基于自适应谱段重组的高光谱图像压缩方法研究
|
摘要
近年来,随着卫星遥感技术的发展,高光谱图像的应用越来越广泛,例如,地球资源勘探、环境监测、军事侦察等。然而,随着图像光谱分辨率的不断提高,高光谱遥感图像的数据量也日益庞大,这给星上图像处理和卫星信道传输提出了诸多挑战。其一,宇航环境条件严苛,数据采集的代价昂贵,所以对图像的保真度要求极高,星上图像处理要尽量减少信息损失;其二,星载平台的处理能力有限,尤其是低负载、低功耗的设备决定了数据存储空间不可能很大,这就要求对采集的数据进行压缩,然后发送到地面及时处理;其三,无线信道的频谱资源极其有限,海量的遥感数据与有限的信道带宽间存在巨大的矛盾,因此需要高倍压缩待传数据,并尽量保证图像的质量;其四,卫星信道的不确定性较高,传输过程受到外界因素影响,所以对星载遥感数据的编码应考虑容错性。
所有上述问题的核心在于如何综合考虑高光谱遥感图像的编码效率和编码质量,使其满足星载处理环境和各种应用的需求。本文以高光谱遥感图像压缩的理论和方法为主要内容,从编码的实时性和有效性入手,较深入地展开了两方面的研究:(1)高光谱图像压缩中的谱段预处理算法。为了充分挖掘图像的谱间相关性,提高高光谱序列光谱维编码的效率,研究其中的谱段重组算法,并结合谱间编码,研究适用于预测和整数变换的谱段预处理方法;(2)高光谱图像压缩中的空、谱变换算法。基于静态图像编码的相关理论,针对高光谱图像的特点研究适合星上高光谱数据处理的空、谱编码方案。具体的研究工作如下:
从整体的角度出发,给出星载高光谱遥感图像软件压缩系统的框架结构和主要功能模块,对系统的工作流程与功能模块的处理机制进行了详细的描述。基于该框架,阐述其中主要模块的理论基础,包括预测和变换等图像编码算法与量化、熵编码等其他编码过程,并结合图像特征,分析这些理论用于高光谱数据压缩时的优点和局限性。
研究高光谱图像压缩中的预处理方法。总结当前谱段预处理算法的研究现状,分析高光谱数据的空、谱特征;描述了C-MEAN聚类分组算法,并分析其局限性,探讨改进办法。以此为基础,提出了一种全新的基于自适应谱段分组的预处理算法。该算法充分考虑分组的依据,设计了符合实际应用的快速算法。通过对几幅高光谱实验数据的测试结果表明,修正后的C-MEAN算法的编码性能有所提高;而本文的自适应谱段分组算法能够在较低的计算复杂度下,大大提高高光谱图像的压缩性能,且不影响图像质量。
研究高光谱图像压缩中的谱间去相关算法。在总结当前谱间编码算法研究现状的基础上,提出了一种精细的线性逐级分组算法,并基于该算法思想的灵活性,设计了可逆的光谱维整数DCT编码方法。精细的逐级分组从计算复杂度的角度考量,利用精细的非均匀间隔阈值组,对高光谱图像的谱段进行一次性分组。另外,基于自适应谱段分组算法,还提出了一种分段的线性最优预测方法。实验结果表明,逐级分组算法能较大地提高谱间去相关算法的性能,且实时性较好;基于逐级分组的整数变换方法与基于自适应谱段分组的分段预测方法均能进一步提高软件压缩系统的整体性能。
研究高光谱图像压缩中的空间去相关算法。在总结当前静态图像编码算法研究现状的基础上,分析了高光谱数据空间域压缩时需要考虑的问题,并针对这些问题,设计了一种基于整数小波变换和位平面编码的压缩方法。然后,简单地分析了离散余弦变换比小波变换更适合高光谱图像的空间维编码,提出了一种基于二维整型局部变换和网格编码量化相结合的空间编码方法。实验结果表明,基于整数小波变换和位平面编码的空间域压缩方法进一步减小了计算复杂度,而结合整数DCT和网格编码量化的编码方法的性能也十分优越。
建立高光谱图像的软件压缩系统。结合各主要功能模块的相关方法,设计了高光谱图像压缩系统,并为系统建立了异常情况处理模块,同时还构建了主、客观评价相结合的质量评价平台。实验结果表明,新的高光谱数据软件压缩系统体现出良好的性能。
本文的研究工作覆盖了高光谱图像压缩系统框架的主要模块,具有理论研究意义和实际应用价值。
In recent years, with the development of both satellite remote sense techniques, the applications based on hyper-spectral image become more and more popular, such as earth resource exploration, environmental monitoring and military reconnaissance. However, the amount of hyper-spectral data increases with the resolution of images. There are many challenges for image processing and channels transmission on satellite. Firstly, the environmental condition is harsh and the collection of image is generally expensive, so the information loss should be minimized during the image processing. Secondly, the processing capability of on-board platform is limited, particularly the low-power consuming devices have not enough storage space, and so the collection of data must be compressed before being transferred to the earth. Thirdly, there is a big contradictory between the limited communication capacity of satellite channel and large amount of hyper-spectral data, so it needs a tradeoff between data bit rate and the quality of image. Finally, satellite channels have high uncertainty. Therefore, the remote sensing data coding should have the ability of fault tolerance.
All above factors require the cooperation of coding efficiency and coding quality so as to satisfy different needs of applications. Based on the framework of hyper-spectral image compression system, this paper aims at improving the efficiency and speed of real-time image coding by developing some new algorithms of these two techniques: (1) Band pre-processing algorithms for hyper-spectral image compression. To remove the spectral correlation and improve spectral coding efficiency, this paper studies the applicable spectrum regroup scheme for predictive coding and integer transform. (2) Spatial and spectral algorithms for hyper-spectral image compression. Based on the still image coding algorithms and the characteristics of hyper-spectral image, this paper investigates special and spectral encoding module, which is applied in processing of satellite hyper-spectral data. Specific research work is as follow.
First, the basic theory of hyper-spectral coding is introduced and described. The framework and the main function module of the system are given. Based on this framework, the basal theory of the main modules is expatiated, including traditional prediction, transform algorithm, quantization and entropy coding.
Second, research for band pre-processing of hyper-spectral image compression. Summing up the state of the art in band pre-processing algorithm and analyzing spatial and spectral characteristics of the hyper-spectral data, we propose an improved C-Mean clustering algorithm. On this basis, this paper proposes a new algorithm based on adaptive band regrouping and designs fast algorithm. The experimental results show that the proposal can maintain the quality of image under low computational complexity. So it is an efficient algorithm; the performance of the revised C-Mean coding algorithm has been improved.
