基于小波的遥感图像薄云去除的研究与实现
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摘要
遥感图像的薄云和雾霭覆盖是遥感图像处理中最常遇到的一种情况，有效的去除云覆盖的影响是遥感图像预处理阶段的重要任务。本文在这里所介绍的去除云覆盖现象的方法属于数字图像盲恢复的研究领域。
本文结合中巴资源卫星地面应用系统图像应用处理分系统“课题的需要，采用数字图像变换的方法来去除无辅助信息的遥感图像薄云覆盖，通过对薄云覆盖的遥感图像进行变换域处理，进行高通滤波去除云覆盖所在的低频信息，通过同态滤波并对图像进行空频分析来增强图像背景。然后在此基础上，通过引入小波变换，利用小波变换的多分辨率分析来进一步改善效果。
小波分析是目前国际上公认的最新时间-频率分析工具，由于其“自适应性”和“数学显微镜性质”而成为许多学科共同关注的焦点，本文利用小波改善传统数字图像处理方法，取得了一定效果。
本文提出多种评价模式，包括目视判别和统计分析，经过大量实验证明，本文所探讨的方法对于去除无辅助信息的大面积含云遥感图像的去云处理具有很好的可操作性和有效性。
本文主要的创新之处在于：
1．基于传统遥感图像去云的思想，建立了一种新的图像薄云去除数学模型，并在此基础上提出了空频分析通过灰度补偿改善去云效果，同时结合IHS变换改善彩色图像薄云去除，取得了很好的结果。
2．将传统高通滤波和同态滤波的思想与小波变换相结合，充分利用小波变换的多分辨率分析，更好的保持了图像的细节信息。
Cloud cover noise often occurs in remote sense image processing. It is a very important task to remove the effects of cloud cover. How to realize it is the necessary stage of bind restoration of remote sense image processing.
This paper introduces a way to remove the effects of cloud by frequency transform. Here, information of cloud cover is removed by high-pass filtering and information of background is enhanced by homomorphic filtering and spatial-frequency analysis. Furthermore, a new way is particularly discussed based on wavelet transform.
After strict tests, we find that multiresolution analysis based on wavelet transform can improve the effect of the method of Fourier transform. The results show this approach is effective when there is massive cloud cover on the remote sense image.
Wavelet analysis is internationally recognized up to the minute tools for analyzing time-frequency. It is chiefly due to the "adaptive feature" and "mathematical micro-telescope feature". This paper introduce the application of wavelet to image processing.
This paper has such special points as:
1. Based on the idea of removing cloud cover, a new mathematical model has been constructed. This paper introduces a way to improve the effect of removing cloud cover by spatial-frequency analysis and a new method to improve the effect of removing cloud cover on color image by IHS transform.
2. Wavelet transform and conventional high-pass filtering method have been combined. Using multiresolution analysis of wavelet transform, details of the result image after being removed cloud cover are greatly improved.
本文结合中巴资源卫星地面应用系统图像应用处理分系统“课题的需要，采用数字图像变换的方法来去除无辅助信息的遥感图像薄云覆盖，通过对薄云覆盖的遥感图像进行变换域处理，进行高通滤波去除云覆盖所在的低频信息，通过同态滤波并对图像进行空频分析来增强图像背景。然后在此基础上，通过引入小波变换，利用小波变换的多分辨率分析来进一步改善效果。
小波分析是目前国际上公认的最新时间-频率分析工具，由于其“自适应性”和“数学显微镜性质”而成为许多学科共同关注的焦点，本文利用小波改善传统数字图像处理方法，取得了一定效果。
本文提出多种评价模式，包括目视判别和统计分析，经过大量实验证明，本文所探讨的方法对于去除无辅助信息的大面积含云遥感图像的去云处理具有很好的可操作性和有效性。
本文主要的创新之处在于：
1．基于传统遥感图像去云的思想，建立了一种新的图像薄云去除数学模型，并在此基础上提出了空频分析通过灰度补偿改善去云效果，同时结合IHS变换改善彩色图像薄云去除，取得了很好的结果。
2．将传统高通滤波和同态滤波的思想与小波变换相结合，充分利用小波变换的多分辨率分析，更好的保持了图像的细节信息。
Cloud cover noise often occurs in remote sense image processing. It is a very important task to remove the effects of cloud cover. How to realize it is the necessary stage of bind restoration of remote sense image processing.
