The data compression technique is an essential one of Synthetic Aperture Radar (SAR). However, the ratio of the data is greatly dependent on the improved radar's resolution and the increasing swath. Techniques in SAR, especially in space-borne ones, develop toward achieving both raw data acquisition, which is the current state of the art, and on-board Real-time Imaging. Therefore, data compression algorithm will be developed to realize compressing both raw data and SAR images in the future.
After reviewing compression algorithms in SAR, the author introduces a new compression algorithm: Trellis Coded Quantization (TCQ). The TCQ algorithm takes advantage of convolution code, and enlarges the Euclidean distances among quantized data by expanding data dimensions.
The author puts forward a method of uniform TCQ algorithm to compress SAR raw data and images. According to real-time imaging technique on space-borne SAR, and then designs a multi-state trellis with for compressing tests. The principle of the method lies in: TCQ is a general source-coding algorithm to process various data. However, the BAG, BFPQ algorithms, are developed only in SAR applications. The performance of TCQ seems to be higher SNR than BAQ in compressed data. The design scheme with a multi-state trellis states also shows a better SNR performance than current reported schemes. The author, in the meanwhile, uses TCQ to implement SAR image compressions. The result proves that the compressed images are high PSNR and low distortion characters, which is suitable to be used in space-borne and airborne SAR systems.
论文在回顾了以往SAR使用的原始数据压缩算法后，引入了一种新的数据压缩编码算法：栅格编码量化(TCQ)。TCQ算法充分利用了卷积码的特性，采用信号空间扩展的方法来增大量化信号的欧式距离，从而达到提高量化性能的目的。
本文针对未来星载SAR星上实时成像的技术发展，提出了采用统一的TCQ算法对SAR原始数据和实时成像图像数据做压缩处理的思想，并设计了具有更多状态数的栅格用于编码试验。这一思想的理论基础在于，TCQ作为一种通用的信源编码算法，可以对各种数据源做编码处理；而以往在SAR中应用的BAQ、BFPQ等算法都是针对SAR原始数据的统计特性而设计的，具有一定的局限性。通过对SAR原始数据的压缩试验证明，TCQ算法较BAQ算法在压缩数据的信噪比上有较大的提高。同时，采用具有更多状态数的栅格设计方案，编码数据的性能指标较国外文献中的设计方案也有了进一步的提高。论文还采用TCQ算法对SAR图像进行了压缩试验，结果表明，经过TCQ算法压缩后的图像具有较高的信噪比，而且图像失真小，可以作为今后星载、机载SAR平台进行数据压缩的一种可选择的技术方案。
The data compression technique is an essential one of Synthetic Aperture Radar (SAR). However, the ratio of the data is greatly dependent on the improved radar's resolution and the increasing swath. Techniques in SAR, especially in space-borne ones, develop toward achieving both raw data acquisition, which is the current state of the art, and on-board Real-time Imaging. Therefore, data compression algorithm will be developed to realize compressing both raw data and SAR images in the future.
After reviewing compression algorithms in SAR, the author introduces a new compression algorithm: Trellis Coded Quantization (TCQ). The TCQ algorithm takes advantage of convolution code, and enlarges the Euclidean distances among quantized data by expanding data dimensions.
The author puts forward a method of uniform TCQ algorithm to compress SAR raw data and images. According to real-time imaging technique on space-borne SAR, and then designs a multi-state trellis with for compressing tests. The principle of the method lies in: TCQ is a general source-coding algorithm to process various data. However, the BAG, BFPQ algorithms, are developed only in SAR applications. The performance of TCQ seems to be higher SNR than BAQ in compressed data. The design scheme with a multi-state trellis states also shows a better SNR performance than current reported schemes. The author, in the meanwhile, uses TCQ to implement SAR image compressions. The result proves that the compressed images are high PSNR and low distortion characters, which is suitable to be used in space-borne and airborne SAR systems.
引文
[1]．张澄波，《综合孔径雷达原理、系统分析与应用》，科学出版社，北京，1989。
[2]. Chris Oliver, Shaun Quegan, Understanding Synthetic Aperture Radar Images. Artech House, Boston London, 1998
[3]．陈宗骘 “合成孔径雷达引论” 微波遥感学术会议论文集 1988年 北京
[4]. G. Ungerboeck, "Channel coding with Multiple/Phase Signals," IEEE Trans. Inform. Theory, vol. IT-28, Jan. 1982, pp. 55～67.
