基于小波变换的遥感图像处理研究
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摘要
小波变换是遥感图像处理技术中的一项重要技术,如何利用小波变换技术遥感图像进行处理是在图像处理技术中有着广泛的应用前景。本文以图像处理技术存在一定的瓶颈、遥感图像的广泛应用、信息融合技术为研究背景,较深入的对遥感图像特点进行分析、基于纹理一致性测度对遥感图像进行融合进行了研究,主要研究的内容包括:
1、对遥感图像进行了系统的研究。全面总结了遥感图像的特点,遥感目标的特征。由于遥感图像最为显著的特点是数据量大和图像信息丰富,而遥感技术是目前为止能够提供动态观测数据的唯一手段,因此,遥感图像处理方法的研究对于民用和军事领域都是至关重要的。
2、详细地介绍了小波变换的相关概念、遥感图像成像、处理遥感图像的意义、图像处理应用的现状、小波变换在遥感图像处理中的应用,以及目前国内外遥感图像处理技术的发展现状。针对原始图像影像本身在成像过程中由于种种的原因引起的图像质量的问题,本文讨论了遥感图像在处理前需要进行的前期处理步骤(包括几何校正、去噪等)处理步骤。并基于小波变换对遥感图像进行边缘检测、图像融合,并对各种融合图像的效果进行分析,得出结论。
3、对像素级图像中的平滑滤波、高通滤波融合法、小波变换等方法进行了详细的讨论。叙述了小波基函数、连续小波变换、离散小波变换、二进小波变换、二维小波变换、多分辨分析小波变换理论。讲述了小波分解的选取对遥感图像处理结果的影响,为小波基函数和小波分解的选择提供了依据,并通过实验和对其特点和性能进行分析后做了详细的对比,结果表明:基于小波变换方法对遥感图像的处理在图像的纹理和结构信息上可以得到很好地保持,还可以使遥感图像的光谱特征信息也保持非常完整。
Wavelet transform is a remote sensing image processing technology in an important technology, how to use wavelet transform image processing of remote sensing image processing technology in a broad application prospects. In this paper, there is a certain image processing bottleneck, the extensive application of remote sensing, information fusion technology research background, more in-depth features on the remote sensing image analysis, texture homogeneity measure based on the fusion of remote sensing images have been studied, we study include:
1、The remote sensing images of the system. A comprehensive summary of the characteristics of remote sensing images, remote sensing target features. As the remote sensing images is the most remarkable features of large data and image information-rich, and remote sensing technology is now able to provide the only means of dynamic observation data, therefore, remote sensing image processing method for the civilian and military fields is vital .
2、Detailed description of the wavelet transform concepts, remote sensing images imaging, the significance of remote sensing image processing, image processing, the status of applications, wavelet transform in remote sensing image processing applications, and the current domestic and foreign remote-sensing image processing technology status. Image for the original image in the imaging process itself causes a variety of image quality issues are discussed before the need for remote sensing image processing carried out in the pre-processing steps (including geometric correction, denoising, etc.) processing steps. And remote sensing image based on wavelet edge detection, image fusion, and the effects of various fusion image analysis, draw conclusions.
3、On the pixel-level image filtering, high pass filter fusion, wavelet transform methods are discussed in detail. Describes the wavelet function, continuous wavelet transform, discrete wavelet transform, dyadic wavelet transform, wavelet transform, multiresolution analysis, wavelet transform theory. About the selection of the wavelet decomposition of the results on the impact of remote sensing image processing for the wavelet function and wavelet decomposition provides the basis for selection, and through experiments and their characteristics and performance analysis a detailed comparison, the results show that: based on wavelet transform the image processing of remote sensing image texture and structure information can be well maintained, but also to the spectral characteristics of remote sensing images also maintained very complete information.