Third, research for spectral coding algorithms of hyper-spectral image compression. Based on the conclusion of the current spectral coding algorithm, this paper presents a detailed linear regroup algorithm. Based on the algorithm, the optimal linear prediction program and reversible spectral dimension integral DCT coding are designed. The experimental results show that these algorithms can greatly improve the performance of the spectral de-correlation algorithm.
Fourth, research for spatial coding algorithms of hyper-spectral image compression. Based on a simple analysis of DCT and DWT, this paper proposes a new special coding program based on two-dimensional integer partial transform coding and trellis coded quantization. The experimental results show that the algorithm has superior performance by combining trellis coded quantization and DCT technique.
Finally, the complete hyper-spectral image compression system is designed. This system has a module of handling abnormal. Considering the performance evaluation of the compression system, this paper designs a simple integrating platform with some subjective and objective quality criteria benchmark. The experimental results show that the new compression system has a good performance based on the evaluation platform. The research work involves the techniques in compression domains of hyper-spectral image; therefore, it has important theoretic and practical significances.
所有上述问题的核心在于如何综合考虑高光谱遥感图像的编码效率和编码质量,使其满足星载处理环境和各种应用的需求。本文以高光谱遥感图像压缩的理论和方法为主要内容,从编码的实时性和有效性入手,较深入地展开了两方面的研究:(1)高光谱图像压缩中的谱段预处理算法。为了充分挖掘图像的谱间相关性,提高高光谱序列光谱维编码的效率,研究其中的谱段重组算法,并结合谱间编码,研究适用于预测和整数变换的谱段预处理方法;(2)高光谱图像压缩中的空、谱变换算法。基于静态图像编码的相关理论,针对高光谱图像的特点研究适合星上高光谱数据处理的空、谱编码方案。具体的研究工作如下:
从整体的角度出发,给出星载高光谱遥感图像软件压缩系统的框架结构和主要功能模块,对系统的工作流程与功能模块的处理机制进行了详细的描述。基于该框架,阐述其中主要模块的理论基础,包括预测和变换等图像编码算法与量化、熵编码等其他编码过程,并结合图像特征,分析这些理论用于高光谱数据压缩时的优点和局限性。
研究高光谱图像压缩中的预处理方法。总结当前谱段预处理算法的研究现状,分析高光谱数据的空、谱特征;描述了C-MEAN聚类分组算法,并分析其局限性,探讨改进办法。以此为基础,提出了一种全新的基于自适应谱段分组的预处理算法。该算法充分考虑分组的依据,设计了符合实际应用的快速算法。通过对几幅高光谱实验数据的测试结果表明,修正后的C-MEAN算法的编码性能有所提高;而本文的自适应谱段分组算法能够在较低的计算复杂度下,大大提高高光谱图像的压缩性能,且不影响图像质量。
研究高光谱图像压缩中的谱间去相关算法。在总结当前谱间编码算法研究现状的基础上,提出了一种精细的线性逐级分组算法,并基于该算法思想的灵活性,设计了可逆的光谱维整数DCT编码方法。精细的逐级分组从计算复杂度的角度考量,利用精细的非均匀间隔阈值组,对高光谱图像的谱段进行一次性分组。另外,基于自适应谱段分组算法,还提出了一种分段的线性最优预测方法。实验结果表明,逐级分组算法能较大地提高谱间去相关算法的性能,且实时性较好;基于逐级分组的整数变换方法与基于自适应谱段分组的分段预测方法均能进一步提高软件压缩系统的整体性能。
研究高光谱图像压缩中的空间去相关算法。在总结当前静态图像编码算法研究现状的基础上,分析了高光谱数据空间域压缩时需要考虑的问题,并针对这些问题,设计了一种基于整数小波变换和位平面编码的压缩方法。然后,简单地分析了离散余弦变换比小波变换更适合高光谱图像的空间维编码,提出了一种基于二维整型局部变换和网格编码量化相结合的空间编码方法。实验结果表明,基于整数小波变换和位平面编码的空间域压缩方法进一步减小了计算复杂度,而结合整数DCT和网格编码量化的编码方法的性能也十分优越。
建立高光谱图像的软件压缩系统。结合各主要功能模块的相关方法,设计了高光谱图像压缩系统,并为系统建立了异常情况处理模块,同时还构建了主、客观评价相结合的质量评价平台。实验结果表明,新的高光谱数据软件压缩系统体现出良好的性能。
本文的研究工作覆盖了高光谱图像压缩系统框架的主要模块,具有理论研究意义和实际应用价值。
In recent years, with the development of both satellite remote sense techniques, the applications based on hyper-spectral image become more and more popular, such as earth resource exploration, environmental monitoring and military reconnaissance. However, the amount of hyper-spectral data increases with the resolution of images. There are many challenges for image processing and channels transmission on satellite. Firstly, the environmental condition is harsh and the collection of image is generally expensive, so the information loss should be minimized during the image processing. Secondly, the processing capability of on-board platform is limited, particularly the low-power consuming devices have not enough storage space, and so the collection of data must be compressed before being transferred to the earth. Thirdly, there is a big contradictory between the limited communication capacity of satellite channel and large amount of hyper-spectral data, so it needs a tradeoff between data bit rate and the quality of image. Finally, satellite channels have high uncertainty. Therefore, the remote sensing data coding should have the ability of fault tolerance.
All above factors require the cooperation of coding efficiency and coding quality so as to satisfy different needs of applications. Based on the framework of hyper-spectral image compression system, this paper aims at improving the efficiency and speed of real-time image coding by developing some new algorithms of these two techniques: (1) Band pre-processing algorithms for hyper-spectral image compression. To remove the spectral correlation and improve spectral coding efficiency, this paper studies the applicable spectrum regroup scheme for predictive coding and integer transform. (2) Spatial and spectral algorithms for hyper-spectral image compression. Based on the still image coding algorithms and the characteristics of hyper-spectral image, this paper investigates special and spectral encoding module, which is applied in processing of satellite hyper-spectral data. Specific research work is as follow.
First, the basic theory of hyper-spectral coding is introduced and described. The framework and the main function module of the system are given. Based on this framework, the basal theory of the main modules is expatiated, including traditional prediction, transform algorithm, quantization and entropy coding.
