基于Contourlet变换和PSO的多光谱遥感图像分形压缩方法
|
摘要
多光谱遥感图像在航空航天、地质勘探、环境监测等领域都有广阔的应用前景。多光谱遥感图像作为一种三维图像提供了关于地物更细致的光谱信息,但同时其数据量急剧增加,给在有限信道上传输和存储带来了困难。所以研究多光谱遥感图像的高质量的压缩技术具有十分重要的实际意义。本文主要研究基于Contourlet变换和粒子群的多光谱遥感图像分形压缩方法,包括以下几个方面:
首先,实现了基于粒子群和K-均值聚类的图像分形压缩算法,进而给出了基于免疫粒子群和K-均值聚类的Contourlet域图像分形压缩算法。通过免疫粒子群和K-均值聚类算法对Contourlet变换后的系数进行分类,完成分形压缩。实验结果表明,与基于K-均值聚类的快速分形编码算法相比,本文给出的两个算法在相同压缩比下能较好地保持图像的纹理;
然后,对多光谱遥感图像每个波段进行空域Contourlet变换,将Krawtchouk矩不变量和模糊C-均值聚类算法引入到分形压缩中,分别实现了基于Krawtchouk矩不变量及基于Krawtchouk矩不变量和Contourlet变换的多光谱图像分形压缩算法。实验结果表明,通过进一步对Range块和Domain块细分类,本文实现的算法能够获得更高的峰值信噪比和压缩比;
接着,实现了基于核模糊聚类及基于改进粒子群核模糊聚类的Contourlet域多光谱遥感图像分形压缩算法,实验结果显示本文算法的编码时间较基于小波域的遥感图像分形压缩算法大为减少,同时获得了更好的解码图像效果;
最后,通过支持向量回归逼近Contourlet系数和分形编码,并利用粒子群算法优化支持向量回归机参数,实现了基于支持向量回归的Contourlet域多光谱遥感图像分形压缩算法。实验结果表明,相对于基于支持向量机的遥感图像压缩算法,本文所给出的算法能在更高的压缩比下获得质量更好的解码图像。
Multispectral remote sensing images have wide potential application, including aerospace, mineral exploration, environment monitoring, etc. As 3-D images, multispectral remote sensing images provide more precise spectral information of landmark, but results in large size data sizes. The storage and transmission of large volumes of multispectral data have become significant concerns. Therefore the research on compression methods of multispectral remote sensing images is very important and practical. This thesis contributes to research compression methods of the multispectral remote sensing images based on contourlet transform, fractal compression and PSO. The thesis mainly includes the following aspects.
Firstly, the image fractal compression algorithm based on PSO and K-means is introduced. Then the algorithm based on contourlet transform, IPSO and K-means is realized. By classifying the contourlet coefficients, the experimental results show that, the realized algorithms can get better quality of image with the same compression ratio.
Then, by decomposing the multispectral remote sensing image based on contourlet transform in the dimensional domain, the Krawtchouk moments and FCM is used in fractal compression. The multispectral image fractal compression algorithm based on Krawtchouk moments is introduced. Then the algorithm based on Krawtchouk moments, FCM and contourlet transform is realized. By classifying the range and domain blocks, the presented algorithms can reach higher PSNR and compression ratio.
Next, to reach the better quality of reconstructed image, the algorithm of multispectral image fractal compression based on KFCM is presented. Then the method based on mtsPSO and KFCM is realized. Experimental results show that compared to the method based on wavelet transform and fractal compression, the introduced algorithms can reduce encoding time efficiently and reach better quality of image.
Finally, SVR is used to approximate the contourlet coefficients and fractal codes. Then the SVM parameters are optimized by PSO. The multispectral remote sensing image fractal compression method based on SVR and contourlet transform is proposed. The experimental results show that, compared to the method based on SVM, the realized method can reach higher PSNR in similar compression ratio.
