基于小波分析和数学形态学相融合的高光谱数据去噪
|
摘要
为了更好的消除高光谱数据噪声,加快高光谱数据去噪去噪速度,设计了基于小波分析和小波分析的高光谱数据去噪方法。首先收集原始的高光谱数据,并采用小波分析对其进行变换,通过设置阈值去除高光谱数据中的噪声,然后采用数学形态学对高光谱数据进行处理,实现高光谱数据的进一步去噪。从主观和客观两个方面对高光谱数据去噪方法的性能进行评价,本文方法的高光谱数据去噪效果好,解决了当前方法存在的不足,加快了高光谱数据去噪速度,研究结果可以为高光谱数据去噪的研究提供有价值参考。
in order to eliminate the noise of hyperspectral data and accelerate the denoising speed of hyperspectral data,a hyperspectral data denoising algorithm based on wavelet analysis and wavelet analysis is designed.The first collection of original hyperspectral data,using wavelet analysis to transform it,by setting the threshold is divided into sub blocks,using wavelet analysis to decompose each block,and then use mathematical morphology for hyperspectral data processing,to achieve high spectral data denoising. To evaluate the performance from the two aspects of subjective and objective denoising algorithm for hyperspectral data,hyperspectral data the algorithm denoising effect,solves the shortcomings of the current algorithms,accelerate the denoising speed of hyperspectral data,the research results can denoising of hyperspectral data provide valuable reference.
in order to eliminate the noise of hyperspectral data and accelerate the denoising speed of hyperspectral data,a hyperspectral data denoising algorithm based on wavelet analysis and wavelet analysis is designed.The first collection of original hyperspectral data,using wavelet analysis to transform it,by setting the threshold is divided into sub blocks,using wavelet analysis to decompose each block,and then use mathematical morphology for hyperspectral data processing,to achieve high spectral data denoising. To evaluate the performance from the two aspects of subjective and objective denoising algorithm for hyperspectral data,hyperspectral data the algorithm denoising effect,solves the shortcomings of the current algorithms,accelerate the denoising speed of hyperspectral data,the research results can denoising of hyperspectral data provide valuable reference.
引文
[1]文莉,刘正士,葛运建.小波去噪的几种方法[J].合肥工业大学学报,2002,25(2):167-172.
[2]李士心,刘鲁源.基于小波阈值去噪方法的研究[J].仪器仪表学报,2002,23(3):478-479.
[3]张维强,宋国乡.基于一种新的阈值函数的小波域信号去噪[J].西安电子科技大学学报,2004,31(2):296-299.
[4]ABDEL-Ouahab Boudraa,JEAN-Christophe Cexus.EMDbased signal filtering[J].IEEE Transactions on Instrumentation and Measurement,2007,56(6):2196-2202.
[5]CHEN S H,WANG J F.Speech enhancement using perceptual wavelet packet decomposition and teager energy operator[J].J of VLSI Signal Processing,2004,36(2/3):125-139.
[6]欧阳森,宋政湘,王建华,等.基于信号相关性和小波方法的电能质量去噪方法[J].电工技术学报,2003,18(3):112-116.
[7]欧阳森,宋政湘,陈德桂,等.小波软阈值去噪技术在电能质量检测中的应用[J].电力系统自动化,2002,26(19):56-6.
[8]程扬军,黄纯,何朝辉,陈续喜.基于自适应顺序形态滤波的电能质量去噪方法[J].计算机仿真,2009,26(12):218-221.
[9]BARCELOS C A Z,BOAVENTURA M,AND E.C.SILVA,JR,A well-balanced flow equation for noise removal and edge detection[J].IEEE Trans Image Processing,2003,12(7):751-763).
[10]Phillips S C,Gledhill R J,Essex J W.Application of the Hilbert-Huang transform to the analysis of molecular dynamics simulations[J].The Journal of Physical Chemistry,2003,107(24):4869-4876.
[11]陈斌,杨平,施克仁.Hilbert-Huang变换在非线性超声无损检测中应用[J].清华大学(自然科学版),2006(46):1369-1372.