Second, research for band pre-processing of hyper-spectral image compression. Summing up the state of the art in band pre-processing algorithm and analyzing spatial and spectral characteristics of the hyper-spectral data, we propose an improved C-Mean clustering algorithm. On this basis, this paper proposes a new algorithm based on adaptive band regrouping and designs fast algorithm. The experimental results show that the proposal can maintain the quality of image under low computational complexity. So it is an efficient algorithm; the performance of the revised C-Mean coding algorithm has been improved.
Third, research for spectral coding algorithms of hyper-spectral image compression. Based on the conclusion of the current spectral coding algorithm, this paper presents a detailed linear regroup algorithm. Based on the algorithm, the optimal linear prediction program and reversible spectral dimension integral DCT coding are designed. The experimental results show that these algorithms can greatly improve the performance of the spectral de-correlation algorithm.
Fourth, research for spatial coding algorithms of hyper-spectral image compression. Based on a simple analysis of DCT and DWT, this paper proposes a new special coding program based on two-dimensional integer partial transform coding and trellis coded quantization. The experimental results show that the algorithm has superior performance by combining trellis coded quantization and DCT technique.
Finally, the complete hyper-spectral image compression system is designed. This system has a module of handling abnormal. Considering the performance evaluation of the compression system, this paper designs a simple integrating platform with some subjective and objective quality criteria benchmark. The experimental results show that the new compression system has a good performance based on the evaluation platform. The research work involves the techniques in compression domains of hyper-spectral image; therefore, it has important theoretic and practical significances.
引文
[1]张斧.空间成像光谱信息的处理与分析[J].中国空间科学技术. 1994, 1: 26~32.
[2]汤国安,张友顺,刘咏梅.遥感数字图像处理[M].北京:科学出版社, 2004.
[3]戴昌达,姜小光,唐伶俐.遥感图像应用处理与分析[M].北京:清华大学出版社, 2004.
[4]杨哲海,韩建峰,宫大鹏.高光谱遥感技术的发展与应用[J].海洋测绘. 2003, 23 (6): 55~58.
[5]韩心志.航天多光谱遥感[M].北京:高等教育出版社, 1991.
[6]浦瑞良,宫鹏.高光谱遥感及其应用[M].北京:高等教育出版社, 2000.
[7]钱乐祥,泮学芹,赵芊.中国高光谱成像遥感应用研究进展[J].国土资源遥感. 2004, (2): 1~6.
[8]陈述彭,童庆禧,郭华东.遥感信息机理研究[M].北京:科学出版社, 1998.
[9]郭仕德,马廷,林旭东.高光谱遥感及其影像空间结构特征分析[J].测绘科学. 2005, 30 (5): 35～37.
[10] Peng Gong, Ruiliang Pu and John R. Miller. Coniferous Forest Leaf Area Index Extimation along the Oregon Transect Using Compact Airbome Spectrographic Imager Data[J]. Photogrammetric Engineering & Remote Sensing. 1995, 61 (9): 1107~1117.
[11] Peng Gong, Ruiliang Pu and Bin Yu. Conifer Species Recognition: An Exploratory Analysis of In Situ Hyperspectral Data[J]. Remote Sens. Environ. 1997, 62: 189~200.
[12] X. Wu, N. Memon. Context-based, adaptive, lossless image coding[J]. IEEE Transactions on Communications. 1997, 45 (4): 437~444.
[13] Shannon, C.E. A mathematical theory of Communication[J]. Bell System Technical Journal. 1948, 27: 379~423; 623~656.
[14]吴伯修,归绍升,祝宗泰.信息论与编码[M].北京:电子工业出版社, 1987.
[15]张雪松,倪国强,周立伟.图像编码技术发展综述[J].光学技术. 1997, 5 (3): 37~41.
[16]章毓晋.图像处理和分析[M].北京:清华大学出版社, 1999.
[17] N. Ahmed, T. Natarajan and R. Rao. Discrete cosine transform[J]. IEEE Transactionson Computers. 1974: 90~97.
[18] Meyer, Y. Orthonormal wavelets, In Wavelets, Time-Frequency Methods and Phase Space[C]. Springer-Verlag. New York: 1989.
[19] R. N. Hoffman, D. W. Johnson. Application of EOF's to multispectral imagery: data compression and noise detection for AVIRIS[J]. IEEE Transactions on Geoscience and Remote Sensing. 1994, 32 (1): 25~34.
[20] B. G. Haskell, P. G.Howard, Y. A. LeCun. Image and video coding - emerging standards and beyond[J]. IEEE Transactions on Circuits and Systems for Video Technology. 1998, 8 (7): 814~836.
[21]郭昌东.感知天地[M].北京:科学出版社, 2000.
[22]刘恒殊,彭风华,黄廉卿.超光谱遥感图像特征分析[J].光学精密工程. 2001, 9 (4): 392～395.
[23]吕东亚,黄普明,孙献璞.高光谱图像的数据特征及压缩技术[J].空间电子技术. 2005, (1): 17～22.
[24] R. E. Roger, M. C. Cavenor Lossless compression of AVIRIS images[J]. IEEE Transactions on Image Processing. 1996, 5 (5): 713~719.
[25] X. Wu, N. Memon Context-based lossless interband compression - extending CALIC[J]. IEEE Transactions on Image Processing. 2000, 9 (6): 994~1001.
[26] T. Berger, J. D. Gibson. Lossy source coding[J]. IEEE Trans. Inform. Theory. 1998, 44 (6): 2693~2723.
[27] Gersho, A. Asymptotically optimal block quantization[J]. IEEE Trans. Inform. Theory. 1979: 373~380.
[28] N. M. Nasrabadi, Y. Feng. Vector quantization of images based upon the Kohonen self-organizing feature maps[C]. IEEE. Int. Conf. o. Neural. Networks. San Diego, CA, USA, 1988. 101~108.
[29] B. Aiazzi, P. S. Alba and L. Alparone. Reversible compression of multispectral imagery based on an enhanced inter-band JPEG prediction[J]. International Geoscience and Remote Sensing Symposium (IGARSS). 1997, 4: 1990~1992.
[30] M. Antonini, M. Barlaud and I. Daubechies. Image coding using wavelet transform[J]. IEEE Trans. Image Processing. 1992, 1 (2): 205~220.
[31]程正兴.小波分析算法与应用[M].西安:西安交通大学出版社, 1998.
[32]张旭东,卢国栋,冯健.图像编码基础和小波压缩技术-原理、算法和标准[M].北京:清华大学出版社, 2004.
[33] Shapiro, J. M. Embedded image coding using zero tree of wavelet coeffients.[J]. IEEE Trans. Signal Processing. 1993, 41 (12): 3445～3462.