[5]．曹志刚，钱亚生，《现代通信原理》，清华大学出版社，北京，1992。
[6]. G. Ungerboeck, "Trellis-Coded Modulation with Redundant Signal Sets," IEEE Communication Mazazine, Vol.25, No.2, Feb. 1987
[7]. G.D. Forney, Jr., "The Viterbi algorithm," Proc. IEEE(Invited Paper), Vol. 61, Mar. 1973, pp.268～278.
[8]. R.Kwok, "Block adaptive quantization of Magellan SAR data", IEEE Trans. Geosci. Remote Sensing, Vol. GE-27,no.4,1989.
[9]. U.Benz, "A fuzzy block adaptive quzntizer(FBAQ) for synthetic aperture radar." in Proc. Fuzz-IEEE, Orlando, FL. 1994.
[10]. A. Moreia and F. Blaeser, "Fusion of block adaptive and vector quzntizer for efficient SAR data compression." in Proc. IGASS'93, Tokyo.
[11]. T. Gioutsos "Vector Quantization Use to Reduce SAR Data Rates", SPIE Trans.On Communication Vol. 28 No. 1, 1980
[12]. U. Benz, et al. "A Comparison of Several Algorithms for On-board SAR Raw Data Compression," IEEE Trans. Geoscience and Remote Sensing, Vol.33, No.5, 1995.
[13]. T. Berger, Rate Distortion Theory, Englewood Cliffs, NJ: Prentice-Hall, 1971
[14]. M. W. Marcellin and Thomas R. Fischer, "Trellis Coded Quantizaiton of Memoryless and Gauss-Markov Sources," IEEE 'Frans. On Comm., Vol. 38, No.1, pp.82～93, Jan. 1990.
[15]. J. W. Owens etc., "Compression of Synthetic Aperture Radar Video Phase History Data Using Trellis-coded Quantization Techniques", IEEE Trans. On Geoscience and Remote Sensing, Vol. 37, NO.2, 1999
[16]．王岩飞，廖蜀燕，杨汝良，陈宗骘，“合成孔径雷达原始数据实时压缩”，卫星遥感图像数据传输与压缩技术专题研讨会论文集，1996，pp．208-211。Ursula Benz,
Klaus Strodl, and Alberto Moreira, "Improved SAR Raw Data Compression With Adaptive Gain Estimation", EUSAR'96,K(?)nigswinter, Germany(1996) pp. 285-288
[17]．王岩飞，贾守新，陈宗骘，“合成孔径雷达原始数据的二维压缩算法”，卫星遥感图象数据传输与压缩技术专题研讨会论文集，pp．217-222，1996
[18]. Tom D.Lookabaugh and Robert M.Gray, "High-Resolution Quantization Theory and the Vector Quantizer Advantage" ,IEEE Transactions on Information Theory Vol.35 No.5 September 1989
[19]. T. H. Joo, D. N. Held, "An Adaptive Quzntization Method For Burst Mode SAR" IEEE International Radar Conference, 1985
[20]．吴乐南，《数据压缩的原理与应用》，电子工业出版社 1995年
[21]．李象霖，“数字图像处理”，中国科学技术大学研究生院。
[22]. Rajan L. Joshi, Valerie J. Crump, and Thomas R. Fischer "Image Subband Coding Using Arithmetic Coded Trellis Coded Quantization", IEEE Trans. On Circuits and Systems for Video Technology, Vol. 5, No. 6, 1995
[23]. Tomas R. Fischer, Michael W. Macellin, and Min Wang "Trellis-Coded Vector Quantization", IEEE Trans. On Information Theory, Vol. 37, No.6 Nov. 1991
[24]．林孝工，刘富全，“八状态的格码编码和译码的模拟方法”，哈尔滨工程大学学报，第18卷第5期，1997年10月。
[25]．钟晓建等，“DMT和卷积编码调制在DSL中的应用”，现代有限传输，第1期，2002年3月。
[26]. N. Farvardin and J. W. Modestino, "Optimum quantizer performance for a class of non-Gaussian memoryless sources." IEEE Trans. Inform. Theory, Vol 30, May 1984
[27]．郑勇，周正华，朱维乐，“基于树结构矢量分类的小波图像网络编码矢量量化”，通信学报，第22卷第9期，2001年9月
[28]．郭虹，冷建华，“卷积码在语音压缩编码中的应用”，信息工程学院学报，Vol．18，No．1，March，1999
[29]. C. E. Cook and M. Bernfeld, "Radar Signals an Introduction to Theory and Application", Academic Press, 1967
[30]．樊平毅，曹志刚，“从Shannon信道编码理论看网格编码调制技术”，无线电工程，第25卷，第6期，1995
[31]．郑勇等，“基于方向树结构矢量分类的小波图像网格编码矢量量化”，信号处理，Vol．18，No．1，Feb．2002
[32]．郭世满等，数字通信——原理、技术及其应用，第一版，北京人民邮电出版社，1994年
[33]．余松煜，张文军，孙军，现代图像信息压缩技术，第一版，北京科学出版社，1998年
[34]．王岩飞，“星载合成孔径雷达数据的非线性量化压缩”，1995年中国青年学者技术科学学术讨论会—空间物理与空间遥感及其应用论文集，1995年。
[35]．周荫清，“空载成像合成孔径雷达”，遥测遥控，1989年2月
[36]．郑勇等，“基于多级零树编码的小波系数网格编码量化”，电子与信息学报，Vol．24 No．12，Dec．2001
[37]．姜丹，钱玉美，《信息理论与编码》，中国科学技术大学出版社，1992年
[38]. NASA, "SIR-C/X-SAR, Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar," JPL 400-518, 1994.