首先,实现了基于粒子群和K-均值聚类的图像分形压缩算法,进而给出了基于免疫粒子群和K-均值聚类的Contourlet域图像分形压缩算法。通过免疫粒子群和K-均值聚类算法对Contourlet变换后的系数进行分类,完成分形压缩。实验结果表明,与基于K-均值聚类的快速分形编码算法相比,本文给出的两个算法在相同压缩比下能较好地保持图像的纹理;
然后,对多光谱遥感图像每个波段进行空域Contourlet变换,将Krawtchouk矩不变量和模糊C-均值聚类算法引入到分形压缩中,分别实现了基于Krawtchouk矩不变量及基于Krawtchouk矩不变量和Contourlet变换的多光谱图像分形压缩算法。实验结果表明,通过进一步对Range块和Domain块细分类,本文实现的算法能够获得更高的峰值信噪比和压缩比;
接着,实现了基于核模糊聚类及基于改进粒子群核模糊聚类的Contourlet域多光谱遥感图像分形压缩算法,实验结果显示本文算法的编码时间较基于小波域的遥感图像分形压缩算法大为减少,同时获得了更好的解码图像效果;
最后,通过支持向量回归逼近Contourlet系数和分形编码,并利用粒子群算法优化支持向量回归机参数,实现了基于支持向量回归的Contourlet域多光谱遥感图像分形压缩算法。实验结果表明,相对于基于支持向量机的遥感图像压缩算法,本文所给出的算法能在更高的压缩比下获得质量更好的解码图像。
Multispectral remote sensing images have wide potential application, including aerospace, mineral exploration, environment monitoring, etc. As 3-D images, multispectral remote sensing images provide more precise spectral information of landmark, but results in large size data sizes. The storage and transmission of large volumes of multispectral data have become significant concerns. Therefore the research on compression methods of multispectral remote sensing images is very important and practical. This thesis contributes to research compression methods of the multispectral remote sensing images based on contourlet transform, fractal compression and PSO. The thesis mainly includes the following aspects.
Firstly, the image fractal compression algorithm based on PSO and K-means is introduced. Then the algorithm based on contourlet transform, IPSO and K-means is realized. By classifying the contourlet coefficients, the experimental results show that, the realized algorithms can get better quality of image with the same compression ratio.
Then, by decomposing the multispectral remote sensing image based on contourlet transform in the dimensional domain, the Krawtchouk moments and FCM is used in fractal compression. The multispectral image fractal compression algorithm based on Krawtchouk moments is introduced. Then the algorithm based on Krawtchouk moments, FCM and contourlet transform is realized. By classifying the range and domain blocks, the presented algorithms can reach higher PSNR and compression ratio.
Next, to reach the better quality of reconstructed image, the algorithm of multispectral image fractal compression based on KFCM is presented. Then the method based on mtsPSO and KFCM is realized. Experimental results show that compared to the method based on wavelet transform and fractal compression, the introduced algorithms can reduce encoding time efficiently and reach better quality of image.
Finally, SVR is used to approximate the contourlet coefficients and fractal codes. Then the SVM parameters are optimized by PSO. The multispectral remote sensing image fractal compression method based on SVR and contourlet transform is proposed. The experimental results show that, compared to the method based on SVM, the realized method can reach higher PSNR in similar compression ratio.
引文
[1]吴家骥.多光谱遥感图像编码技术研究[D].西安:西北工业大学, 2005.
[2] Witten I H, Neal R M, Cleary J G. Arithmetic coding for data compression[J]. Comm. Of the ACM, 1987, 30(6):520~541.
[3] Howard P G, Vitter J S. Fast and efficient lossless image compression[M]. In Storer. J. A and Cohn. M. Proceedings of Data Compression Conference, IEEE Computer Society Press, Los Alamitos, CA, 1993.
[4] Tate S R. Band ordering in lossless compression of multispectral images[J]. IEEE Transactions on Computers, 1997, 46(4):477~483.
[5] Arnavut Z. Permutations and prediction for lossless compression of multispectral TM images[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(3):999~1003.
[6] J. Mielikainen, P. Toivanen. Clustered DPCM for the lossless compression of hyperspectral images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(12):2943~2946.
[7] Lee J. Optimized quadtree of Karhunen-Loeve transform in multispectral image coding[J]. IEEE Transactions on Image Processing, 1999, 8(4):453~461.
