高光谱图像条带噪声去除方法研究与应用
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摘要
遥感图像在生成和传输过程中常常受到各种噪声源的干扰和影响而使图像质量变差。条带噪声在许多星载、机载多传感器和单传感器光谱仪成像中是一种很普遍的现象。条带噪声的出现严重影响了遥感数据的解译和信息提取,使其不能发挥应有的作用。不少学者已经提出了很多条带噪声去除算法,但这些算法多是针对某种条带噪声图像展开的,因而分别在条带去除效果、算法实现的简便性、算法应用的通用性和自适应性等方面具有一定的不足。针对这些方法的不足,本文主要针对高光谱图像条带噪声去除算法进行研究。具体研究了一些条带噪声去除方法,着重提出和讨论了利用平均值滤波法、多项式拟合滤波法、移动窗口滤波法结合矩匹配方法来近似恢复由入射辐射强度产生的均值分布,从而达到保持图像质量并有效去除图像条带噪声的目的。并对条带噪声去除前后图像质量做了定性定量的比较、评价。
本文的主要贡献和创新之处如下:
1)提出了基于平均值滤波的改进的矩匹配法,利用平均值滤波法结合矩匹配方法法来近似恢复由人射辐射强度产生的均值分布,从而达到保持图像质量并有效去除条带噪声的目的。此方法对图像地物类型的单一性和灰度分布的均匀性均无要求。
2)提出了基于多项式拟合滤波的改进的矩匹配法,应用最小二乘法对原始图像的列均值和方差进行拟合,获得平滑滤波后的列均值和方差,用来代替传统矩匹配算法中“参考图像”的平均值和方差。此方法可使变化较缓和变化剧烈的数据,都获得良好的平滑效果。
3)提出了基于移动窗口滤波的改进的矩匹配法,结合空域滤波思想,进行图像的滑动滤波,用滑动窗口所包含列均值和列方差的平均值来代替中心点的列均值和列方差。应用此方法一般不会发生图像灰度分布不均匀时应用矩匹配方法产生的失真,进行条带消除后各列灰度分布更符合自然地物的辐射分布,去条带后图像也最清晰。
4)利用图像质量评价标准,对各种方法噪声去除效果进行比较和评价。通过实验、研究找出本文提出的改进的矩匹配法中去条带效果、原始图像信息保留等各方面综合评价最好的一种方法,应用于HJ-1-A卫星高光谱图像条带噪声去除中,并将其作为今后HJ-1-A卫星高光谱图像预处理推荐采用的方法。
5)为了给大量的高光谱图像条带噪声去除带来方便,本文设计了高光谱图像条带噪声去除批处理算法,并用IDL进行了实现。
In the process of generation and transmission, remote sensing images are affected by noise and the quality of images is worse. Band noise is a very common phenomenon in images of many space-borne multi-sensor, airborne multi-sensor and single sensor spectrometer. Band noise seriously affects the interpretation of remote sensing data and the extraction of information. It can not play its due role. Many scholars have already made a lot of band noise reduction algorithms, but many of these algorithms are targeted to a certain particular band noise images. So the method has some deficiencies in the effectiveness of eliminating strips, simplicity, universal and self-adaptability of algorithm. This paper research on the algorithm of eliminating strips of hyper-spectral images. Some specific methods were researched. Improved algorithms of Moment Matching based on smoothing filter were put forward. They can recover approximately mean distribution caused by radiation strength and effectively eliminate the strips. This paper compared to image quality before and after destriping.
The main contribution and the innovations of this paper are as follows:
1) An improved algorithm of Moment Matching based on mean value filter was put forward. It can recover approximately mean distribution caused by radiation strength through combining Moment Matching and mean value filter.This algorithm was no requirement for feature type and gray distribution of images.
2) An improved algorithm of Moment Matching based on polynomial fitting filter was put forward. The proposed algorithm uses the column average and variance which was processed with polynomial fitting filter instead of the average and standard deviation of the reference image in traditional moment matching algorithm.Data of dramatic and moderate changes all can get good smoothing effect.
3) An improved algorithm of Moment Matching based on sliding window filter was put forward. The proposed algorithm uses the mean value of column average and variance in sliding window instead of the average and standard deviation of the center in the window. This method generally does not cause distortion and recovers radiation distribution well.Images destriped are very clear.