[39]. Bryan L. Huneycutt "Spaceborne Imaging Radar—C instrument", IEEE Trans. on Geoscience and Remote Sensing, Vol.27, No.2, 1989.
[40]. F.T Ulaby, R. K. Moore, A. K. Fung, Microwave Remote Sensing Active and Passive, Vol.2, Addison-Wesley, MA, 1982.
[41]．周正华等，“DCT域网格编码量化及其在图像量化中的应用”，信号处理，Vol．17，No．1，Feb．2001
[42]．易波主编，现代通信导论，第1版，湖南国防科技大学出版社，1998年
[43]．A.J.Vertibi, J.K.小村 著，蒋慧清译，数字通信和编码原理，第一版，北京人民邮电出版社，1990年
[44]．曲长文，何友，“空载SAR发展状况”，遥感技术与应用，Vol．16，No．4，Dec．2001
[2]. Chris Oliver, Shaun Quegan, Understanding Synthetic Aperture Radar Images. Artech House, Boston London, 1998
[3]．陈宗骘 “合成孔径雷达引论” 微波遥感学术会议论文集 1988年 北京
[4]. G. Ungerboeck, "Channel coding with Multiple/Phase Signals," IEEE Trans. Inform. Theory, vol. IT-28, Jan. 1982, pp. 55～67.
[5]．曹志刚，钱亚生，《现代通信原理》，清华大学出版社，北京，1992。
[6]. G. Ungerboeck, "Trellis-Coded Modulation with Redundant Signal Sets," IEEE Communication Mazazine, Vol.25, No.2, Feb. 1987
[7]. G.D. Forney, Jr., "The Viterbi algorithm," Proc. IEEE(Invited Paper), Vol. 61, Mar. 1973, pp.268～278.
[8]. R.Kwok, "Block adaptive quantization of Magellan SAR data", IEEE Trans. Geosci. Remote Sensing, Vol. GE-27,no.4,1989.
[9]. U.Benz, "A fuzzy block adaptive quzntizer(FBAQ) for synthetic aperture radar." in Proc. Fuzz-IEEE, Orlando, FL. 1994.
[10]. A. Moreia and F. Blaeser, "Fusion of block adaptive and vector quzntizer for efficient SAR data compression." in Proc. IGASS'93, Tokyo.
[11]. T. Gioutsos "Vector Quantization Use to Reduce SAR Data Rates", SPIE Trans.On Communication Vol. 28 No. 1, 1980
[12]. U. Benz, et al. "A Comparison of Several Algorithms for On-board SAR Raw Data Compression," IEEE Trans. Geoscience and Remote Sensing, Vol.33, No.5, 1995.
[13]. T. Berger, Rate Distortion Theory, Englewood Cliffs, NJ: Prentice-Hall, 1971
[14]. M. W. Marcellin and Thomas R. Fischer, "Trellis Coded Quantizaiton of Memoryless and Gauss-Markov Sources," IEEE 'Frans. On Comm., Vol. 38, No.1, pp.82～93, Jan. 1990.