[8] P Luigi Dragotti, G Poggi, A R P Ragozini. Compression of multispectral images by three-dimensional SPIHT algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(1):416~428.
[9] M J Ryan, J F Arnold. The lossless compression of AVIRIS images by vector quantization[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(3):546~550.
[10]张荣,刘政凯,詹凯.基于小波变换的多光谱遥感图像压缩方法[J].遥感学报, 2000, 4(2):100~105.
[11]张荣,刘政凯.一种多光谱遥感图象的近无损压缩方法[J].中国图象图形学报, 1998, 3(10):823~826.
[12] Mielikainen J, Toivanen P. Clustered DPCM for the lossless compression of hyperspectral image[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 42(12):2943~2946.
[13] Roger R E, Cavenor M C. Lossless compression of AVIRIS images[J]. IEEE Transactions on Image Processing, 1996, 5(5):713~719.
[14]张晓玲,沈兰荪,张培强.基于三维自适应预测的高光谱遥感图像无损压缩算法[J].电子学报, 2004, 32(6):957~959.
[15] Momon N D, Sayood K, Nagliras S S. Lossless compression of multispectral image data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(2):282~289.
[16]张荣,阎青,刘政凯.一种基于预测树的多光谱遥感图像无损压缩方法[J].遥感学报, 1998, 2(3): 171~175.
[17]冯燕,何明一,魏江.基于神经网络的多光谱遥感图像无损压缩[J].遥感技术与应用, 2004, 19(1):42~46.
[18] D Tretter, C A Bouman. Optimal transforms for multispectral and multilayer image coding[J]. IEEE Transactions on Image processing, 1995, 4(3):296 ~308.
[19] Kaarna, P Zemcik, H Kalviainen et al. Compression of multispectral remote sensing images using clustering and spectral reduction[J]. IEEE Transactions on Geoscience Remote Sensing, 2000, 38(2):1073~1082.
[20]吴家骥,吴成柯.基于Karhunen-Loeve和小波变换的多光谱遥感图像三维集合嵌入块编码压缩算法[J].电子与信息学报, 2005, 27(8):1244~1248.
[21] G P Abousleman. Compression of hyperspectral imagery using the 3-D DCT and hybrid DPCM/DCT[J]. IEEE Transactions on Geoscience Remote Sensing, 1995, 33(1):26~34.
[22] D Markman, D Malah. Hyperspectral image coding using 3D transforms[C]. Proceedings of International Conference on Image Processing, Thessaloniki, Greece, 2001, 1:114~117.
[23] Shapiro J M. Embedded image coding using zerotree of wavelet coefficients[J]. IEEE Transactions on Signal Processing, 1993, 41(12):3445~3462.
[24]罗建书,卓红艳,孙蕾.基于分类和3D_SPIHT算法相结合的多光谱遥感图像压缩[J].电子与信息学报, 2006, 28(9):1607~1610.
[25]付文秀,王世刚,高燕梅等.结合矢量量化的SPIHT算法用于多光谱图像压缩[J].通信学报, 2004, 25(6):109~114.
[26]王柯俨,吴成柯,孔繁锵等.卫星干涉多光谱遥感图像压缩新算法[J].光子学报, 2008, 37(3):555~560.
[27]李云松,吴成柯,陈军等.基于小波的干涉多光谱卫星图像压缩方法[J].光学学报, 2001, 21(6):691~695.
[28]吴铮,何明一.基于小波变换和分段DPCM混合编码的多光谱遥感图像压缩算法[J].电子与信息学报, 2003, 25(6):747~754.
[29]张培强,柴焱,张晓玲等.基于波段分组的3D_SPIHT高光谱遥感图像无损压缩算法[J].中国图象图形学报, 2005, 10(4):623~628.
[30] Isiam A, Pearlman W A. An embedded and efficient low-complexity hierarchical imagecoder[C]. Proceedings of SPIE Conference on Visual Communication and Image Processing, San Jose, USA, 1999, 3653:294~305.