4) The standard of image quality evaluation was established.Through the evaluation of the capacity of retaining characteristic information of the original image and de-noising effect, finding the best algorithm of the improved algorithms.The best method is recommended as the hyper-spectral images preprocessing methods. Considering the specials of HJ-1-A satellite HSI data, the best improved algorithms of Moment Matching was applied to eliminate the strips.
5) In order to conveniently eliminate strips of a large number of hyper-spectral images, the paper designed the hyper-spectral image band noise reduction batch algorithm, and it was realized with the IDL.
本文的主要贡献和创新之处如下:
1)提出了基于平均值滤波的改进的矩匹配法,利用平均值滤波法结合矩匹配方法法来近似恢复由人射辐射强度产生的均值分布,从而达到保持图像质量并有效去除条带噪声的目的。此方法对图像地物类型的单一性和灰度分布的均匀性均无要求。
2)提出了基于多项式拟合滤波的改进的矩匹配法,应用最小二乘法对原始图像的列均值和方差进行拟合,获得平滑滤波后的列均值和方差,用来代替传统矩匹配算法中“参考图像”的平均值和方差。此方法可使变化较缓和变化剧烈的数据,都获得良好的平滑效果。
3)提出了基于移动窗口滤波的改进的矩匹配法,结合空域滤波思想,进行图像的滑动滤波,用滑动窗口所包含列均值和列方差的平均值来代替中心点的列均值和列方差。应用此方法一般不会发生图像灰度分布不均匀时应用矩匹配方法产生的失真,进行条带消除后各列灰度分布更符合自然地物的辐射分布,去条带后图像也最清晰。
4)利用图像质量评价标准,对各种方法噪声去除效果进行比较和评价。通过实验、研究找出本文提出的改进的矩匹配法中去条带效果、原始图像信息保留等各方面综合评价最好的一种方法,应用于HJ-1-A卫星高光谱图像条带噪声去除中,并将其作为今后HJ-1-A卫星高光谱图像预处理推荐采用的方法。
5)为了给大量的高光谱图像条带噪声去除带来方便,本文设计了高光谱图像条带噪声去除批处理算法,并用IDL进行了实现。
In the process of generation and transmission, remote sensing images are affected by noise and the quality of images is worse. Band noise is a very common phenomenon in images of many space-borne multi-sensor, airborne multi-sensor and single sensor spectrometer. Band noise seriously affects the interpretation of remote sensing data and the extraction of information. It can not play its due role. Many scholars have already made a lot of band noise reduction algorithms, but many of these algorithms are targeted to a certain particular band noise images. So the method has some deficiencies in the effectiveness of eliminating strips, simplicity, universal and self-adaptability of algorithm. This paper research on the algorithm of eliminating strips of hyper-spectral images. Some specific methods were researched. Improved algorithms of Moment Matching based on smoothing filter were put forward. They can recover approximately mean distribution caused by radiation strength and effectively eliminate the strips. This paper compared to image quality before and after destriping.
The main contribution and the innovations of this paper are as follows:
1) An improved algorithm of Moment Matching based on mean value filter was put forward. It can recover approximately mean distribution caused by radiation strength through combining Moment Matching and mean value filter.This algorithm was no requirement for feature type and gray distribution of images.
2) An improved algorithm of Moment Matching based on polynomial fitting filter was put forward. The proposed algorithm uses the column average and variance which was processed with polynomial fitting filter instead of the average and standard deviation of the reference image in traditional moment matching algorithm.Data of dramatic and moderate changes all can get good smoothing effect.
3) An improved algorithm of Moment Matching based on sliding window filter was put forward. The proposed algorithm uses the mean value of column average and variance in sliding window instead of the average and standard deviation of the center in the window. This method generally does not cause distortion and recovers radiation distribution well.Images destriped are very clear.
4) The standard of image quality evaluation was established.Through the evaluation of the capacity of retaining characteristic information of the original image and de-noising effect, finding the best algorithm of the improved algorithms.The best method is recommended as the hyper-spectral images preprocessing methods. Considering the specials of HJ-1-A satellite HSI data, the best improved algorithms of Moment Matching was applied to eliminate the strips.
5) In order to conveniently eliminate strips of a large number of hyper-spectral images, the paper designed the hyper-spectral image band noise reduction batch algorithm, and it was realized with the IDL.