1、对遥感图像进行了系统的研究。全面总结了遥感图像的特点,遥感目标的特征。由于遥感图像最为显著的特点是数据量大和图像信息丰富,而遥感技术是目前为止能够提供动态观测数据的唯一手段,因此,遥感图像处理方法的研究对于民用和军事领域都是至关重要的。
2、详细地介绍了小波变换的相关概念、遥感图像成像、处理遥感图像的意义、图像处理应用的现状、小波变换在遥感图像处理中的应用,以及目前国内外遥感图像处理技术的发展现状。针对原始图像影像本身在成像过程中由于种种的原因引起的图像质量的问题,本文讨论了遥感图像在处理前需要进行的前期处理步骤(包括几何校正、去噪等)处理步骤。并基于小波变换对遥感图像进行边缘检测、图像融合,并对各种融合图像的效果进行分析,得出结论。
3、对像素级图像中的平滑滤波、高通滤波融合法、小波变换等方法进行了详细的讨论。叙述了小波基函数、连续小波变换、离散小波变换、二进小波变换、二维小波变换、多分辨分析小波变换理论。讲述了小波分解的选取对遥感图像处理结果的影响,为小波基函数和小波分解的选择提供了依据,并通过实验和对其特点和性能进行分析后做了详细的对比,结果表明:基于小波变换方法对遥感图像的处理在图像的纹理和结构信息上可以得到很好地保持,还可以使遥感图像的光谱特征信息也保持非常完整。
Wavelet transform is a remote sensing image processing technology in an important technology, how to use wavelet transform image processing of remote sensing image processing technology in a broad application prospects. In this paper, there is a certain image processing bottleneck, the extensive application of remote sensing, information fusion technology research background, more in-depth features on the remote sensing image analysis, texture homogeneity measure based on the fusion of remote sensing images have been studied, we study include:
1、The remote sensing images of the system. A comprehensive summary of the characteristics of remote sensing images, remote sensing target features. As the remote sensing images is the most remarkable features of large data and image information-rich, and remote sensing technology is now able to provide the only means of dynamic observation data, therefore, remote sensing image processing method for the civilian and military fields is vital .
2、Detailed description of the wavelet transform concepts, remote sensing images imaging, the significance of remote sensing image processing, image processing, the status of applications, wavelet transform in remote sensing image processing applications, and the current domestic and foreign remote-sensing image processing technology status. Image for the original image in the imaging process itself causes a variety of image quality issues are discussed before the need for remote sensing image processing carried out in the pre-processing steps (including geometric correction, denoising, etc.) processing steps. And remote sensing image based on wavelet edge detection, image fusion, and the effects of various fusion image analysis, draw conclusions.
3、On the pixel-level image filtering, high pass filter fusion, wavelet transform methods are discussed in detail. Describes the wavelet function, continuous wavelet transform, discrete wavelet transform, dyadic wavelet transform, wavelet transform, multiresolution analysis, wavelet transform theory. About the selection of the wavelet decomposition of the results on the impact of remote sensing image processing for the wavelet function and wavelet decomposition provides the basis for selection, and through experiments and their characteristics and performance analysis a detailed comparison, the results show that: based on wavelet transform the image processing of remote sensing image texture and structure information can be well maintained, but also to the spectral characteristics of remote sensing images also maintained very complete information.
引文
[1]张克军.遥感图像特征提取方法研究[D].西北工业大学,硕士学位论文,2007
[2]程子敬,周孝宽.星载高速图像数据压缩原理样机的研制[J].北京航空航天大学学报,1999:25(16).
[3]李强,王正志.遥感图像数字处理系统的发展综述[J].遥感技术与应用,1998.13(4):54-58.
[4]孙延奎.小波分析及其应用[M].北京:机械工业出版社,2005.
[5] Mallat S.A Wavelet Tour of Signal Processing 2nd.Academic Press[M].1999.
[6]彭玉华.小波变换与工程应用[J].测绘出版社,2002(1).
[7]徐佩霞,孙功宪.小波分析与应用实例[J].中国科学技术出版社,2001(1).