[31]罗建书,卓红艳,黎明君.基于整数小波变换的多光谱遥感图像压缩技术[J].国防科技大学学报, 2005, 27(6):35~42.
[32] Taubman D. High performance scalable image compression with EBCOT[J]. IEEE Transactions on Image Processing, 2000, 9(7):1158~1170.
[33]王柯俨,吴成柯,邓家先等.基于经验数据分解的干涉多光谱遥感图像压缩算法[J].西安电子科技大学学报(自然科学版), 2007, 34(6):900~905.
[34]李晓翼,孔繁锵.基于不等权重率失真斜率提升的干涉多光谱遥感图像压缩算法[J].光子学报, 2007, 36:290~294.
[35] Xiao J, Wu C K, Deng J X, et al. A method for ROI coding in multispectral remote sensing image coding system[J]. Journal of Astronautics, 2004, 25(10):13~18.
[36]邓家先,吴成柯,陈军.基于率失真斜率提升的干涉多光谱遥感图像压缩[J].光学学报, 2004, 24(3):299~303.
[37]孔繁锵,吴成柯,王柯俨等.基于运动补偿和码率预分配的干涉多光谱遥感图像压缩算法[J].光子学报, 2007, 36(6):1162~1166.
[38]易文娟,郁梅,蒋刚毅. Contourlet:一种有效的方向多尺度变换分析方法[J].计算机应用研究, 2006, 9:18~22.
[39]郭建军,吴一全.基于Contourlet变换的嵌入式图像编码[J].电子测量与仪器学报, 2008, 22(2):107~110.
[40]吴一全,郭建军.基于Contourlet变换的自适应门限分形零树混合图像编码[J].光电子·激光, 2008, 19(12):1694~1697.
[41]张晓玲,张培强,沈兰荪.基于信息量失真测度的VQ及在高光谱遥感图像无损压缩中的应用[J].遥感学报, 2004, 8(5):414~418.
[42] Qian S E. Hyperspectral data compression using a fast vector quantization algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(8):1791~1798.
[43]钱神恩,闫敬文,孙辉等.基于光谱特征编码的快速矢量量化三维谱象数据压缩[J].电子学报, 1997, 25(5):11~16,28.
[44] 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.
[45] M R Pickering, M J Ryan. Efficient spatial-spectral compression of hyperspectral data[J]. IEEE Transactions on Geoscience Remote Sensing, 2001, 39(7):1536~1539.
[46]郑力,何国金.基于小波和分形的遥感图像快速编码[J].科学技术与工程, 2007, 7(15): 3780~3784, 3802.
[47]张中山,余洁,覃会玲等.一种基于小波域的分形遥感影像压缩方法[J].地理空间信息, 2008, 6(2):40~42.
[48]倪林.多波段遥感图像的快速分形编码[J].电子学报, 2002, 30(7):1079~1082.
[49]穆超,余洁.基于分形理论的遥感图像压缩方法的探讨[J].测绘信息与工程, 2007, 32(2): 41~43.
[50]李弼程,罗建书.小波分析及其应用[M].北京:电子工业出版社, 2003.23～26.
[51] Jacquin A E. Fractal image coding: a review[J]. Proceedings of the IEEE, 1993, 81(10):1451~1465.
[52] Rinaldo R, Calvagno G. Image coding by block prediction of multiresolution sub images[J]. IEEE Transactions on Image Processing, 1995, 4(7): 909~920.
[53] Davis G M. A wavelet based analysis of fractal image compression[J]. IEEE Transactions on Image Processing, 1998, 7(2): 141~154.
[54]陈作平,叶正麟,郑红婵等.基于K均值聚类的快速分形编码方法[J].中国图象图形学报, 2007, 12(4):586~591.
[55]周晨光,邱祖廉.一种改进的紧凑遗传算法及其在分形图像压缩中的应用[J].中国图象图形学报, 2007, 12(4):597~602.
[56] Kennedy J, Eberhart R. Particle swarm optimization[C]. Proceedings of IEEE International Conference on Neural Networks, Perth, 1995:1942~1948.