引文
[1]袁迎辉,林子瑜.高光谱遥感技术综述[J].中国水运.2007,7(8).
[2]郭水霞.图像处理中非加性噪声情形下维纳滤波的推广[J].计算机工程与应用.2007,43(12).
[3]杨金红,顾松山,程明虎.插值法在去除MODIS遥感影像条带噪声中的应用[J].气象科学.2007,27(6).
[4]曹治华,宋斌恒.多种形状窗口下的快速中值滤波算法[J].计算机应用研究.2006,23 (3).
[5]吴军,张万昌. MODIS影像条带噪声去除的自相关插值法[J].遥感技术与应用.2006,21(3).
[6]胡旺,李志蜀,黄奇.基于双窗口和极值压缩的自适应中值滤波[J].中国图象图形学报.2007,12 (1).
[7] Srinivasan R,Cannon M,White J.Landsat Data Destriping Using Power Filtering[J].Opt.Eng.,1988,27(11):939-943.
[8] Horn B K P,Woodham R J.Destriping Landsat MSS Images by Histogram Modification[J].Comput. Graph & Image Process,1979,10:69-83.
[9] Kautsky J,Nichols N K,Jupp D L B.Smoothed Histogram Modification for Image Processing[J].Comput. Vis. & Image Process,1984,26:271-291.
[10] Gadallah F L,Csillag F,Smitb E J M.Destriping Multisensor Imagery with Moment Matching[J].Int. J. Remote Sensing,2000,21(12):2505-2511.
[11]马德敏.高光谱图像质量评价[J].《红外》月刊,2004,7.
[12]苏俊英,舒宁.一种基于非线性增益小波滤波的高光谱影像去噪技术研究[J].遥感技术与应用.2008,23 (4).
[13]路威,余旭初,刘娟.高光谱遥感数据三次光滑样条滤噪[J].测绘学院学报.2005,22 (1).
[14]王芳,卓莉,李继红.基于粗集均值滤波的Hyperion高光谱数据滤噪方法[J].地理与地理信息科学.2006,22 (6).
[15]张媛.高光谱遥感图像的处理与应用.西北工业大学硕士学位论文.2006,3.
[16]夏丽丽.基于分形理论的高光谱图像压缩算法研究.厦门大学硕士学位论文.2007,6.
[17]中国资源卫星应用中心.资源一号02B卫星(CBERS-02B)工程在轨测试报告[M]. 2007,12.
[18]白香花,朱启疆,唐世浩,帅艳民.基于纹理分析的去图象噪声研究[J].计算机工程与科学.2004,26 (3).
[19]陈富龙,方圣辉.采用方向滤波消除中巴地球资源卫星图像条带噪声[J].测绘信息与工程.2003,28 (2).
[20]牛生丽,唐军武,蒋兴伟,郭茂华,宋庆君. HY-1A卫星COCTS数据条带消除的两种定量化方法比较[J].遥感学报.2007,11 (6).
[21]修吉宏,翟林培,刘红. CCD图像条带噪声消除方法[J].电子器件.2005,28(4).
[22]中国资源卫星应用中心.中巴地球资源卫星应用研究文集[M]. 2003,7.
[23]环境减灾A、B星在轨测试指挥组.环境与灾害监测预报小卫星星座A、B星在轨测试技术报告[M].2009,2.
[24]黄勤超,罗家融,王华忠,李翀.基于IDL的等离子体放电位形数据可视化[J].计算机工程.2006,32(6).
[25]韩培友. IDL可视化分析与应用[M].西安:西北工业大学出版社.2006,9.
[26]关新平,赵立兴,唐英干.图像去噪混合滤波方法[J].中国图象图形学报.2005,10 (3).
[27]吴传庆,赵永超,童庆禧,郑兰芬.基于光谱信息的遥感图像空间域自适应滤波[J].遥感学报.2004,8 (1).
[28]马德敏.高光谱图像质量评价[J].《红外》月刊,2004,7.
[29]周奇,张永光,徐健健.基于直方图特性的图像去噪方法[J].电子测量技术.2007,30 (1).
[30]陈劲松,邵芸,朱博勤.一种改进的矩匹配方法在CMODIS数据条带去除中的应用[J].遥感技术与应用.2003,18(5).