[34] A. Said, W. A. Pearlman. An image multiresolution representation for lossless and lossy image compression[J]. IEEE Transactions on Image Process. 1996, 5 (9): 1303~1310.
[35]宋好好,王欣.一种改进的SPECK图像编码算法[J].山东大学学报. 2004, 34 (2): 51~54.
[36] A.Skodras, C.Christopoulos and T.Ebrahimi. The JPEG2000 still image compression standard[J]. IEEE Signal Processing Magazine. 2001, 18: 36～58.
[37] M.Rabbani, R.Joshi. An overview of the JPEG2000 still image compression standard[J]. Signal Processing: Image Communication. 2002, 17: 3～48.
[38] Abousleman, G. P. Compression of hyperspectral imagery using the 3-D DCT and hybrid DPCM/DCT[J]. IEEE Transactions on Geoscience and Remote Sensing. 1995, 31 (1): 26~34.
[39] D. Markman, D. Malah. Hyperspectral image coding using 3D transforms[J]. IEEE International Conference on Image Processing. 2001, 1: 114~117.
[40] S. Lim, K. Sohn and C. Lee. Compression for hyperspectral images using three dimensional wavelet transform[J]. International Geoscience and Remote Sensing Symposium (IGARSS). 2001, 1: 109~111.
[41] X. Tang, W. A. Pearlman and J. W. Modestino Hyperspectral image compression using three-dimensional wavelet coding[C]. Proceedings of SPIE - The International Society for Optical Engineering. Santa Clara, CA, USA, 2003, 5022: 1037~1047.
[42]倪林.基于改进SPIHT的多波段遥感图像无损编码[J].电子学报. 2001, 29 (5): 704～707.
[43] A. K. Rao, S. Bhargava. Multispectral data compression using bidirectional interband prediction[J]. IEEE Transactions on Geoscience and Remote Sensing. 1996, 34 (2): 385~397.
[44] B. Aiazzi, S. Baronti and L. Alparone. Lossless image compression based on an enhanced fuzzy regression prediction[C]. IEEE International Conference on Image Processing. Orlando, FL, USA, 1999. 435~439.
[45] M. C. Hung, D. L. Yang. An efficient fuzzy C-means clustering algorithm[C]. IEEEInternational Conference on Data Mining. San Diego, CA, USA, 2001. 225～232.
[46] M. Abdul Mannan, M. Kaykobad. Block Huffman coding[J]. Computers and Mathematics with Applications. 2003, 46 (10): 1581~1587.
[47] Kim, Hyungjin, Wen, Jiangtao and John D. Villasenor. Secure arithmetic coding[J]. IEEE Transactions on Signal Processing. 2007, 55 (5): 2263~2272.
[48] M. J. Tsai, J. D. Villasenor and F. Chen. Stack-run image coding[J]. IEEE Transactions on Circuits and Systems for Video Technology. 1996, 6 (5): 519~521.
[49] Lakhani, G. Reducing coding redundancy in LZW[J]. Information Sciences. 2006, 176 (10): 1417~1434.
[50] Xie Chengjun, Su Yan and Wei Zhang. The Rice Coding algorithm achieves high-performance lossless and progressive image compression basing on the improving of integer lifting scheme Rice Coding algorithm[C]. Proceedings of SPIE - The International Society for Optical Engineering. San Diego, CA, USA, 2006.
[51] S. Gupta, A. Gersho. Feature predictive vector quantization of multispectral images[J]. IEEE Transactions on Geoscience and Remote Sensing. 1992, 30 (3): 491~501.
[52] Wai C. Chu. Embedded quantization of line spectral frequencies using a multistage tree-structured vector quantizer[J]. IEEE Transactions on Audio, Speech and Language Processing. 2006, 14 (4): 1205~1217.
[53] Kaarna, A. Integer PCA and wavelet transforms for multispectral image compression[J]. International Geoscience and Remote Sensing Symposium (IGARSS). 2001, 4: 1853~1855.
[54] C. Jutten, J. Herault. Independent Component Ayalysis versus PCA[C]. Proceeding of European Signal Processing Conference. Santa Clara, CA, USA, 1988. 643~646.
[55] B. R. Epstein, R. Hingorani and J. M. Shapiro. Multispectral KLT-wavelet data compression for Landsat thematic mapper images[C]. Proc. Data Compression Conference. NY, USA, 1992. 200~208.
[56] M. R. Pickering, M.J. Ryan Efficient spatial-spectral compression of hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing. 2001, 39 (7): 1536～1539.
[57]吴培中.从地球观测卫星看21世纪卫星新技术[J].国际太空. 2001, (8): 136~142.
[58] Freek van der Meer, Wim Bakker. Cross Correlogram Spectral Matching : Application to Surface Mineralogical Mapping by Using AVIRIS Data from Cuprite, Nevada .[J]. Remote Sens. Environ. 1997, 61: 371~382.
[59] Kaarna Arto, Zemcik Pavel and Kalviainen Heikki Compression of multispectral remote sensing images using clustering and spectral reduction[J]. IEEE Transactions on Geoscience and Remote Sensing. 2000, 38 (2): 1073~1082.
[60]石峰,莫忠良.信息论基础[M].武汉:武大大学出版社, 2002.
[61] Wu, Xiaolin. High-order context modeling and embedded conditional entropy coding of wavelet coefficients for image compression[C]. Conference Record of the Asilomar Conference on Signals, Systems & Computers. Pacific Grove, CA, USA, 1997. 1378~1382.
[62] Huffman, D. A. A method for the construction of minimum redundancy codes[C]. Proc. IRE. 1952. 1098~1101.
[63] J. Rissanen, G. Langdon. Arithmetic coding[J]. IBM J. Res. Develop. 1979: 149~162.
[64] G. Sullivan, T. Wiegand. Video Compression—From Concepts to the H.264/AVC Standard[C]. Proceedings of the IEEE. NJ, USA, 2005. 1121~1124.
[65] D. Marpe, H. Schwarz and T. Wiegand. Context-adaptive binary arithmetic coding in the H.264/AVC video compression standard[J]. IEEE Trans. Circuits Syst. Video Technol. 2003: 620~636.
[66] Daubechies, I. The wavelet transforms, time-frequency localization and signal analysis[J]. IEEE Trans. Information Theory. 1990, 36 (5): 961～1005.
[67]姚天任.现代数字信号处理[M].武汉:华中理工大学出版社, 1999.
[68]田金文,柳健.小波变换理论及其在图像压缩编码中的应用[R]. 1998.
[69]焦李成,谭山.图像的多尺度几何分析:回顾和展望[J].电子学报. 2003, 31 (12A): 1975～1981.