[57]高鹰,谢胜利.免疫粒子群优化算法[J].计算机工程与应用, 2004,6:4~6.
[58]郑晓鸣,吕士颖,王晓东.免疫接种粒子群的聚类算法[J].电子科技大学学报,2007,36(6):1264~1267.
[59] I H Witten, E Frank.数据挖掘:实用机器学习技术[M].北京,机械工业出版社, 2005: 116~118.
[60]姜来,黄彩玲,纪震.基于粒子群优化算法的矢量量化图像压缩方法[J].深圳大学学报理工版, 2006,23(3):268~271.
[61] Yap P T, Paramesran R, Ong S H. Image analysis by Krawtchouk moments[J].IEEE Transactions on Image Processing,2003,12(11):1367～1377.
[62]吴一全,郭建军.基于Krawtchouk矩的自适应门限四叉树分形编码[J].信号处理, 2009, 25(7):1033~1037.
[63]冯燕.高光谱图像压缩技术研究[D].西安:西北工业大学, 2006.
[64]袁静,冯前进,陈武凡.基于模糊聚类优化的分形图像压缩快速算法[J].计算机应用与软件, 2005 ,22 (5) :13~15, 80.
[65]粘永健,苏令华,孙蕾.基于聚类的高光谱图像无损压缩[J].电子与信息学报, 2009, 31(6):1271~1274.
[66]杨红颖,王向阳,于雁春.基于FCM聚类的快速分形图像编码算法[J].小型微型计算机系统, 2009, 30(3):493~497.
[67] Lee C K,Lee W K. Fast fractal image block coding based on local variance[J]. IEEE Transactions on Image Processing,1998,7(6):888～891.
[68] Tang X L, A Pearlman William, W Modestino James. Hyperspectral image compression using three-dimensional wavelet coding[J]. SPIE/IS&T Electronic Imaging, 2003, 5022:1037~1047.
[69] He C, Dong J Y, Zheng Y F, et al. Optimal 3-D coefficient tree structure for 3-D wavelet video coding[J]. Circuits and systems for video technology, 2003, 13(10):961~972.
[70]王朝晖.多层次快速聚类的遥感图象无损压缩[J].中国图象图形学报, 2003, 8(A)(7):843~848.
[71]粘永健,辛勤,万建伟.基于分类非线性预测的高光谱图像无损压缩[J].信号处理, 2009, 25(8):1223~1227.
[72] Kim D W, Lee K Y, Lee D, et al. Evaluation of the performance of clustering algorithms in kernel-induced feature space[J]. Pattern Recognition, 2005, 38(4):607～611.
[73]伍忠东,高新波,谢维信.基于核方法的模糊聚类算法[J].西安电子科技大学学报(自然科学版), 2004,31(4):533～537.
[74]胡旺,李志蜀.一种更简化而高效的粒子群优化算法[J].软件学报, 2007, 18(4):861~868.
[75]贵敏,贾建援,韩琪.粒子群优化算法的惯性权值递减策略研究[J].西安交通大学学报, 2006, 40(1):53-56.
[76]陶卿,姚穗,范劲松等.一种新的机器学习方法: Support Vector Manhines[J].模式识别与人工智能, 2000, 13(3):285~290.
[77] Jonathan Robinson, Vojislav Kecman. Combing support vector machine learning with the discrete cosine transform in image compression[J]. IEEE Transactions on Neural Networks, 2003, 14(4) : 950~958.
[78]张丽萍,李元诚.基于支持向量机的遥感图像压缩方法[J].计算机工程与应用, 2006, 27:200~202.
[79]张丽萍.小波支持向量机在遥感图像压缩中的应用[D].天津:天津大学, 2006.
[2] Witten I H, Neal R M, Cleary J G. Arithmetic coding for data compression[J]. Comm. Of the ACM, 1987, 30(6):520~541.
[3] Howard P G, Vitter J S. Fast and efficient lossless image compression[M]. In Storer. J. A and Cohn. M. Proceedings of Data Compression Conference, IEEE Computer Society Press, Los Alamitos, CA, 1993.