[31]刘正军,王长耀,王成.成像光谱仪图像条带噪声去除的改进矩匹配方法[J].遥感学报.2002,6(4).
[32]姜三平,郝晓剑.应用小波系数GSM统计模型的混合傅里叶—小波图像降噪[J].中国图象图形学报.2009,14(3).
[33]龚昌来.基于小波变换和均值滤波的图像去噪方法[J].光电工程.2007,34 (1).
[34]吴传庆,童庆禧,郑兰芬.基于小波变换的高光谱图像消噪[J].遥感信息.2005,4.
[35]鲍早朝.基于不可分小波和二维主成分分析的图像去噪算法.湖北大学硕士学位论文.2008,4.
[36]周景超,戴汝为,肖柏华.图像质量评价研究综述[J].计算机科学.2008,35 (7).
[37]史玉林,李飞飞,孙益顶.基于均值滤波和小波分析的图像去噪[J].电子测量技术.2008,31 (8).
[38]杨敏华,赵永超,刘团结,张兵,赵春江,童庆禧.基于DN值趋势面的成像光谱数据低信噪比波段修复研究[J].中国图象图形学报.2003,8(11).
[39]唐吉,黄清.多项式拟合方法在目标跟踪中的应用[J].仪器仪表用户.2008,1.
[40]李健翮,李学仁.多项式拟合在试飞数据预处理中的应用[J].弹箭与制导学报.2005,25 (4).
[41]郭晓新,卢奕南,许志闻,王云霄,庞云阶.自适应定向加权中值滤波[J].吉林大学学报.2005,43(4).
[42]杨元喜,徐天河.基于移动开窗法协方差估计和方差分量估计的自适应滤波[J].武汉大学学报.2003,28 (6).
[43]马德敏.高光谱图像质量评价[J].《红外》月刊.2004,7.
[44]刘海霞.去噪图像质量的评价[J].内蒙古水利.2009,1.
[45] Giovani Corsini,Marco Diani,Thomas Walzel.Striping Removal in MOS-B Data[J].IEEE Trans. on Geoscience & Remote Sensing.2000,38(3):1439-1446.
[46]王登位,李炜.基于小波变换的图像去噪研究[J].计算机与数字工程.2007,35 (9).
[2]郭水霞.图像处理中非加性噪声情形下维纳滤波的推广[J].计算机工程与应用.2007,43(12).
[3]杨金红,顾松山,程明虎.插值法在去除MODIS遥感影像条带噪声中的应用[J].气象科学.2007,27(6).
[4]曹治华,宋斌恒.多种形状窗口下的快速中值滤波算法[J].计算机应用研究.2006,23 (3).
[5]吴军,张万昌. MODIS影像条带噪声去除的自相关插值法[J].遥感技术与应用.2006,21(3).
[6]胡旺,李志蜀,黄奇.基于双窗口和极值压缩的自适应中值滤波[J].中国图象图形学报.2007,12 (1).
[7] Srinivasan R,Cannon M,White J.Landsat Data Destriping Using Power Filtering[J].Opt.Eng.,1988,27(11):939-943.
[8] Horn B K P,Woodham R J.Destriping Landsat MSS Images by Histogram Modification[J].Comput. Graph & Image Process,1979,10:69-83.
[9] Kautsky J,Nichols N K,Jupp D L B.Smoothed Histogram Modification for Image Processing[J].Comput. Vis. & Image Process,1984,26:271-291.
[10] Gadallah F L,Csillag F,Smitb E J M.Destriping Multisensor Imagery with Moment Matching[J].Int. J. Remote Sensing,2000,21(12):2505-2511.
[11]马德敏.高光谱图像质量评价[J].《红外》月刊,2004,7.
[12]苏俊英,舒宁.一种基于非线性增益小波滤波的高光谱影像去噪技术研究[J].遥感技术与应用.2008,23 (4).
[13]路威,余旭初,刘娟.高光谱遥感数据三次光滑样条滤噪[J].测绘学院学报.2005,22 (1).
[14]王芳,卓莉,李继红.基于粗集均值滤波的Hyperion高光谱数据滤噪方法[J].地理与地理信息科学.2006,22 (6).
[15]张媛.高光谱遥感图像的处理与应用.西北工业大学硕士学位论文.2006,3.