[8]徐晨,赵瑞珍,甘小冰.小波分析应用算法[J].科学出版社,2004:64～66.
[9]章毓晋.图像处理和分析[J].清华大学出版社,1999.
[10]冈萨雷斯(Gonzalez,R.C.)等.数字图像处理(第二版) [J].北京:电子工业出版社,2003.
[11] L.G.Brown.A Survey of Image Rregistration Techniques.ACM Computer Survey[J]. 1992:325-376 .
[12]刘长柱.一种基于小波变换的全色遥感图像与彩色多光谱图像融合算法[J].空间电子技术,2002(1):8-14.
[13]丁海玲,林宝军,张善从.高速遥感图像实时压缩仿真系统研究[J].计算机仿真.2006.
[14]霍宏涛,游先祥.小波变换在遥感图像融合中的应用研究[J].中国图像图形学报,2003,8(5):551-556.
[15]张德祥,高清维,陈军宁.基于纹理一致性测度小波变换的遥感图像融合算法[J].仪器仪表学报,2006.
[16]李树涛,王耀南,张昌凡.多传感器图像融合的客观评价与分析[J].仪器仪表学报,2002:65l-654.
[17] Hong Wang,ZhongLiang Jing,JianXun Li.Image fusion using non-separable wavelet frame[J].Chinese optics letters,2003,1(9):523-526.
[18]彭玉华.小波变换与工程应用[M].北京:科学出版社,2003.
[19] Argenti E Alparone L.Speckle removal from SAR images in the undecimated wavelet domain[J]. IEEE Trans,Geosci.Remote Sensing,2002,40(11):2363-2374.
[20] Abrishami M H,zoujV,M.J,Dehghani M,Bayesian-based speckle reduction using wavelet transforms[A].Accepted in the Conference on Applications of Digital Image Processing in 49th SHE annual meeting(C),Denver.CO USA,2004.
[21] Dehghani,M.An efficient algorithm for Bayesian-based speckle reduction of SAR imagesusing wavelet transform[D].M.Sc.thesis.2003.
[22] ZHANG de–xiang,GAO Qing-wei,CHEN Jun-ning.Single Channel Speech Enhancement by Denoising using stationary wavelet transform[J].Journal of Electronic Science and Technology ofChina,2006,1.4(1):39-42.
[23]何锦平.基于小波多分辨分析的图像增强及其应用研究[D].西北工业大学,2003.
[24]霍宏涛,游先祥.小波变换在遥感图像融合中的应用研究[J].北京:中国图像图形学报,2003,8(5):551-556.
[26]战场地理信息系统中遥感图像处理和三维综合建模技术研究[D].云南:昆明理工大学,2008.
[25] Jin Wu,Jinwen Tian,Jian Lin.Multiscale Image Data Fusion Based on Local Deviation of Wavelet Transform[C],IEEE,ICIMA2004,2004:677-680.
[26] Lee J S. Speckle suppression and analysis for synthetic aperture radar image[J].Opt.Eng.1986, 25(5):636-643.
[27] X.Zong,A. F. Laine,E A.Geiser and D.C.Wilson.De-noising and contrast enhancement via wavelet shrinkage and non-linear adaptive gain[J].Wavelet Applications 3:Proceeding ofSPIE,19962762:566-574.
[28] MinhN.Do,directionalmultiresolutionimagerepresentations[D].Departmentof Electrical and Computer Engineering University of Illinois at Urbana-Champaign.2003.
[29] Truong T Nguyen Soontom Oraintara.Multiresolution Direction Filterbanks:Theory.Design and Applications[J].IEEE Transactions On Signal Processing,2005 (53):3895—3905.
[30] Argenti F,Alparone L.Speckle removal from SAR images in the undecimated wavelet domain[J]IEEE Trans.Geosci.Remote Sensing,2002,40(11):2 363-2374.
