多源图像融合及其流程建模研究
|
摘要
作为多源数据融合领域的一个重要分支,多源图像融合研究的是如何将不同类型传感器获取的图像数据,采用一定算法将其优势或互补性信息有机结合起来产生新图像的技术。其优点是不仅能够克服单幅图像在几何、光谱和空间分辨率等方面存在的局限性和差异性,进而提高图像的质量;而且有利于对感兴趣的目标进行定位、识别和变化检测等。在信息化高度发展的今天,多源图像融合技术已经成为数据融合领域中不可或缺的技术,并在许多军事和民用方面有着重要的应用。
建立多源图像融合机制包含三大基本要素:合理的图像融合流程、有效的融合规则的选择以及综合的融合性能评价。本文的研究工作就是针对这三大基本要素展开的,主要工作如下:
1深入讨论了多源图像融合的不同层次和模式,对基于像素级的空间域和变换域上的多源图像融合方法进行了分类研究,并将这些方法进行融合实验,在此基础上,分析了融合方法的适用性及其边界条件;
2对现有融合结果评价方法加以总结,在此基础上归纳出基于信息量、基于统计特性、基于信噪比、基于梯度值等四类十二项融合结果评价指标,从而为融合性能评估提供了定量的评价准则;研究了以主观视觉分析为主,以客观定量分析为辅的多因素图像融合质量评价方法,提出了闭环评价系统的框架,为后续的研究提供了理论支撑;
3通过对各种不同的图像融合方法和融合类型的分析,研究了多源图像融合流程建模方法,建立了一个以图像数据源为入口,以融合模型和融合规则为核心,以融合后图像为输出,以多因素综合判据下的图像融合评价体制为终点的闭环式图像融合流程,最后通过融合实验验证了该模型的有效性;
4在运用小波变换法进行图像融合的基础上,针对局部方差融合规则和高通滤波各自的优缺点,研究了一种新的融合规则的选择方法,提出了基于局部方差和高通滤波的小波变换图像融合算法,并对全色图像和多光谱图像进行了融合实验,融合结果的性能评价表明该算法是有效的,融合后的结果图像既保留了多光谱图像的光谱特性,且同时具有较高的空间细节表现能力。
As the important research branch in multi-source data fusion region, research on multi-source image fusion is how to obtain a new image with self-complementary and advantage information by combine different image data from different sensors together based on some fusion algorithm. The purpose is not only to overcome the limitation and the discrepancy of single image in geometry, spectrum and spatial resolution and then to increase image quality, but also to be good for the next application including target identification, feature extraction and change detection and so on. In this day with high-speed informationization development, multi-source image fusion technology has become an absolutely necessary technology in data fusion region, and also has paid important function in some military and civil regions.
There are three basic elements for establishing a complete multi-source image fusion system, which are logical image fusion flow, effective fusion rule selection and integrated fusion performance evaluation criterion. The research work of this paper is just carried out according to these three basic elements, which are depicted as follows:
1 Different levels and modes for multi-source image fusion are deeply discussed; image fusion methods based on spatial domain and transform domain of pixel level are classified and researched, and then their simulation are carried out. Based on the above analysis, the applicability and its boundary condition for each fusion method are analyzed;
2 Existing evaluation criterions for image fusion are summarized, and the integrated evaluation system with four classes and twelve kinds standards including information quantity, statistic characteristics, signal-noise ratio and gradient value are presented, which can provide a quantitative evaluation criterion for image fusion; and then a new evaluation method for image fusion based on multi-factors criterions taking visual analysis as main measure and quantitative analysis as auxiliary measure is researched, and a close-loop evaluation system framework is presented, which can provide a theoretical supporting for next research;
3 Based on the analysis for different image fusion method and fusion types, a modeling method on multi-source image fusion process is analyzed and researched, and moreover a closed image fusion process taking image data as input, fusion model and fusion rule as core, fused images as output and fusion appraisal system based on multifactor integrative criterion as terminal is presented, and finally, the validity of this fusion process is validated by relevant fusion experiments;
4 In view of each fact of local deviation and high-pass filtering in image fusion and based on using wavelet transform to do image fusion, a new selection method for fusion rule is studied. Image fusion algorithm based on local deviation and high-pass filtering of wavelet transform is advanced. Taking the fusion between PAN image and multi-spectral image as example, the simulation results show that compared with the other single method, the new method presented is clearly better in preserving spectral and improving spatial presentation.