[4] Tate S R. Band ordering in lossless compression of multispectral images[J]. IEEE Transactions on Computers, 1997, 46(4):477~483.
[5] Arnavut Z. Permutations and prediction for lossless compression of multispectral TM images[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(3):999~1003.
[6] J. Mielikainen, P. Toivanen. Clustered DPCM for the lossless compression of hyperspectral images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(12):2943~2946.
[7] Lee J. Optimized quadtree of Karhunen-Loeve transform in multispectral image coding[J]. IEEE Transactions on Image Processing, 1999, 8(4):453~461.
[8] P Luigi Dragotti, G Poggi, A R P Ragozini. Compression of multispectral images by three-dimensional SPIHT algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(1):416~428.
[9] M J Ryan, J F Arnold. The lossless compression of AVIRIS images by vector quantization[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(3):546~550.
[10]张荣,刘政凯,詹凯.基于小波变换的多光谱遥感图像压缩方法[J].遥感学报, 2000, 4(2):100~105.
[11]张荣,刘政凯.一种多光谱遥感图象的近无损压缩方法[J].中国图象图形学报, 1998, 3(10):823~826.
[12] Mielikainen J, Toivanen P. Clustered DPCM for the lossless compression of hyperspectral image[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 42(12):2943~2946.
[13] Roger R E, Cavenor M C. Lossless compression of AVIRIS images[J]. IEEE Transactions on Image Processing, 1996, 5(5):713~719.
[14]张晓玲,沈兰荪,张培强.基于三维自适应预测的高光谱遥感图像无损压缩算法[J].电子学报, 2004, 32(6):957~959.
[15] Momon N D, Sayood K, Nagliras S S. Lossless compression of multispectral image data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(2):282~289.
[16]张荣,阎青,刘政凯.一种基于预测树的多光谱遥感图像无损压缩方法[J].遥感学报, 1998, 2(3): 171~175.
[17]冯燕,何明一,魏江.基于神经网络的多光谱遥感图像无损压缩[J].遥感技术与应用, 2004, 19(1):42~46.
[18] D Tretter, C A Bouman. Optimal transforms for multispectral and multilayer image coding[J]. IEEE Transactions on Image processing, 1995, 4(3):296 ~308.
[19] Kaarna, P Zemcik, H Kalviainen et al. Compression of multispectral remote sensing images using clustering and spectral reduction[J]. IEEE Transactions on Geoscience Remote Sensing, 2000, 38(2):1073~1082.
[20]吴家骥,吴成柯.基于Karhunen-Loeve和小波变换的多光谱遥感图像三维集合嵌入块编码压缩算法[J].电子与信息学报, 2005, 27(8):1244~1248.
[21] G P Abousleman. Compression of hyperspectral imagery using the 3-D DCT and hybrid DPCM/DCT[J]. IEEE Transactions on Geoscience Remote Sensing, 1995, 33(1):26~34.
[22] D Markman, D Malah. Hyperspectral image coding using 3D transforms[C]. Proceedings of International Conference on Image Processing, Thessaloniki, Greece, 2001, 1:114~117.
[23] Shapiro J M. Embedded image coding using zerotree of wavelet coefficients[J]. IEEE Transactions on Signal Processing, 1993, 41(12):3445~3462.
[24]罗建书,卓红艳,孙蕾.基于分类和3D_SPIHT算法相结合的多光谱遥感图像压缩[J].电子与信息学报, 2006, 28(9):1607~1610.
[25]付文秀,王世刚,高燕梅等.结合矢量量化的SPIHT算法用于多光谱图像压缩[J].通信学报, 2004, 25(6):109~114.
[26]王柯俨,吴成柯,孔繁锵等.卫星干涉多光谱遥感图像压缩新算法[J].光子学报, 2008, 37(3):555~560.
[27]李云松,吴成柯,陈军等.基于小波的干涉多光谱卫星图像压缩方法[J].光学学报, 2001, 21(6):691~695.
[28]吴铮,何明一.基于小波变换和分段DPCM混合编码的多光谱遥感图像压缩算法[J].电子与信息学报, 2003, 25(6):747~754.