[70] O. Rioul, P. Duhamel. Fast algorithms for discrete and continuous wavelet transforms[J]. Information Theory, IEEE Transactions. 1992, 38 (2): 569～586.
[71]陈武凡.小波分析及其在图像处理中的应用[M].北京:科学出版社, 2002.
[72] Berger, T. Rate Distortion Theory[R]. 1971.
[73] Y. Shoham, A. Gersho. Efficient bit allocation for an arbitrary set of quantizers[J]. IEEE Transactions on Acoustics, Speech and Signal Processing. 1988, 36 (9):1445~1453.
[74] M. Robert, Gray, L. David. Quantization[J]. IEEE Transactions on Information Theory. 1998, 44 (6): 1~63.
[75] Elias, P. Bounds on performance of optimum quantizers[J]. IEEE Trans. Inform. Theory. 1970: 172~184.
[76] S. Z. Kiang, R. L. Baker and G. J. Sullivan. Recursive optimal pruning with applications to tree structured vector quantizers[J]. IEEE Transactions on Image Processing. 1992, 1 (2): 162～169.
[77] N. Farvardin, J. W. Modestino. Optimum quantizer performance for a class of non-gaussian memoryless sources[J]. IEEE Trans. Inform. Theory. 1984, 30 (3): 485~497.
[78] Langdon, G.G. An introduction to arithmetic coding[J]. IBM Journal of Research and Development. 1984, 28 (2): 135~149.
[79] A. Moffat, R.M. Neal and I.H. Witten. Arithmetic coding revisited[J]. ACM Transactions on Information Systems. 1998, 16 (3): 256~294.
[80]肖江,邓家先,吴成柯.一种支持干涉多光谱图像ROI的压缩编码方法[J].光子学报. 2003, 32 (4): 481~484.
[81]黄睿,何明一.基于分类别PCA散度的高光谱图像分类波段选择[J].电子与信息学报. 2005, 27 (10): 1588～1592.
[82] M. Petrou, P. Hou and S. -I. Kamata. Region-based image coding with multiple algorithms[J]. IEEE Transactions on Geoscience and Remote Sensing. 2001, 39 (3): 562~570.
[83]周正,谭毅华,柳健,田金文.基于自适应谱段分组的星载超光谱图像压缩算法[J].华中科技大学学报. 2007, 11.(已录用,待发表)
[84] Zheng Zhou, Yihua Tan and Jian Liu Satellite hyperspectral imagery compression algorithm based on adaptive band regrouping[C]. 2006 International Conference on Wireless Communications, Networking and Mobile Computing. Wuhan, China: 2006. 1~4.
[85] S. Gupta, A. Gersho. Feature predictive vector quantization of multispectral image[J]. IEEE Trans. Geosci. Remote Sensing. 1992, 30 (3): 491~501.
[86] Lee, J. Optimized quadtree for Karhunen-Loeve transform in multispectral image coding[J]. IEEE Transactions on Image Processing. 1999, 8 (4): 453~461.
[87]何斌,郝志航.卫星解码数字图像块效应计算机后处理方法[J].光学精密工程. 2001, 9 (1): 43～49.
[88] Zheng Zhou, Jian Liu and Jinwen Tian. Real-time hyperspectral image cube compression combining adaptive classification and partial transform coding[C]. International Conference on Signal Processing Proceedings, ICSP. Guilin, China: 2006. 1157~1160.
[89]姜小光,王长耀,王成.成像光谱数据的光谱信息特点及最佳波段选择[J].干旱区地理. 2002, 3 (3): 214～230.
[90] Huang Rui, He Mingyi. Band selection based on feature weighting for classification of hyperspectral data[J]. IEEE Geoscience and Remote Sensing Letters. 2005, 2 (2): 156~159.
[91] I. Daubechies, W. Sweldens. Factoring wavelet transforms into lifting steps[J]. J. Fourier Anal. 1998, 4 (3): 245~267.
[92] Mallat, S. A theory for multiresolution signal decomposition: the wavelet representation[J]. IEEE Trans. on PAMI. 1989, 11 (7): 674～693.
[93] Mallat, S. An efficient image representation for multiscale analysis[C]. Proceedings of Machine Vision Conference. Incline Village, NV, USA, 1987. 172~175.
[94] Sweldens, W. The Lift Scheme: A custom-design construction of biorthogonal wavelets[J]. Applied and Computational Harmonic Analysis. 1996, 3 (2): 186～200.
[95] R. Calderbank, I. Daubechies and W. Sweldens. Wavelet transforms that map integers to integers[J]. Applied and Computational Harmonic Analysis. 1998, 5 (3): 332～369.
[96] Sweldens, W. The lifting scheme: A construction of second generation wavelets[J]. SIAM Journal of Math. Analysis. 1997, 29 (2): 511～546.
[97]闫允一,郭宝龙,陈龙潭.小波提升原理在图像编码中的应用[J].计算机应用研究. 2004, (8): 136~174.
[98] Michael D.Adams, Faouzi Kossentini. Reversible Integer-to-Integer Wavelet Transforms for Image Compression: Performance Evaluation and Analysis[J]. IEEE Trans. Image Processing. 2000, 9 (6): 1010~1023.
[99]王勇,郑南宁,梅魁志.一种高效的JPEG2000位平面编码器设计[J].西安交通大学学报. 2005, 39 (2): 158~161.
[100] D. Taubman, A. Zakhor. Multirate 3-D subband coding of video[J]. IEEETransactions on Image Processing. 1994, 3 (5): 572~689.
[101] Taubman, D. High performance scalable image compression with EBCOT[J]. IEEE Transactions on Image Processing. 2000, 9 (7): 1151~1170.
[102] H. Gish, J. N. Pierce. Asymptotically efficient quantizing[J]. IEEE Transactions on Information Theory. 1968, 14 (9): 676~683.
[103] B. Tao, H. A. Peterson and B. W. Dickson. A rate-quantization model for MPEG Encoders[C]. Proceedings of International conference on Image Processing. Santa Barbara, CA, USA, 1997. 338~341.
[104]周正,柳健,严国萍.结合谱段预处理和网格编码量化的高光谱图像压缩方法[J].计算机应用研究. 2008, 7.(已录用,待发表)
[105] P. L. Dragotti, G. Poggi and A. Ragozini. Compression of multispectral images by three-dimensional SPIHT algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing. 2000, 38 (1): 416~428.
[106] F. W. Wheeler, W. A. Pearlman. SPIHT image compression without lists[C]. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Istanbul, Turkey, 2000. 2047~2050.