This paper introduces a way to remove the effects of cloud by frequency transform. Here, information of cloud cover is removed by high-pass filtering and information of background is enhanced by homomorphic filtering and spatial-frequency analysis. Furthermore, a new way is particularly discussed based on wavelet transform.
After strict tests, we find that multiresolution analysis based on wavelet transform can improve the effect of the method of Fourier transform. The results show this approach is effective when there is massive cloud cover on the remote sense image.
Wavelet analysis is internationally recognized up to the minute tools for analyzing time-frequency. It is chiefly due to the "adaptive feature" and "mathematical micro-telescope feature". This paper introduce the application of wavelet to image processing.
This paper has such special points as:
1. Based on the idea of removing cloud cover, a new mathematical model has been constructed. This paper introduces a way to improve the effect of removing cloud cover by spatial-frequency analysis and a new method to improve the effect of removing cloud cover on color image by IHS transform.
2. Wavelet transform and conventional high-pass filtering method have been combined. Using multiresolution analysis of wavelet transform, details of the result image after being removed cloud cover are greatly improved.
引文
[1]赵忠明，朱重光．遥感图像中薄云的去除方法，环境遥感．Vol．11，No．2，1996．8
[2]赵荣椿，赵忠明，崔甦生．数字图像处理导论．西北工业大学出版社，1995
[3]R．C．冈萨雷斯，P．温茨著(李书梁等译)．数字图像处理．科学出版社，1989
[4]程正兴．小波分析算法与应用．西安交通大学出版社，1998
[5]章孝灿，黄智才，赵元洪．遥感数字图像处理．浙江大学出版社，1997
[6]李建平．小波分析与信号处理——理论、应用及软件实现．重庆出版社，1997
[7]方勇，常本义．联合应用多传感器影像消除云层遮挡影响的研究．中国图象图形学报(A版)，2001，6(2)：138-141
[8]陈述彭．“数字地球”战略及其制高点．遥感学报，1999，3(4)，247-253
[9]张新明，沈兰荪．基于小波的同态波器用于图像对比度增强．电子学报，2001，29(4)：531-533
[10]张晔，黄秀明．小波变换及其在图像处理中的小波特性分析．中国图象图形学报，1997，2(7)：480-482
[11]何国金．小波变换在遥感影像处理中的应用综述．遥感信息，1999．1，No．1
[12]胡国生，鲍虎军．彩色图像的小波变换编码．自然科学进展——国家重点实验室通讯，1993，3：26-34
[13]Mallat S.A theory for multiresolution signal decomposition: the wavelet representation [J]. IEEE Trans, PAMI, 1989, 11(7): 674-693
[14]Y. Zhang. A new merging method and its spectral and spatial effects.INT. J. Remote Sensing, 1999, 20(10): 2003-2014
[15]Singh. Digital change detection techniques using remoteing sensed data. INT. J. Remote Sensing, 1989, 10: 989-1003
[16]Yesou, H., Besnus, Y., and Rolet. Extraction of spectral information from Landsat TM data and Merger with SPOT-PAN imagery——a contribution to the study of geological structures. ISPRS Journal of Photogrammetry and Remote Sensing. 1996, 48: 23-36
[17]Fonseca Leila M G, Manjunath B S.. Registration techniques for multisensor remotely sensed imagery. Photogrammetric Engineering & Remote Sensing, 1996, 62(9): 1049-1056