[16]夏丽丽.基于分形理论的高光谱图像压缩算法研究.厦门大学硕士学位论文.2007,6.
[17]中国资源卫星应用中心.资源一号02B卫星(CBERS-02B)工程在轨测试报告[M]. 2007,12.
[18]白香花,朱启疆,唐世浩,帅艳民.基于纹理分析的去图象噪声研究[J].计算机工程与科学.2004,26 (3).
[19]陈富龙,方圣辉.采用方向滤波消除中巴地球资源卫星图像条带噪声[J].测绘信息与工程.2003,28 (2).
[20]牛生丽,唐军武,蒋兴伟,郭茂华,宋庆君. HY-1A卫星COCTS数据条带消除的两种定量化方法比较[J].遥感学报.2007,11 (6).
[21]修吉宏,翟林培,刘红. CCD图像条带噪声消除方法[J].电子器件.2005,28(4).
[22]中国资源卫星应用中心.中巴地球资源卫星应用研究文集[M]. 2003,7.
[23]环境减灾A、B星在轨测试指挥组.环境与灾害监测预报小卫星星座A、B星在轨测试技术报告[M].2009,2.
[24]黄勤超,罗家融,王华忠,李翀.基于IDL的等离子体放电位形数据可视化[J].计算机工程.2006,32(6).
[25]韩培友. IDL可视化分析与应用[M].西安:西北工业大学出版社.2006,9.
[26]关新平,赵立兴,唐英干.图像去噪混合滤波方法[J].中国图象图形学报.2005,10 (3).
[27]吴传庆,赵永超,童庆禧,郑兰芬.基于光谱信息的遥感图像空间域自适应滤波[J].遥感学报.2004,8 (1).
[28]马德敏.高光谱图像质量评价[J].《红外》月刊,2004,7.
[29]周奇,张永光,徐健健.基于直方图特性的图像去噪方法[J].电子测量技术.2007,30 (1).
[30]陈劲松,邵芸,朱博勤.一种改进的矩匹配方法在CMODIS数据条带去除中的应用[J].遥感技术与应用.2003,18(5).
[31]刘正军,王长耀,王成.成像光谱仪图像条带噪声去除的改进矩匹配方法[J].遥感学报.2002,6(4).
[32]姜三平,郝晓剑.应用小波系数GSM统计模型的混合傅里叶—小波图像降噪[J].中国图象图形学报.2009,14(3).
[33]龚昌来.基于小波变换和均值滤波的图像去噪方法[J].光电工程.2007,34 (1).
[34]吴传庆,童庆禧,郑兰芬.基于小波变换的高光谱图像消噪[J].遥感信息.2005,4.
[35]鲍早朝.基于不可分小波和二维主成分分析的图像去噪算法.湖北大学硕士学位论文.2008,4.
[36]周景超,戴汝为,肖柏华.图像质量评价研究综述[J].计算机科学.2008,35 (7).
[37]史玉林,李飞飞,孙益顶.基于均值滤波和小波分析的图像去噪[J].电子测量技术.2008,31 (8).
[38]杨敏华,赵永超,刘团结,张兵,赵春江,童庆禧.基于DN值趋势面的成像光谱数据低信噪比波段修复研究[J].中国图象图形学报.2003,8(11).
[39]唐吉,黄清.多项式拟合方法在目标跟踪中的应用[J].仪器仪表用户.2008,1.
[40]李健翮,李学仁.多项式拟合在试飞数据预处理中的应用[J].弹箭与制导学报.2005,25 (4).
[41]郭晓新,卢奕南,许志闻,王云霄,庞云阶.自适应定向加权中值滤波[J].吉林大学学报.2005,43(4).
[42]杨元喜,徐天河.基于移动开窗法协方差估计和方差分量估计的自适应滤波[J].武汉大学学报.2003,28 (6).
[43]马德敏.高光谱图像质量评价[J].《红外》月刊.2004,7.
[44]刘海霞.去噪图像质量的评价[J].内蒙古水利.2009,1.
[45] Giovani Corsini,Marco Diani,Thomas Walzel.Striping Removal in MOS-B Data[J].IEEE Trans. on Geoscience & Remote Sensing.2000,38(3):1439-1446.
[46]王登位,李炜.基于小波变换的图像去噪研究[J].计算机与数字工程.2007,35 (9).