[107] B. Aiazzi, S. Baronti and L. Alparone. Lossless image compression based on a fuzzy-clustered prediction[C]. Proceedings - IEEE International Symposium on Circuits and Systems. Orlando, FL, USA, 1999. 9~12.
[108] G. Gelli, G. Poggi Compression of multispectral images by spectral classification and transform coding[J]. IEEE Transactions on Image Processing. 1999, 8 (4): 476~489.
[109] Shen-En Qian, A. B. Hollinger and D. Williams. Vector quantization using spectral index-based multiple subcodebooks for hyperspectral data compression[J]. IEEE Transactions on Geoscience and Remote Sensing. 2000, 38 (3): 1183~1190.
[2]汤国安,张友顺,刘咏梅.遥感数字图像处理[M].北京:科学出版社, 2004.
[3]戴昌达,姜小光,唐伶俐.遥感图像应用处理与分析[M].北京:清华大学出版社, 2004.
[4]杨哲海,韩建峰,宫大鹏.高光谱遥感技术的发展与应用[J].海洋测绘. 2003, 23 (6): 55~58.
[5]韩心志.航天多光谱遥感[M].北京:高等教育出版社, 1991.
[6]浦瑞良,宫鹏.高光谱遥感及其应用[M].北京:高等教育出版社, 2000.
[7]钱乐祥,泮学芹,赵芊.中国高光谱成像遥感应用研究进展[J].国土资源遥感. 2004, (2): 1~6.
[8]陈述彭,童庆禧,郭华东.遥感信息机理研究[M].北京:科学出版社, 1998.
[9]郭仕德,马廷,林旭东.高光谱遥感及其影像空间结构特征分析[J].测绘科学. 2005, 30 (5): 35～37.
[10] Peng Gong, Ruiliang Pu and John R. Miller. Coniferous Forest Leaf Area Index Extimation along the Oregon Transect Using Compact Airbome Spectrographic Imager Data[J]. Photogrammetric Engineering & Remote Sensing. 1995, 61 (9): 1107~1117.
[11] Peng Gong, Ruiliang Pu and Bin Yu. Conifer Species Recognition: An Exploratory Analysis of In Situ Hyperspectral Data[J]. Remote Sens. Environ. 1997, 62: 189~200.
[12] X. Wu, N. Memon. Context-based, adaptive, lossless image coding[J]. IEEE Transactions on Communications. 1997, 45 (4): 437~444.
[13] Shannon, C.E. A mathematical theory of Communication[J]. Bell System Technical Journal. 1948, 27: 379~423; 623~656.
[14]吴伯修,归绍升,祝宗泰.信息论与编码[M].北京:电子工业出版社, 1987.
[15]张雪松,倪国强,周立伟.图像编码技术发展综述[J].光学技术. 1997, 5 (3): 37~41.
[16]章毓晋.图像处理和分析[M].北京:清华大学出版社, 1999.
[17] N. Ahmed, T. Natarajan and R. Rao. Discrete cosine transform[J]. IEEE Transactionson Computers. 1974: 90~97.
[18] Meyer, Y. Orthonormal wavelets, In Wavelets, Time-Frequency Methods and Phase Space[C]. Springer-Verlag. New York: 1989.
[19] R. N. Hoffman, D. W. Johnson. Application of EOF's to multispectral imagery: data compression and noise detection for AVIRIS[J]. IEEE Transactions on Geoscience and Remote Sensing. 1994, 32 (1): 25~34.
[20] B. G. Haskell, P. G.Howard, Y. A. LeCun. Image and video coding - emerging standards and beyond[J]. IEEE Transactions on Circuits and Systems for Video Technology. 1998, 8 (7): 814~836.
[21]郭昌东.感知天地[M].北京:科学出版社, 2000.
[22]刘恒殊,彭风华,黄廉卿.超光谱遥感图像特征分析[J].光学精密工程. 2001, 9 (4): 392～395.
[23]吕东亚,黄普明,孙献璞.高光谱图像的数据特征及压缩技术[J].空间电子技术. 2005, (1): 17～22.
[24] R. E. Roger, M. C. Cavenor Lossless compression of AVIRIS images[J]. IEEE Transactions on Image Processing. 1996, 5 (5): 713~719.
[25] X. Wu, N. Memon Context-based lossless interband compression - extending CALIC[J]. IEEE Transactions on Image Processing. 2000, 9 (6): 994~1001.
[26] T. Berger, J. D. Gibson. Lossy source coding[J]. IEEE Trans. Inform. Theory. 1998, 44 (6): 2693~2723.
[27] Gersho, A. Asymptotically optimal block quantization[J]. IEEE Trans. Inform. Theory. 1979: 373~380.
[28] N. M. Nasrabadi, Y. Feng. Vector quantization of images based upon the Kohonen self-organizing feature maps[C]. IEEE. Int. Conf. o. Neural. Networks. San Diego, CA, USA, 1988. 101~108.
[29] B. Aiazzi, P. S. Alba and L. Alparone. Reversible compression of multispectral imagery based on an enhanced inter-band JPEG prediction[J]. International Geoscience and Remote Sensing Symposium (IGARSS). 1997, 4: 1990~1992.
[30] M. Antonini, M. Barlaud and I. Daubechies. Image coding using wavelet transform[J]. IEEE Trans. Image Processing. 1992, 1 (2): 205~220.
[31]程正兴.小波分析算法与应用[M].西安:西安交通大学出版社, 1998.
[32]张旭东,卢国栋,冯健.图像编码基础和小波压缩技术-原理、算法和标准[M].北京:清华大学出版社, 2004.
[33] Shapiro, J. M. Embedded image coding using zero tree of wavelet coeffients.[J]. IEEE Trans. Signal Processing. 1993, 41 (12): 3445～3462.
[34] A. Said, W. A. Pearlman. An image multiresolution representation for lossless and lossy image compression[J]. IEEE Transactions on Image Process. 1996, 5 (9): 1303~1310.
[35]宋好好,王欣.一种改进的SPECK图像编码算法[J].山东大学学报. 2004, 34 (2): 51~54.
[36] A.Skodras, C.Christopoulos and T.Ebrahimi. The JPEG2000 still image compression standard[J]. IEEE Signal Processing Magazine. 2001, 18: 36～58.
[37] M.Rabbani, R.Joshi. An overview of the JPEG2000 still image compression standard[J]. Signal Processing: Image Communication. 2002, 17: 3～48.