[18]赵松年，熊小芸．子波变换与子波分析[M]．北京：电子工业出版社，1997
[19]姬光荣，王国宁，王宁．基于小波变换的多尺度边缘检测[J]．中国图象图形学报，1997，2(10)：717-720
[20]秦前清，杨宗凯．实用小波分析．西安电子科技大学出版社，1994
[21]石启星，田金文，柳健．小波变换及其在信号处理中的应用．遥测遥控，1996，17(3)：56-61
[22]耿则勋，钱曾波．遥感影像无损压缩编码的实验分析与改进．测绘学报，1996，25(4)：262-265
[23]张晔，黄秀明．小波变换及在图像处理中的小波特性分析．中国图象图形学报，1997，2(7)：480-483
[24]李德仁，邵巨良．影像融合与复原的小波模型．武汉测绘科技大学学报，1996，21(3)：213-217
[25]邵巨良，李德仁．小波理论及其在影像边缘检测中的应用．测绘学报，1993，22(2)
[26]宁书年，吕松棠，杨小勤等．遥感图像处理与应用．地震出版社，1995
[27]朱长青，杨启和，朱文忠．基于小波变换特征的遥感地貌影像纹理分析和分类．测绘学报，1996，25(4)：252-256
[28]魏海涛，郑南宁，张志华．具有紧支撑的正交小波变换滤波器的设计原理和一种图像精确分解与重构算法．电子学报，2000，28(10)：17-19
[29]吴均．基于内容的图像检索技术及其在遥感图像中的应用研究：[学位论文]．2001
[30]郝鹏威．数字图像空间分辨率改善的方法研究：[学位论文]．1997
[31]D.A Yocky. Image Merging and data Fusion by Means of the Discrete Twodimensional Wavelet Transform. J. Opt. Soc. Am. A, 1995, 12(9): 1834-1841
[32]何国金．卫星遥感数据开采与知识发现的信息论方法：[学位论文]．1998
[33]S. Mallat. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation, IEEE Trans. on Pattern Analysis and Machine Intelligence.1989, 11: 674-693
[34]张新明，沈兰荪．电子学报．2001，29(4)：531-533
[35]L.I. Coicu, Harley. R. Myler. Practical consideratrions on color image enhancement using homomorphic filtering[J] Journal of Electronic Imagring, 1997, 6(1): 108-113
[36]J.D. Fanhnesstock, R.A. Schowengerdt. Spatially variant contrast
enhancement using local range modification {H}. Optical Eng, 1983,22(3): 378-381
[37]S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromatrie, A. Geselowitz,T. Greer, B. H. Romeny, J. B. Zimmerman, K. Zuiderveld. Adaptive histogram equalization and its variances [J]. Comput. Vision Graphics Image Process. 1987, 39: 355-368
[38]J. S. Lee. Digital image enhancement and noise filtering by use of local statistics[J]. IEEE trans. Pattern Anal. Mach. Intell., 1980, 2(3): 165-168
[39]A.F. Laine, Schuler, J. Fan, W. Huda. Mammographic feature enhancement by multicale analysis[J]. IEEE Trans, Medical Imaging, 1994, 13(4): 725-740
[40]J. lu, D. M. Herly,Jr, J. B. Weaver. Contrast enhancement of medical images using multiscale edge representation [J]. Opt. Eng, 1994, 13(7): 2151-2161
[2]赵荣椿，赵忠明，崔甦生．数字图像处理导论．西北工业大学出版社，1995
[3]R．C．冈萨雷斯，P．温茨著(李书梁等译)．数字图像处理．科学出版社，1989
[4]程正兴．小波分析算法与应用．西安交通大学出版社，1998
[5]章孝灿，黄智才，赵元洪．遥感数字图像处理．浙江大学出版社，1997
[6]李建平．小波分析与信号处理——理论、应用及软件实现．重庆出版社，1997
[7]方勇，常本义．联合应用多传感器影像消除云层遮挡影响的研究．中国图象图形学报(A版)，2001，6(2)：138-141
[8]陈述彭．“数字地球”战略及其制高点．遥感学报，1999，3(4)，247-253
[9]张新明，沈兰荪．基于小波的同态波器用于图像对比度增强．电子学报，2001，29(4)：531-533
[10]张晔，黄秀明．小波变换及其在图像处理中的小波特性分析．中国图象图形学报，1997，2(7)：480-482
[11]何国金．小波变换在遥感影像处理中的应用综述．遥感信息，1999．1，No．1
[12]胡国生，鲍虎军．彩色图像的小波变换编码．自然科学进展——国家重点实验室通讯，1993，3：26-34
[13]Mallat S.A theory for multiresolution signal decomposition: the wavelet representation [J]. IEEE Trans, PAMI, 1989, 11(7): 674-693