[29]张培强,柴焱,张晓玲等.基于波段分组的3D_SPIHT高光谱遥感图像无损压缩算法[J].中国图象图形学报, 2005, 10(4):623~628.
[30] Isiam A, Pearlman W A. An embedded and efficient low-complexity hierarchical imagecoder[C]. Proceedings of SPIE Conference on Visual Communication and Image Processing, San Jose, USA, 1999, 3653:294~305.
[31]罗建书,卓红艳,黎明君.基于整数小波变换的多光谱遥感图像压缩技术[J].国防科技大学学报, 2005, 27(6):35~42.
[32] Taubman D. High performance scalable image compression with EBCOT[J]. IEEE Transactions on Image Processing, 2000, 9(7):1158~1170.
[33]王柯俨,吴成柯,邓家先等.基于经验数据分解的干涉多光谱遥感图像压缩算法[J].西安电子科技大学学报(自然科学版), 2007, 34(6):900~905.
[34]李晓翼,孔繁锵.基于不等权重率失真斜率提升的干涉多光谱遥感图像压缩算法[J].光子学报, 2007, 36:290~294.
[35] Xiao J, Wu C K, Deng J X, et al. A method for ROI coding in multispectral remote sensing image coding system[J]. Journal of Astronautics, 2004, 25(10):13~18.
[36]邓家先,吴成柯,陈军.基于率失真斜率提升的干涉多光谱遥感图像压缩[J].光学学报, 2004, 24(3):299~303.
[37]孔繁锵,吴成柯,王柯俨等.基于运动补偿和码率预分配的干涉多光谱遥感图像压缩算法[J].光子学报, 2007, 36(6):1162~1166.
[38]易文娟,郁梅,蒋刚毅. Contourlet:一种有效的方向多尺度变换分析方法[J].计算机应用研究, 2006, 9:18~22.
[39]郭建军,吴一全.基于Contourlet变换的嵌入式图像编码[J].电子测量与仪器学报, 2008, 22(2):107~110.
[40]吴一全,郭建军.基于Contourlet变换的自适应门限分形零树混合图像编码[J].光电子·激光, 2008, 19(12):1694~1697.
[41]张晓玲,张培强,沈兰荪.基于信息量失真测度的VQ及在高光谱遥感图像无损压缩中的应用[J].遥感学报, 2004, 8(5):414~418.
[42] Qian S E. Hyperspectral data compression using a fast vector quantization algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(8):1791~1798.
[43]钱神恩,闫敬文,孙辉等.基于光谱特征编码的快速矢量量化三维谱象数据压缩[J].电子学报, 1997, 25(5):11~16,28.
[44] 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.
[45] M R Pickering, M J Ryan. Efficient spatial-spectral compression of hyperspectral data[J]. IEEE Transactions on Geoscience Remote Sensing, 2001, 39(7):1536~1539.
[46]郑力,何国金.基于小波和分形的遥感图像快速编码[J].科学技术与工程, 2007, 7(15): 3780~3784, 3802.
[47]张中山,余洁,覃会玲等.一种基于小波域的分形遥感影像压缩方法[J].地理空间信息, 2008, 6(2):40~42.
[48]倪林.多波段遥感图像的快速分形编码[J].电子学报, 2002, 30(7):1079~1082.
[49]穆超,余洁.基于分形理论的遥感图像压缩方法的探讨[J].测绘信息与工程, 2007, 32(2): 41~43.
[50]李弼程,罗建书.小波分析及其应用[M].北京:电子工业出版社, 2003.23～26.
[51] Jacquin A E. Fractal image coding: a review[J]. Proceedings of the IEEE, 1993, 81(10):1451~1465.
[52] Rinaldo R, Calvagno G. Image coding by block prediction of multiresolution sub images[J]. IEEE Transactions on Image Processing, 1995, 4(7): 909~920.
[53] Davis G M. A wavelet based analysis of fractal image compression[J]. IEEE Transactions on Image Processing, 1998, 7(2): 141~154.
[54]陈作平,叶正麟,郑红婵等.基于K均值聚类的快速分形编码方法[J].中国图象图形学报, 2007, 12(4):586~591.