建立多源图像融合机制包含三大基本要素:合理的图像融合流程、有效的融合规则的选择以及综合的融合性能评价。本文的研究工作就是针对这三大基本要素展开的,主要工作如下:
1深入讨论了多源图像融合的不同层次和模式,对基于像素级的空间域和变换域上的多源图像融合方法进行了分类研究,并将这些方法进行融合实验,在此基础上,分析了融合方法的适用性及其边界条件;
2对现有融合结果评价方法加以总结,在此基础上归纳出基于信息量、基于统计特性、基于信噪比、基于梯度值等四类十二项融合结果评价指标,从而为融合性能评估提供了定量的评价准则;研究了以主观视觉分析为主,以客观定量分析为辅的多因素图像融合质量评价方法,提出了闭环评价系统的框架,为后续的研究提供了理论支撑;
3通过对各种不同的图像融合方法和融合类型的分析,研究了多源图像融合流程建模方法,建立了一个以图像数据源为入口,以融合模型和融合规则为核心,以融合后图像为输出,以多因素综合判据下的图像融合评价体制为终点的闭环式图像融合流程,最后通过融合实验验证了该模型的有效性;
4在运用小波变换法进行图像融合的基础上,针对局部方差融合规则和高通滤波各自的优缺点,研究了一种新的融合规则的选择方法,提出了基于局部方差和高通滤波的小波变换图像融合算法,并对全色图像和多光谱图像进行了融合实验,融合结果的性能评价表明该算法是有效的,融合后的结果图像既保留了多光谱图像的光谱特性,且同时具有较高的空间细节表现能力。
As the important research branch in multi-source data fusion region, research on multi-source image fusion is how to obtain a new image with self-complementary and advantage information by combine different image data from different sensors together based on some fusion algorithm. The purpose is not only to overcome the limitation and the discrepancy of single image in geometry, spectrum and spatial resolution and then to increase image quality, but also to be good for the next application including target identification, feature extraction and change detection and so on. In this day with high-speed informationization development, multi-source image fusion technology has become an absolutely necessary technology in data fusion region, and also has paid important function in some military and civil regions.
There are three basic elements for establishing a complete multi-source image fusion system, which are logical image fusion flow, effective fusion rule selection and integrated fusion performance evaluation criterion. The research work of this paper is just carried out according to these three basic elements, which are depicted as follows:
1 Different levels and modes for multi-source image fusion are deeply discussed; image fusion methods based on spatial domain and transform domain of pixel level are classified and researched, and then their simulation are carried out. Based on the above analysis, the applicability and its boundary condition for each fusion method are analyzed;
2 Existing evaluation criterions for image fusion are summarized, and the integrated evaluation system with four classes and twelve kinds standards including information quantity, statistic characteristics, signal-noise ratio and gradient value are presented, which can provide a quantitative evaluation criterion for image fusion; and then a new evaluation method for image fusion based on multi-factors criterions taking visual analysis as main measure and quantitative analysis as auxiliary measure is researched, and a close-loop evaluation system framework is presented, which can provide a theoretical supporting for next research;
3 Based on the analysis for different image fusion method and fusion types, a modeling method on multi-source image fusion process is analyzed and researched, and moreover a closed image fusion process taking image data as input, fusion model and fusion rule as core, fused images as output and fusion appraisal system based on multifactor integrative criterion as terminal is presented, and finally, the validity of this fusion process is validated by relevant fusion experiments;
4 In view of each fact of local deviation and high-pass filtering in image fusion and based on using wavelet transform to do image fusion, a new selection method for fusion rule is studied. Image fusion algorithm based on local deviation and high-pass filtering of wavelet transform is advanced. Taking the fusion between PAN image and multi-spectral image as example, the simulation results show that compared with the other single method, the new method presented is clearly better in preserving spectral and improving spatial presentation.