[38] Abousleman, G. P. Compression of hyperspectral imagery using the 3-D DCT and hybrid DPCM/DCT[J]. IEEE Transactions on Geoscience and Remote Sensing. 1995, 31 (1): 26~34.
[39] D. Markman, D. Malah. Hyperspectral image coding using 3D transforms[J]. IEEE International Conference on Image Processing. 2001, 1: 114~117.
[40] S. Lim, K. Sohn and C. Lee. Compression for hyperspectral images using three dimensional wavelet transform[J]. International Geoscience and Remote Sensing Symposium (IGARSS). 2001, 1: 109~111.
[41] X. Tang, W. A. Pearlman and J. W. Modestino Hyperspectral image compression using three-dimensional wavelet coding[C]. Proceedings of SPIE - The International Society for Optical Engineering. Santa Clara, CA, USA, 2003, 5022: 1037~1047.
[42]倪林.基于改进SPIHT的多波段遥感图像无损编码[J].电子学报. 2001, 29 (5): 704～707.
[43] A. K. Rao, S. Bhargava. Multispectral data compression using bidirectional interband prediction[J]. IEEE Transactions on Geoscience and Remote Sensing. 1996, 34 (2): 385~397.
[44] B. Aiazzi, S. Baronti and L. Alparone. Lossless image compression based on an enhanced fuzzy regression prediction[C]. IEEE International Conference on Image Processing. Orlando, FL, USA, 1999. 435~439.
[45] M. C. Hung, D. L. Yang. An efficient fuzzy C-means clustering algorithm[C]. IEEEInternational Conference on Data Mining. San Diego, CA, USA, 2001. 225～232.
[46] M. Abdul Mannan, M. Kaykobad. Block Huffman coding[J]. Computers and Mathematics with Applications. 2003, 46 (10): 1581~1587.
[47] Kim, Hyungjin, Wen, Jiangtao and John D. Villasenor. Secure arithmetic coding[J]. IEEE Transactions on Signal Processing. 2007, 55 (5): 2263~2272.
[48] M. J. Tsai, J. D. Villasenor and F. Chen. Stack-run image coding[J]. IEEE Transactions on Circuits and Systems for Video Technology. 1996, 6 (5): 519~521.
[49] Lakhani, G. Reducing coding redundancy in LZW[J]. Information Sciences. 2006, 176 (10): 1417~1434.
[50] Xie Chengjun, Su Yan and Wei Zhang. The Rice Coding algorithm achieves high-performance lossless and progressive image compression basing on the improving of integer lifting scheme Rice Coding algorithm[C]. Proceedings of SPIE - The International Society for Optical Engineering. San Diego, CA, USA, 2006.
[51] S. Gupta, A. Gersho. Feature predictive vector quantization of multispectral images[J]. IEEE Transactions on Geoscience and Remote Sensing. 1992, 30 (3): 491~501.
[52] Wai C. Chu. Embedded quantization of line spectral frequencies using a multistage tree-structured vector quantizer[J]. IEEE Transactions on Audio, Speech and Language Processing. 2006, 14 (4): 1205~1217.
[53] Kaarna, A. Integer PCA and wavelet transforms for multispectral image compression[J]. International Geoscience and Remote Sensing Symposium (IGARSS). 2001, 4: 1853~1855.
[54] C. Jutten, J. Herault. Independent Component Ayalysis versus PCA[C]. Proceeding of European Signal Processing Conference. Santa Clara, CA, USA, 1988. 643~646.
[55] B. R. Epstein, R. Hingorani and J. M. Shapiro. Multispectral KLT-wavelet data compression for Landsat thematic mapper images[C]. Proc. Data Compression Conference. NY, USA, 1992. 200~208.
[56] M. R. Pickering, M.J. Ryan Efficient spatial-spectral compression of hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing. 2001, 39 (7): 1536～1539.
[57]吴培中.从地球观测卫星看21世纪卫星新技术[J].国际太空. 2001, (8): 136~142.
[58] Freek van der Meer, Wim Bakker. Cross Correlogram Spectral Matching : Application to Surface Mineralogical Mapping by Using AVIRIS Data from Cuprite, Nevada .[J]. Remote Sens. Environ. 1997, 61: 371~382.
[59] Kaarna Arto, Zemcik Pavel and Kalviainen Heikki Compression of multispectral remote sensing images using clustering and spectral reduction[J]. IEEE Transactions on Geoscience and Remote Sensing. 2000, 38 (2): 1073~1082.
[60]石峰,莫忠良.信息论基础[M].武汉:武大大学出版社, 2002.
[61] Wu, Xiaolin. High-order context modeling and embedded conditional entropy coding of wavelet coefficients for image compression[C]. Conference Record of the Asilomar Conference on Signals, Systems & Computers. Pacific Grove, CA, USA, 1997. 1378~1382.
[62] Huffman, D. A. A method for the construction of minimum redundancy codes[C]. Proc. IRE. 1952. 1098~1101.
[63] J. Rissanen, G. Langdon. Arithmetic coding[J]. IBM J. Res. Develop. 1979: 149~162.
[64] G. Sullivan, T. Wiegand. Video Compression—From Concepts to the H.264/AVC Standard[C]. Proceedings of the IEEE. NJ, USA, 2005. 1121~1124.
[65] D. Marpe, H. Schwarz and T. Wiegand. Context-adaptive binary arithmetic coding in the H.264/AVC video compression standard[J]. IEEE Trans. Circuits Syst. Video Technol. 2003: 620~636.
[66] Daubechies, I. The wavelet transforms, time-frequency localization and signal analysis[J]. IEEE Trans. Information Theory. 1990, 36 (5): 961～1005.
[67]姚天任.现代数字信号处理[M].武汉:华中理工大学出版社, 1999.
[68]田金文,柳健.小波变换理论及其在图像压缩编码中的应用[R]. 1998.
[69]焦李成,谭山.图像的多尺度几何分析:回顾和展望[J].电子学报. 2003, 31 (12A): 1975～1981.
[70] O. Rioul, P. Duhamel. Fast algorithms for discrete and continuous wavelet transforms[J]. Information Theory, IEEE Transactions. 1992, 38 (2): 569～586.
[71]陈武凡.小波分析及其在图像处理中的应用[M].北京:科学出版社, 2002.
[72] Berger, T. Rate Distortion Theory[R]. 1971.
[73] Y. Shoham, A. Gersho. Efficient bit allocation for an arbitrary set of quantizers[J]. IEEE Transactions on Acoustics, Speech and Signal Processing. 1988, 36 (9):1445~1453.
[74] M. Robert, Gray, L. David. Quantization[J]. IEEE Transactions on Information Theory. 1998, 44 (6): 1~63.