[31] Abrishami M H,zouj V,M.J,Dehghani M,Bayesian-based speckle reduction using wavelet transforms[A].Accepted in the Conference on Applications of Digital Image Processing in 49th SHE annual meeting(C),Denver,CO USA,2004.
[32] Dehghani,M.An efficient algorithm for Bayesian-based speckle reduction of SAR images using wavelet transform[D],M.Sc.thesis.2003.
[33] Min D,Cheng P,Chan A K,et a1.Bayesian wavelet shrinkage with edge detection for SAP, image despeckling[J].In:IEEE Trans.Geosci.Remote Sensing,2004,42(8):1642-1648.
[34] Xavier Otazu,Maria Gonzalez-Audicana ,Octavi Fors,and Jorge Nt)?ez.Introduction of Sensor Spectral Response Into Image Fusion Methods.Application to Wavelet-Based Methods[J].IEEE Transactions on Geoscience And Remote Sensing,2005,43(10):2376-2385.
[35]基于小波变换的图像处理算法研究[D].西安电子科技大学, 2008.
[36]小波理论在图像去噪与纹理分析中的应用研究[D].合肥工业大学,2008.
[37] Daily M.L,Farr T,Elachi C.Geologic.interpretation from Composited Radar and Land sat Imagery.Photo grammetric Engineering and Remote Sensing.1979,45(8):1109-1116.
[38] Laner D.T Todd WJ.Land Cover Mapping with Merged Land sat RBV and MSS Stereoscopic Images. Proc.Of of ASP Fall Technical Conference,1981:680-689.
[39] R. R. Tenney,N.R.Sandell.Detection with distribute sensors.IEEE Trails On AES.1989,17(4):501-510.
[40] Scheunders P. Multiscale edge representation applied to image fusion[J].2002.
[41] Qu Ji-shuang,Wang Chao.A wavelet package-based data fusion method for multi.Temporal remote sensing image processing[J].Center for remote imaging sensing and processing(CRISP).2001.
[42] POHL C,VAN Genderen.Multisensor image fusion in remote sensing:concepts, Methods methods and applications.Int. J. Remote Sensing.1998,19(5):823-854.
[43]刘贵喜,杨万海,西安电子科技大学博士学位论文.多传感器图像融合方法研究.2001:15-17.
[44]李军,周月琴,李德仁.影像局部直方图匹配滤波技术用于遥感影像数据融合,测绘学报,1999(8):227-231.
[45] Robert C.Krempien,Sascha Daeuber, et al.Image fusion of CT and MRl data enables improved target volume definition in 3D-brachytherapy treatment planning, Brachytherapy,2003(2):164-171.
[46] N.Riefenstahl,G.Krell,R.Calow.A multimodal image fusion framework applied in radiotherapy, in the TV 2001 Information Visualisation Conference, London .25-27,2001(7):173-178.
[47] LC,KAY MG,Multisensor integration and fusion for intelligent machines and systems.US:Abberx Publishing Corporation.1995:321-456.
[48]黄卉.基于小波变换的图像融合方法研究.合肥工业大学,2005.
[49]李弼程,罗建书.小波分析及应用[J].北京:电子工业出版社,2003.
[50] Te-Ming Tu,Shun-Chi Su,Hsuen-Chyun.A new look at HIS-like image fusion methods,Information Fusion 2001(2):177-186.
[51]陈德超,周海波,陈中原等。TM与SPOT影像融合算法比较研究[J].遥感技术与应用,2001.2:110-115.
[52]刘贵喜.赵曙光,陈文锦.双正交小波变换多分辨率图像融合方法[J].光电工程,2004,31(4):50～53.
[53]张德祥,高清维,陈军宁.基于纹理一致性测度小波变换的遥感图像融合算法[J].仪器仪表学报.2006.
[2]程子敬,周孝宽.星载高速图像数据压缩原理样机的研制[J].北京航空航天大学学报,1999:25(16).