[15]. J. W. Owens etc., "Compression of Synthetic Aperture Radar Video Phase History Data Using Trellis-coded Quantization Techniques", IEEE Trans. On Geoscience and Remote Sensing, Vol. 37, NO.2, 1999
[16]．王岩飞，廖蜀燕，杨汝良，陈宗骘，“合成孔径雷达原始数据实时压缩”，卫星遥感图像数据传输与压缩技术专题研讨会论文集，1996，pp．208-211。Ursula Benz,
Klaus Strodl, and Alberto Moreira, "Improved SAR Raw Data Compression With Adaptive Gain Estimation", EUSAR'96,K(?)nigswinter, Germany(1996) pp. 285-288
[17]．王岩飞，贾守新，陈宗骘，“合成孔径雷达原始数据的二维压缩算法”，卫星遥感图象数据传输与压缩技术专题研讨会论文集，pp．217-222，1996
[18]. Tom D.Lookabaugh and Robert M.Gray, "High-Resolution Quantization Theory and the Vector Quantizer Advantage" ,IEEE Transactions on Information Theory Vol.35 No.5 September 1989
[19]. T. H. Joo, D. N. Held, "An Adaptive Quzntization Method For Burst Mode SAR" IEEE International Radar Conference, 1985
[20]．吴乐南，《数据压缩的原理与应用》，电子工业出版社 1995年
[21]．李象霖，“数字图像处理”，中国科学技术大学研究生院。
[22]. Rajan L. Joshi, Valerie J. Crump, and Thomas R. Fischer "Image Subband Coding Using Arithmetic Coded Trellis Coded Quantization", IEEE Trans. On Circuits and Systems for Video Technology, Vol. 5, No. 6, 1995
[23]. Tomas R. Fischer, Michael W. Macellin, and Min Wang "Trellis-Coded Vector Quantization", IEEE Trans. On Information Theory, Vol. 37, No.6 Nov. 1991
[24]．林孝工，刘富全，“八状态的格码编码和译码的模拟方法”，哈尔滨工程大学学报，第18卷第5期，1997年10月。
[25]．钟晓建等，“DMT和卷积编码调制在DSL中的应用”，现代有限传输，第1期，2002年3月。
[26]. N. Farvardin and J. W. Modestino, "Optimum quantizer performance for a class of non-Gaussian memoryless sources." IEEE Trans. Inform. Theory, Vol 30, May 1984
[27]．郑勇，周正华，朱维乐，“基于树结构矢量分类的小波图像网络编码矢量量化”，通信学报，第22卷第9期，2001年9月
[28]．郭虹，冷建华，“卷积码在语音压缩编码中的应用”，信息工程学院学报，Vol．18，No．1，March，1999
[29]. C. E. Cook and M. Bernfeld, "Radar Signals an Introduction to Theory and Application", Academic Press, 1967
[30]．樊平毅，曹志刚，“从Shannon信道编码理论看网格编码调制技术”，无线电工程，第25卷，第6期，1995
[31]．郑勇等，“基于方向树结构矢量分类的小波图像网格编码矢量量化”，信号处理，Vol．18，No．1，Feb．2002
[32]．郭世满等，数字通信——原理、技术及其应用，第一版，北京人民邮电出版社，1994年
[33]．余松煜，张文军，孙军，现代图像信息压缩技术，第一版，北京科学出版社，1998年
[34]．王岩飞，“星载合成孔径雷达数据的非线性量化压缩”，1995年中国青年学者技术科学学术讨论会—空间物理与空间遥感及其应用论文集，1995年。
[35]．周荫清，“空载成像合成孔径雷达”，遥测遥控，1989年2月
[36]．郑勇等，“基于多级零树编码的小波系数网格编码量化”，电子与信息学报，Vol．24 No．12，Dec．2001
[37]．姜丹，钱玉美，《信息理论与编码》，中国科学技术大学出版社，1992年
[38]. NASA, "SIR-C/X-SAR, Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar," JPL 400-518, 1994.
[39]. Bryan L. Huneycutt "Spaceborne Imaging Radar—C instrument", IEEE Trans. on Geoscience and Remote Sensing, Vol.27, No.2, 1989.
[40]. F.T Ulaby, R. K. Moore, A. K. Fung, Microwave Remote Sensing Active and Passive, Vol.2, Addison-Wesley, MA, 1982.
[41]．周正华等，“DCT域网格编码量化及其在图像量化中的应用”，信号处理，Vol．17，No．1，Feb．2001
[42]．易波主编，现代通信导论，第1版，湖南国防科技大学出版社，1998年
[43]．A.J.Vertibi, J.K.小村 著，蒋慧清译，数字通信和编码原理，第一版，北京人民邮电出版社，1990年
[44]．曲长文，何友，“空载SAR发展状况”，遥感技术与应用，Vol．16，No．4，Dec．2001