引文
[1]韩崇昭,朱洪艳,段战胜等.多源信息融合.清华大学出版社,2006
[2]毛士艺,赵巍.多传感器图像融合技术综述.北京航空航天大学学报,Vol.28,No.5,2002
[3]郁文贤,郭桂蓉.多传感器信息融合技术述评[J].国防科技大学学报,1994,16(4)
[4]覃征,鲍复民,李爱国等.多传感器图像融合及其应用综述.微电子学与计算机,Vol.21,No.2.2004
[5]Lawrence A,Klein.Sensor and Data Fusion Concepts and Applications[M].SPIE Optical Engineering Press,Washington,1999
[6]Klein,Processing Requirements for Multi-sensor Low-Cost Brilliant Munitions,IEEE transactions on aerospace and electronic systems,Vol.29,No.4,Oct.1993
[7]Pohl C,Van Genderen J L.Multi-sensor image fusion in remote sensing:Concepts,Methods and Application[J],INT J Remote Sensing,1998,19(5):823-854
[8]郑林.基于多源信息融合的图像处理.识别与跟踪研究[D].西安交通大学,2003
[9]刘喜贵.多传感器图像融合方法研究[D].西安电子科技大学,2001年
[10]White F E.Data fusion lexicon.Joint directors of laboratories,Technical Panel for C~3,Data fusion sub-panel,naval ocean systems center,San Diego,CA,USA,1987
[11]Zhenyue Zhang and Hongyuan Zha,Local Linear Smoothing for Nonlinear Manifold Learning,CSE-03-003,Technical Report,CSE,Penn State Univ,2003
[12]王海晖,彭嘉雄,吴巍.一种多传感器遥感图像的配准方法[J].华中科技大学学报,2002,30(8)
[13]L.Saul,S.Roweis,Think Globally,Fit Locally:Unsupervised Learning of Nonlinear Manifolds,Technical Report MS CIS-02-18,University of Pennsylvania,2002
[14]M.Belkin,P.Niyogi,Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering,Neural Information Processing Systems 14(NIPS'2001),2002
[15]Toet A,Ruyven L J,Valeton J M,Mering thermal and visual images by a contrast pyramid[J],Optical Engineering,1989,28(7):789-792
[16]Therrien C.W,Scrofani J.W,Kreb W.K,An adaptive technique for the enhanced fusion of low-light visible with uncooled thermal infrared imagery[J],International Conference on Image Processing,1997
[17]Costantini M,Farina A,Zirilli F,The fusion of different resolution SAR images,ESREN,Eur Space Agency,Frascati,Italy
[18]王海晖,彭嘉雄,吴巍.评价多传感器图像融合效果方法的比较.红外与激光工程,Vol.33,NO.2,Apr.2004
[19]L.Jimenez,D.A.Landgrebe,Projection pursuit for high dimensional feature reduction:Parallel and sequential approaches,presented at the Int.Geoscience and Remote Sensing Symp,Florence,Italy,July 10-14,1995
[20]Burt P J,Kolczynski R J.Enhanced image capture through fusion.In:IEEE 4th International Conf on Computer Vision.1993,4.173-182
[21]Zhijun Wang,Djemel Ziou,Costas Armenakis,Deren Li,Qingquan Li,A Comparative Analysis of Image Fusion Methods,IEEE Transactions on geoscience and remote sensing,Vol.43,No.6,June 2005,1391-1401
[22]Mallat S G.A wavelet tour of signal processing[M].San Diego:Academic Press,1998:302-310
[23]Maria Petrou,Smart Decision Support System Using Parsimonious Information Fusion,2005 7th International Conference on Information Fusion.
[24]王洪华,王双亭,杜春萍.基于多进制小波的多源遥感影像融合[J].中国图像图形学报,2002(4):341-345
[25]贾永红.多源遥感影像数据融合方法及其应用研究[D].武汉大学,2001
[26]崔岩梅,倪国强,钟堰利等.利用统计特性进行图像融合效果分析及评价[J].北京理工大学学报,2000,20(1):102-106
[27]Hall D L.Mathematical Techniques in multi-sensor data fusion[M].Boston:Artech House,1992
[28]夏明革,何友,黄晓冬.多传感器图像融合效果评价方法研究.光电与控制,Vol.10,No.2.May 2003
[29]Miguel Angel Carreira-Perpinan,Continuous latent variable models for dimensionality reduction and sequential data reconstruction,Department of Computer Science University of Sheffield,UK.February 2001
[30]钟志勇,陈鹰.多源信息融合中小波变换的应用研究[J].测绘学报,2002
[31]王智均,李德仁,李清泉.利用小波变换对影像进行融合的研究[J].武汉测绘科技大学学报,2000,25(2):137-142
[32]陈勇,皮德富,周士源等.基于小波变换的红外图像融合技术研究[J].红外与激光工程,2001,30(1):15-17
[33]QiangZanxia,Peng Jiaxiong.Remote sensing image fusions based on local deviation of wavelet transform[J].HuaZhong Univ.of Sci.&Tech.(Nature Science Edition),2003,31(6):89-91