[75] Elias, P. Bounds on performance of optimum quantizers[J]. IEEE Trans. Inform. Theory. 1970: 172~184.
[76] S. Z. Kiang, R. L. Baker and G. J. Sullivan. Recursive optimal pruning with applications to tree structured vector quantizers[J]. IEEE Transactions on Image Processing. 1992, 1 (2): 162～169.
[77] N. Farvardin, J. W. Modestino. Optimum quantizer performance for a class of non-gaussian memoryless sources[J]. IEEE Trans. Inform. Theory. 1984, 30 (3): 485~497.
[78] Langdon, G.G. An introduction to arithmetic coding[J]. IBM Journal of Research and Development. 1984, 28 (2): 135~149.
[79] A. Moffat, R.M. Neal and I.H. Witten. Arithmetic coding revisited[J]. ACM Transactions on Information Systems. 1998, 16 (3): 256~294.
[80]肖江,邓家先,吴成柯.一种支持干涉多光谱图像ROI的压缩编码方法[J].光子学报. 2003, 32 (4): 481~484.
[81]黄睿,何明一.基于分类别PCA散度的高光谱图像分类波段选择[J].电子与信息学报. 2005, 27 (10): 1588～1592.
[82] M. Petrou, P. Hou and S. -I. Kamata. Region-based image coding with multiple algorithms[J]. IEEE Transactions on Geoscience and Remote Sensing. 2001, 39 (3): 562~570.
[83]周正,谭毅华,柳健,田金文.基于自适应谱段分组的星载超光谱图像压缩算法[J].华中科技大学学报. 2007, 11.(已录用,待发表)
[84] Zheng Zhou, Yihua Tan and Jian Liu Satellite hyperspectral imagery compression algorithm based on adaptive band regrouping[C]. 2006 International Conference on Wireless Communications, Networking and Mobile Computing. Wuhan, China: 2006. 1~4.
[85] S. Gupta, A. Gersho. Feature predictive vector quantization of multispectral image[J]. IEEE Trans. Geosci. Remote Sensing. 1992, 30 (3): 491~501.
[86] Lee, J. Optimized quadtree for Karhunen-Loeve transform in multispectral image coding[J]. IEEE Transactions on Image Processing. 1999, 8 (4): 453~461.
[87]何斌,郝志航.卫星解码数字图像块效应计算机后处理方法[J].光学精密工程. 2001, 9 (1): 43～49.
[88] Zheng Zhou, Jian Liu and Jinwen Tian. Real-time hyperspectral image cube compression combining adaptive classification and partial transform coding[C]. International Conference on Signal Processing Proceedings, ICSP. Guilin, China: 2006. 1157~1160.
[89]姜小光,王长耀,王成.成像光谱数据的光谱信息特点及最佳波段选择[J].干旱区地理. 2002, 3 (3): 214～230.
[90] Huang Rui, He Mingyi. Band selection based on feature weighting for classification of hyperspectral data[J]. IEEE Geoscience and Remote Sensing Letters. 2005, 2 (2): 156~159.
[91] I. Daubechies, W. Sweldens. Factoring wavelet transforms into lifting steps[J]. J. Fourier Anal. 1998, 4 (3): 245~267.
[92] Mallat, S. A theory for multiresolution signal decomposition: the wavelet representation[J]. IEEE Trans. on PAMI. 1989, 11 (7): 674～693.
[93] Mallat, S. An efficient image representation for multiscale analysis[C]. Proceedings of Machine Vision Conference. Incline Village, NV, USA, 1987. 172~175.
[94] Sweldens, W. The Lift Scheme: A custom-design construction of biorthogonal wavelets[J]. Applied and Computational Harmonic Analysis. 1996, 3 (2): 186～200.
[95] R. Calderbank, I. Daubechies and W. Sweldens. Wavelet transforms that map integers to integers[J]. Applied and Computational Harmonic Analysis. 1998, 5 (3): 332～369.
[96] Sweldens, W. The lifting scheme: A construction of second generation wavelets[J]. SIAM Journal of Math. Analysis. 1997, 29 (2): 511～546.
[97]闫允一,郭宝龙,陈龙潭.小波提升原理在图像编码中的应用[J].计算机应用研究. 2004, (8): 136~174.
[98] Michael D.Adams, Faouzi Kossentini. Reversible Integer-to-Integer Wavelet Transforms for Image Compression: Performance Evaluation and Analysis[J]. IEEE Trans. Image Processing. 2000, 9 (6): 1010~1023.
[99]王勇,郑南宁,梅魁志.一种高效的JPEG2000位平面编码器设计[J].西安交通大学学报. 2005, 39 (2): 158~161.
[100] D. Taubman, A. Zakhor. Multirate 3-D subband coding of video[J]. IEEETransactions on Image Processing. 1994, 3 (5): 572~689.
[101] Taubman, D. High performance scalable image compression with EBCOT[J]. IEEE Transactions on Image Processing. 2000, 9 (7): 1151~1170.
[102] H. Gish, J. N. Pierce. Asymptotically efficient quantizing[J]. IEEE Transactions on Information Theory. 1968, 14 (9): 676~683.
[103] B. Tao, H. A. Peterson and B. W. Dickson. A rate-quantization model for MPEG Encoders[C]. Proceedings of International conference on Image Processing. Santa Barbara, CA, USA, 1997. 338~341.
[104]周正,柳健,严国萍.结合谱段预处理和网格编码量化的高光谱图像压缩方法[J].计算机应用研究. 2008, 7.(已录用,待发表)
[105] P. L. Dragotti, G. Poggi and A. Ragozini. Compression of multispectral images by three-dimensional SPIHT algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing. 2000, 38 (1): 416~428.
[106] F. W. Wheeler, W. A. Pearlman. SPIHT image compression without lists[C]. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Istanbul, Turkey, 2000. 2047~2050.
[107] B. Aiazzi, S. Baronti and L. Alparone. Lossless image compression based on a fuzzy-clustered prediction[C]. Proceedings - IEEE International Symposium on Circuits and Systems. Orlando, FL, USA, 1999. 9~12.
[108] G. Gelli, G. Poggi Compression of multispectral images by spectral classification and transform coding[J]. IEEE Transactions on Image Processing. 1999, 8 (4): 476~489.
[109] Shen-En Qian, A. B. Hollinger and D. Williams. Vector quantization using spectral index-based multiple subcodebooks for hyperspectral data compression[J]. IEEE Transactions on Geoscience and Remote Sensing. 2000, 38 (3): 1183~1190.