[12]ABHIJEET Dipak Shinde.A Wavelet Packet Based Sifting Process and Its Application for Structural Health Monitoring[J].Structural Health Monitoring,2005,4:153-170.
[13]TANG Baoping,DONG Shaojiang,SONG Tao.Method for eliminating mode mixing of empirical mode decomposition based on the revised blind source separation[J].Signal Processing,2011,92(1):248-258.
[14]LI Lin,JI Hongbing.Signal feature extraction based on an improved EMD method[J].Measurement,2009,42(5):796-803.
[15]王惠刚,梁红,李志舜.高斯噪声中的参数盲估计[J].声学学报,2003,28(5):443-446.
[16]王光新,王正明,段晓君.基于广义高斯噪声分布模型的迭代正则化图像复原[J].中国图象图形学报,2004,9(8):978-983.
[17]赵晓明,叶喜剑,姚敏.一种改进的自适应局部噪声消除滤波方法[J].系统工程理论与实践,2006,12:99-104.
[18]WU Zhaohua,HUANG Norden E.A study of the characteristics of white noise using the empirical mode decomposition method[J].Proc.R.Soc.Lond,2004,460(A):1597-1611.
[2]李士心,刘鲁源.基于小波阈值去噪方法的研究[J].仪器仪表学报,2002,23(3):478-479.
[3]张维强,宋国乡.基于一种新的阈值函数的小波域信号去噪[J].西安电子科技大学学报,2004,31(2):296-299.
[4]ABDEL-Ouahab Boudraa,JEAN-Christophe Cexus.EMDbased signal filtering[J].IEEE Transactions on Instrumentation and Measurement,2007,56(6):2196-2202.
[5]CHEN S H,WANG J F.Speech enhancement using perceptual wavelet packet decomposition and teager energy operator[J].J of VLSI Signal Processing,2004,36(2/3):125-139.
[6]欧阳森,宋政湘,王建华,等.基于信号相关性和小波方法的电能质量去噪方法[J].电工技术学报,2003,18(3):112-116.
[7]欧阳森,宋政湘,陈德桂,等.小波软阈值去噪技术在电能质量检测中的应用[J].电力系统自动化,2002,26(19):56-6.
[8]程扬军,黄纯,何朝辉,陈续喜.基于自适应顺序形态滤波的电能质量去噪方法[J].计算机仿真,2009,26(12):218-221.
[9]BARCELOS C A Z,BOAVENTURA M,AND E.C.SILVA,JR,A well-balanced flow equation for noise removal and edge detection[J].IEEE Trans Image Processing,2003,12(7):751-763).
[10]Phillips S C,Gledhill R J,Essex J W.Application of the Hilbert-Huang transform to the analysis of molecular dynamics simulations[J].The Journal of Physical Chemistry,2003,107(24):4869-4876.
[11]陈斌,杨平,施克仁.Hilbert-Huang变换在非线性超声无损检测中应用[J].清华大学(自然科学版),2006(46):1369-1372.
[12]ABHIJEET Dipak Shinde.A Wavelet Packet Based Sifting Process and Its Application for Structural Health Monitoring[J].Structural Health Monitoring,2005,4:153-170.
[13]TANG Baoping,DONG Shaojiang,SONG Tao.Method for eliminating mode mixing of empirical mode decomposition based on the revised blind source separation[J].Signal Processing,2011,92(1):248-258.
[14]LI Lin,JI Hongbing.Signal feature extraction based on an improved EMD method[J].Measurement,2009,42(5):796-803.
[15]王惠刚,梁红,李志舜.高斯噪声中的参数盲估计[J].声学学报,2003,28(5):443-446.
[16]王光新,王正明,段晓君.基于广义高斯噪声分布模型的迭代正则化图像复原[J].中国图象图形学报,2004,9(8):978-983.
[17]赵晓明,叶喜剑,姚敏.一种改进的自适应局部噪声消除滤波方法[J].系统工程理论与实践,2006,12:99-104.
[18]WU Zhaohua,HUANG Norden E.A study of the characteristics of white noise using the empirical mode decomposition method[J].Proc.R.Soc.Lond,2004,460(A):1597-1611.