[3]李强,王正志.遥感图像数字处理系统的发展综述[J].遥感技术与应用,1998.13(4):54-58.
[4]孙延奎.小波分析及其应用[M].北京:机械工业出版社,2005.
[5] Mallat S.A Wavelet Tour of Signal Processing 2nd.Academic Press[M].1999.
[6]彭玉华.小波变换与工程应用[J].测绘出版社,2002(1).
[7]徐佩霞,孙功宪.小波分析与应用实例[J].中国科学技术出版社,2001(1).
[8]徐晨,赵瑞珍,甘小冰.小波分析应用算法[J].科学出版社,2004:64～66.
[9]章毓晋.图像处理和分析[J].清华大学出版社,1999.
[10]冈萨雷斯(Gonzalez,R.C.)等.数字图像处理(第二版) [J].北京:电子工业出版社,2003.
[11] L.G.Brown.A Survey of Image Rregistration Techniques.ACM Computer Survey[J]. 1992:325-376 .
[12]刘长柱.一种基于小波变换的全色遥感图像与彩色多光谱图像融合算法[J].空间电子技术,2002(1):8-14.
[13]丁海玲,林宝军,张善从.高速遥感图像实时压缩仿真系统研究[J].计算机仿真.2006.
[14]霍宏涛,游先祥.小波变换在遥感图像融合中的应用研究[J].中国图像图形学报,2003,8(5):551-556.
[15]张德祥,高清维,陈军宁.基于纹理一致性测度小波变换的遥感图像融合算法[J].仪器仪表学报,2006.
[16]李树涛,王耀南,张昌凡.多传感器图像融合的客观评价与分析[J].仪器仪表学报,2002:65l-654.
[17] Hong Wang,ZhongLiang Jing,JianXun Li.Image fusion using non-separable wavelet frame[J].Chinese optics letters,2003,1(9):523-526.
[18]彭玉华.小波变换与工程应用[M].北京:科学出版社,2003.
[19] Argenti E Alparone L.Speckle removal from SAR images in the undecimated wavelet domain[J]. IEEE Trans,Geosci.Remote Sensing,2002,40(11):2363-2374.
[20] Abrishami M H,zoujV,M.J,Dehghani M,Bayesian-based speckle reduction using wavelet transforms[A].Accepted in the Conference on Applications of Digital Image Processing in 49th SHE annual meeting(C),Denver.CO USA,2004.
[21] Dehghani,M.An efficient algorithm for Bayesian-based speckle reduction of SAR imagesusing wavelet transform[D].M.Sc.thesis.2003.
[22] ZHANG de–xiang,GAO Qing-wei,CHEN Jun-ning.Single Channel Speech Enhancement by Denoising using stationary wavelet transform[J].Journal of Electronic Science and Technology ofChina,2006,1.4(1):39-42.
[23]何锦平.基于小波多分辨分析的图像增强及其应用研究[D].西北工业大学,2003.
[24]霍宏涛,游先祥.小波变换在遥感图像融合中的应用研究[J].北京:中国图像图形学报,2003,8(5):551-556.
[26]战场地理信息系统中遥感图像处理和三维综合建模技术研究[D].云南:昆明理工大学,2008.
[25] Jin Wu,Jinwen Tian,Jian Lin.Multiscale Image Data Fusion Based on Local Deviation of Wavelet Transform[C],IEEE,ICIMA2004,2004:677-680.
[26] Lee J S. Speckle suppression and analysis for synthetic aperture radar image[J].Opt.Eng.1986, 25(5):636-643.
[27] X.Zong,A. F. Laine,E A.Geiser and D.C.Wilson.De-noising and contrast enhancement via wavelet shrinkage and non-linear adaptive gain[J].Wavelet Applications 3:Proceeding ofSPIE,19962762:566-574.
[28] MinhN.Do,directionalmultiresolutionimagerepresentations[D].Departmentof Electrical and Computer Engineering University of Illinois at Urbana-Champaign.2003.