[14]Y. Zhang. A new merging method and its spectral and spatial effects.INT. J. Remote Sensing, 1999, 20(10): 2003-2014
[15]Singh. Digital change detection techniques using remoteing sensed data. INT. J. Remote Sensing, 1989, 10: 989-1003
[16]Yesou, H., Besnus, Y., and Rolet. Extraction of spectral information from Landsat TM data and Merger with SPOT-PAN imagery——a contribution to the study of geological structures. ISPRS Journal of Photogrammetry and Remote Sensing. 1996, 48: 23-36
[17]Fonseca Leila M G, Manjunath B S.. Registration techniques for multisensor remotely sensed imagery. Photogrammetric Engineering & Remote Sensing, 1996, 62(9): 1049-1056
[18]赵松年，熊小芸．子波变换与子波分析[M]．北京：电子工业出版社，1997
[19]姬光荣，王国宁，王宁．基于小波变换的多尺度边缘检测[J]．中国图象图形学报，1997，2(10)：717-720
[20]秦前清，杨宗凯．实用小波分析．西安电子科技大学出版社，1994
[21]石启星，田金文，柳健．小波变换及其在信号处理中的应用．遥测遥控，1996，17(3)：56-61
[22]耿则勋，钱曾波．遥感影像无损压缩编码的实验分析与改进．测绘学报，1996，25(4)：262-265
[23]张晔，黄秀明．小波变换及在图像处理中的小波特性分析．中国图象图形学报，1997，2(7)：480-483
[24]李德仁，邵巨良．影像融合与复原的小波模型．武汉测绘科技大学学报，1996，21(3)：213-217
[25]邵巨良，李德仁．小波理论及其在影像边缘检测中的应用．测绘学报，1993，22(2)
[26]宁书年，吕松棠，杨小勤等．遥感图像处理与应用．地震出版社，1995
[27]朱长青，杨启和，朱文忠．基于小波变换特征的遥感地貌影像纹理分析和分类．测绘学报，1996，25(4)：252-256
[28]魏海涛，郑南宁，张志华．具有紧支撑的正交小波变换滤波器的设计原理和一种图像精确分解与重构算法．电子学报，2000，28(10)：17-19
[29]吴均．基于内容的图像检索技术及其在遥感图像中的应用研究：[学位论文]．2001
[30]郝鹏威．数字图像空间分辨率改善的方法研究：[学位论文]．1997
[31]D.A Yocky. Image Merging and data Fusion by Means of the Discrete Twodimensional Wavelet Transform. J. Opt. Soc. Am. A, 1995, 12(9): 1834-1841
[32]何国金．卫星遥感数据开采与知识发现的信息论方法：[学位论文]．1998
[33]S. Mallat. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation, IEEE Trans. on Pattern Analysis and Machine Intelligence.1989, 11: 674-693
[34]张新明，沈兰荪．电子学报．2001，29(4)：531-533
[35]L.I. Coicu, Harley. R. Myler. Practical consideratrions on color image enhancement using homomorphic filtering[J] Journal of Electronic Imagring, 1997, 6(1): 108-113
[36]J.D. Fanhnesstock, R.A. Schowengerdt. Spatially variant contrast
enhancement using local range modification {H}. Optical Eng, 1983,22(3): 378-381
[37]S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromatrie, A. Geselowitz,T. Greer, B. H. Romeny, J. B. Zimmerman, K. Zuiderveld. Adaptive histogram equalization and its variances [J]. Comput. Vision Graphics Image Process. 1987, 39: 355-368
[38]J. S. Lee. Digital image enhancement and noise filtering by use of local statistics[J]. IEEE trans. Pattern Anal. Mach. Intell., 1980, 2(3): 165-168
[39]A.F. Laine, Schuler, J. Fan, W. Huda. Mammographic feature enhancement by multicale analysis[J]. IEEE Trans, Medical Imaging, 1994, 13(4): 725-740
[40]J. lu, D. M. Herly,Jr, J. B. Weaver. Contrast enhancement of medical images using multiscale edge representation [J]. Opt. Eng, 1994, 13(7): 2151-2161
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