[55]周晨光,邱祖廉.一种改进的紧凑遗传算法及其在分形图像压缩中的应用[J].中国图象图形学报, 2007, 12(4):597~602.
[56] Kennedy J, Eberhart R. Particle swarm optimization[C]. Proceedings of IEEE International Conference on Neural Networks, Perth, 1995:1942~1948.
[57]高鹰,谢胜利.免疫粒子群优化算法[J].计算机工程与应用, 2004,6:4~6.
[58]郑晓鸣,吕士颖,王晓东.免疫接种粒子群的聚类算法[J].电子科技大学学报,2007,36(6):1264~1267.
[59] I H Witten, E Frank.数据挖掘:实用机器学习技术[M].北京,机械工业出版社, 2005: 116~118.
[60]姜来,黄彩玲,纪震.基于粒子群优化算法的矢量量化图像压缩方法[J].深圳大学学报理工版, 2006,23(3):268~271.
[61] Yap P T, Paramesran R, Ong S H. Image analysis by Krawtchouk moments[J].IEEE Transactions on Image Processing,2003,12(11):1367～1377.
[62]吴一全,郭建军.基于Krawtchouk矩的自适应门限四叉树分形编码[J].信号处理, 2009, 25(7):1033~1037.
[63]冯燕.高光谱图像压缩技术研究[D].西安:西北工业大学, 2006.
[64]袁静,冯前进,陈武凡.基于模糊聚类优化的分形图像压缩快速算法[J].计算机应用与软件, 2005 ,22 (5) :13~15, 80.
[65]粘永健,苏令华,孙蕾.基于聚类的高光谱图像无损压缩[J].电子与信息学报, 2009, 31(6):1271~1274.
[66]杨红颖,王向阳,于雁春.基于FCM聚类的快速分形图像编码算法[J].小型微型计算机系统, 2009, 30(3):493~497.
[67] Lee C K,Lee W K. Fast fractal image block coding based on local variance[J]. IEEE Transactions on Image Processing,1998,7(6):888～891.
[68] Tang X L, A Pearlman William, W Modestino James. Hyperspectral image compression using three-dimensional wavelet coding[J]. SPIE/IS&T Electronic Imaging, 2003, 5022:1037~1047.
[69] He C, Dong J Y, Zheng Y F, et al. Optimal 3-D coefficient tree structure for 3-D wavelet video coding[J]. Circuits and systems for video technology, 2003, 13(10):961~972.
[70]王朝晖.多层次快速聚类的遥感图象无损压缩[J].中国图象图形学报, 2003, 8(A)(7):843~848.
[71]粘永健,辛勤,万建伟.基于分类非线性预测的高光谱图像无损压缩[J].信号处理, 2009, 25(8):1223~1227.
[72] Kim D W, Lee K Y, Lee D, et al. Evaluation of the performance of clustering algorithms in kernel-induced feature space[J]. Pattern Recognition, 2005, 38(4):607～611.
[73]伍忠东,高新波,谢维信.基于核方法的模糊聚类算法[J].西安电子科技大学学报(自然科学版), 2004,31(4):533～537.
[74]胡旺,李志蜀.一种更简化而高效的粒子群优化算法[J].软件学报, 2007, 18(4):861~868.
[75]贵敏,贾建援,韩琪.粒子群优化算法的惯性权值递减策略研究[J].西安交通大学学报, 2006, 40(1):53-56.
[76]陶卿,姚穗,范劲松等.一种新的机器学习方法: Support Vector Manhines[J].模式识别与人工智能, 2000, 13(3):285~290.
[77] Jonathan Robinson, Vojislav Kecman. Combing support vector machine learning with the discrete cosine transform in image compression[J]. IEEE Transactions on Neural Networks, 2003, 14(4) : 950~958.
[78]张丽萍,李元诚.基于支持向量机的遥感图像压缩方法[J].计算机工程与应用, 2006, 27:200~202.
[79]张丽萍.小波支持向量机在遥感图像压缩中的应用[D].天津:天津大学, 2006.