[29] Truong T Nguyen Soontom Oraintara.Multiresolution Direction Filterbanks:Theory.Design and Applications[J].IEEE Transactions On Signal Processing,2005 (53):3895—3905.
[30] Argenti F,Alparone L.Speckle removal from SAR images in the undecimated wavelet domain[J]IEEE Trans.Geosci.Remote Sensing,2002,40(11):2 363-2374.
[31] Abrishami M H,zouj V,M.J,Dehghani M,Bayesian-based speckle reduction using wavelet transforms[A].Accepted in the Conference on Applications of Digital Image Processing in 49th SHE annual meeting(C),Denver,CO USA,2004.
[32] Dehghani,M.An efficient algorithm for Bayesian-based speckle reduction of SAR images using wavelet transform[D],M.Sc.thesis.2003.
[33] Min D,Cheng P,Chan A K,et a1.Bayesian wavelet shrinkage with edge detection for SAP, image despeckling[J].In:IEEE Trans.Geosci.Remote Sensing,2004,42(8):1642-1648.
[34] Xavier Otazu,Maria Gonzalez-Audicana ,Octavi Fors,and Jorge Nt)?ez.Introduction of Sensor Spectral Response Into Image Fusion Methods.Application to Wavelet-Based Methods[J].IEEE Transactions on Geoscience And Remote Sensing,2005,43(10):2376-2385.
[35]基于小波变换的图像处理算法研究[D].西安电子科技大学, 2008.
[36]小波理论在图像去噪与纹理分析中的应用研究[D].合肥工业大学,2008.
[37] Daily M.L,Farr T,Elachi C.Geologic.interpretation from Composited Radar and Land sat Imagery.Photo grammetric Engineering and Remote Sensing.1979,45(8):1109-1116.
[38] Laner D.T Todd WJ.Land Cover Mapping with Merged Land sat RBV and MSS Stereoscopic Images. Proc.Of of ASP Fall Technical Conference,1981:680-689.
[39] R. R. Tenney,N.R.Sandell.Detection with distribute sensors.IEEE Trails On AES.1989,17(4):501-510.
[40] Scheunders P. Multiscale edge representation applied to image fusion[J].2002.
[41] Qu Ji-shuang,Wang Chao.A wavelet package-based data fusion method for multi.Temporal remote sensing image processing[J].Center for remote imaging sensing and processing(CRISP).2001.
[42] POHL C,VAN Genderen.Multisensor image fusion in remote sensing:concepts, Methods methods and applications.Int. J. Remote Sensing.1998,19(5):823-854.
[43]刘贵喜,杨万海,西安电子科技大学博士学位论文.多传感器图像融合方法研究.2001:15-17.
[44]李军,周月琴,李德仁.影像局部直方图匹配滤波技术用于遥感影像数据融合,测绘学报,1999(8):227-231.
[45] Robert C.Krempien,Sascha Daeuber, et al.Image fusion of CT and MRl data enables improved target volume definition in 3D-brachytherapy treatment planning, Brachytherapy,2003(2):164-171.
[46] N.Riefenstahl,G.Krell,R.Calow.A multimodal image fusion framework applied in radiotherapy, in the TV 2001 Information Visualisation Conference, London .25-27,2001(7):173-178.
[47] LC,KAY MG,Multisensor integration and fusion for intelligent machines and systems.US:Abberx Publishing Corporation.1995:321-456.
[48]黄卉.基于小波变换的图像融合方法研究.合肥工业大学,2005.
[49]李弼程,罗建书.小波分析及应用[J].北京:电子工业出版社,2003.
[50] Te-Ming Tu,Shun-Chi Su,Hsuen-Chyun.A new look at HIS-like image fusion methods,Information Fusion 2001(2):177-186.
[51]陈德超,周海波,陈中原等。TM与SPOT影像融合算法比较研究[J].遥感技术与应用,2001.2:110-115.
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