[2]毛士艺,赵巍.多传感器图像融合技术综述.北京航空航天大学学报,Vol.28,No.5,2002
[3]郁文贤,郭桂蓉.多传感器信息融合技术述评[J].国防科技大学学报,1994,16(4)
[4]覃征,鲍复民,李爱国等.多传感器图像融合及其应用综述.微电子学与计算机,Vol.21,No.2.2004
[5]Lawrence A,Klein.Sensor and Data Fusion Concepts and Applications[M].SPIE Optical Engineering Press,Washington,1999
[6]Klein,Processing Requirements for Multi-sensor Low-Cost Brilliant Munitions,IEEE transactions on aerospace and electronic systems,Vol.29,No.4,Oct.1993
[7]Pohl C,Van Genderen J L.Multi-sensor image fusion in remote sensing:Concepts,Methods and Application[J],INT J Remote Sensing,1998,19(5):823-854
[8]郑林.基于多源信息融合的图像处理.识别与跟踪研究[D].西安交通大学,2003
[9]刘喜贵.多传感器图像融合方法研究[D].西安电子科技大学,2001年
[10]White F E.Data fusion lexicon.Joint directors of laboratories,Technical Panel for C~3,Data fusion sub-panel,naval ocean systems center,San Diego,CA,USA,1987
[11]Zhenyue Zhang and Hongyuan Zha,Local Linear Smoothing for Nonlinear Manifold Learning,CSE-03-003,Technical Report,CSE,Penn State Univ,2003
[12]王海晖,彭嘉雄,吴巍.一种多传感器遥感图像的配准方法[J].华中科技大学学报,2002,30(8)
[13]L.Saul,S.Roweis,Think Globally,Fit Locally:Unsupervised Learning of Nonlinear Manifolds,Technical Report MS CIS-02-18,University of Pennsylvania,2002
[14]M.Belkin,P.Niyogi,Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering,Neural Information Processing Systems 14(NIPS'2001),2002
[15]Toet A,Ruyven L J,Valeton J M,Mering thermal and visual images by a contrast pyramid[J],Optical Engineering,1989,28(7):789-792
[16]Therrien C.W,Scrofani J.W,Kreb W.K,An adaptive technique for the enhanced fusion of low-light visible with uncooled thermal infrared imagery[J],International Conference on Image Processing,1997
[17]Costantini M,Farina A,Zirilli F,The fusion of different resolution SAR images,ESREN,Eur Space Agency,Frascati,Italy
[18]王海晖,彭嘉雄,吴巍.评价多传感器图像融合效果方法的比较.红外与激光工程,Vol.33,NO.2,Apr.2004
[19]L.Jimenez,D.A.Landgrebe,Projection pursuit for high dimensional feature reduction:Parallel and sequential approaches,presented at the Int.Geoscience and Remote Sensing Symp,Florence,Italy,July 10-14,1995
[20]Burt P J,Kolczynski R J.Enhanced image capture through fusion.In:IEEE 4th International Conf on Computer Vision.1993,4.173-182
[21]Zhijun Wang,Djemel Ziou,Costas Armenakis,Deren Li,Qingquan Li,A Comparative Analysis of Image Fusion Methods,IEEE Transactions on geoscience and remote sensing,Vol.43,No.6,June 2005,1391-1401
[22]Mallat S G.A wavelet tour of signal processing[M].San Diego:Academic Press,1998:302-310
[23]Maria Petrou,Smart Decision Support System Using Parsimonious Information Fusion,2005 7th International Conference on Information Fusion.
[24]王洪华,王双亭,杜春萍.基于多进制小波的多源遥感影像融合[J].中国图像图形学报,2002(4):341-345
[25]贾永红.多源遥感影像数据融合方法及其应用研究[D].武汉大学,2001
[26]崔岩梅,倪国强,钟堰利等.利用统计特性进行图像融合效果分析及评价[J].北京理工大学学报,2000,20(1):102-106
[27]Hall D L.Mathematical Techniques in multi-sensor data fusion[M].Boston:Artech House,1992
[28]夏明革,何友,黄晓冬.多传感器图像融合效果评价方法研究.光电与控制,Vol.10,No.2.May 2003
[29]Miguel Angel Carreira-Perpinan,Continuous latent variable models for dimensionality reduction and sequential data reconstruction,Department of Computer Science University of Sheffield,UK.February 2001
[30]钟志勇,陈鹰.多源信息融合中小波变换的应用研究[J].测绘学报,2002
[31]王智均,李德仁,李清泉.利用小波变换对影像进行融合的研究[J].武汉测绘科技大学学报,2000,25(2):137-142
[32]陈勇,皮德富,周士源等.基于小波变换的红外图像融合技术研究[J].红外与激光工程,2001,30(1):15-17
[33]QiangZanxia,Peng Jiaxiong.Remote sensing image fusions based on local deviation of wavelet transform[J].HuaZhong Univ.of Sci.&Tech.(Nature Science Edition),2003,31(6):89-91