经验模态分解与Savitzky-Golay方法的自适应遥感影像融合
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摘要
多源遥感影像融合是一种综合利用来自不同传感器或者同一种传感器不同波段影像生成新的影像数据的图像处理技术。新的融合影像同时具有各种传感器或者同种传感器不同波段影像的光谱和细节信息。随着图像融合处理技术的快速发展,多源遥感影像融合也逐渐成为遥感研究和应用的热点。本文在对比分析现有传统融合算法的基础上,以Quick Bird和TM多光谱和全色影像为数据源,进行了一系列融合实验,主要工作和结论如下：
(1)通过实验,分析比较了传统遥感影像融合技术,如ISH变换融合、主成分分析(PCA)融合、Brovey融合、小波变换融合和高通滤波(HPF)融合等融合方法的优缺点,并指出了各种融合算法存在的问题。
(2)把经验模态分解方法应用于遥感影像融合的高低频信息分离过程中,提出了基于二维经验模态分解的遥感影像融合算法。首先把基于行列的经验模态分解方法应用于遥感影像融合,针对该方法融合过程中产生的条带状噪声,提出把二维经验模态分解应用于融合过程中的高低频信息分离。经过验证发现,基于二维经验模态分解融合后的影像中条带状噪声得到很好的抑制。与传统融合算法比较,该方法可以在增强影像细节信息的同时很好的保持图像光谱信息。
(3)把Savitzky-Golay方法拓展到二维,应用于遥感影像融合的高低频信息分离之中,提出了基于Savitzky-Golay方法的遥感影像融合算法。设计了不同阶数和不同次数的二维Savitzky-Golay算子用于图像融合,并与不同分解次数的小波变换融合进行了详细的比较。发现Savitzky-Golay算子的阶数与小波变换的分解次数在融合过程中起到相同的作用。Savitzky-Golay算子阶数越高,小波变换分解次数越多都会导致融合后的影像细节信息更加丰富,不过光谱信息会同时受到不同程度的损失。
(4)总结分析了图像融合过程中已有的融合规则,提出了一种新的融合规则用于基于二维经验模态分解的融合方法和基于Savitzky-Golay算子的遥感影像融合,以使融合算法自适应化。经过验证,该融合规则可以很好地协调融合过程中细节信息的注入和光谱信息的保持。
Multi-source remote sensing image fusion is a technology of image processing that uses different sensor images or different band images from one sensor to generate a new image. The new integrated image should both have the spectral information and detail information of different sensor images or different band images. With the rapid development of image processing, multi-source remote sensing image fusion has become a hot research and application field in remote sensing. A series of fusion experiments were carried out using Quick Bird and TM multispectral and panchromatic images as the source data based on comparative analysis of the existing traditional fusion algorithms in this paper. The main work and conclusions are as follows:
(1) The advantages and disadvantages of traditional fusion techniques such as ISH transform fusion, principal component analysis (PCA) fusion, Brovey fusion, wavelet transform fusion and high-pass filter (HPF) fusion were comparatively analyzed through experiments. The problems that exist in the traditional fusion algorithms were presented.
(2) The empirical mode decomposition method was applied to high-low frequency information separation in remote sensing image fusion and a new remote sensing image fusion algorithm based on the two-dimensional empirical mode decomposition was proposed. Firstly, the empirical mode decomposition based on row and column was used in image fusion. As the belt noise was introduced by the new fusion method, a fusion algorithm based on the two-dimensional empirical mode decomposition was used to separate the high-low frequency information. The fusion algorithm based on the two-dimensional empirical mode decomposition can effectively remove the belt noise was proved by experiment. And compared with traditional fusion algorithms, this method can enhance the image detail while maintaining excellent spectral information.
(3) The Savitzky-Golay method was extended to two-dimension, and then was used to the high-low frequency information separation. A new remote sensing image fusion algorithm based on Savitzky-Golay method was proposed. A series of Savitzky-Golay operators with different order and power were designed. Then a detailed comparison was carried out between the new fusion algorithm and wavelet transform fusion. The conclusion that the number of the order of Savitzky-Golay operator and the number of decomposition of wavelet transform has the same effect in image fusion was presented. Higher order of Savitzky-Golay operator and bigger of number of wavelet transform would both result in more detail information but less spectral information.
(4) The fusion rules that used in traditional fusion algorithms were analyzed and summarized. A new fusion rule for the fusion methods based on two-dimensional empirical mode decomposition and Savitzky-Golay was proposed to make the fusion algorithms self-adaptive. Lastly, the conclusion that the fusion rule could harmonize the detail information and spectral information well was presented.
(1)通过实验,分析比较了传统遥感影像融合技术,如ISH变换融合、主成分分析(PCA)融合、Brovey融合、小波变换融合和高通滤波(HPF)融合等融合方法的优缺点,并指出了各种融合算法存在的问题。
(2)把经验模态分解方法应用于遥感影像融合的高低频信息分离过程中,提出了基于二维经验模态分解的遥感影像融合算法。首先把基于行列的经验模态分解方法应用于遥感影像融合,针对该方法融合过程中产生的条带状噪声,提出把二维经验模态分解应用于融合过程中的高低频信息分离。经过验证发现,基于二维经验模态分解融合后的影像中条带状噪声得到很好的抑制。与传统融合算法比较,该方法可以在增强影像细节信息的同时很好的保持图像光谱信息。
(3)把Savitzky-Golay方法拓展到二维,应用于遥感影像融合的高低频信息分离之中,提出了基于Savitzky-Golay方法的遥感影像融合算法。设计了不同阶数和不同次数的二维Savitzky-Golay算子用于图像融合,并与不同分解次数的小波变换融合进行了详细的比较。发现Savitzky-Golay算子的阶数与小波变换的分解次数在融合过程中起到相同的作用。Savitzky-Golay算子阶数越高,小波变换分解次数越多都会导致融合后的影像细节信息更加丰富,不过光谱信息会同时受到不同程度的损失。
(4)总结分析了图像融合过程中已有的融合规则,提出了一种新的融合规则用于基于二维经验模态分解的融合方法和基于Savitzky-Golay算子的遥感影像融合,以使融合算法自适应化。经过验证,该融合规则可以很好地协调融合过程中细节信息的注入和光谱信息的保持。
Multi-source remote sensing image fusion is a technology of image processing that uses different sensor images or different band images from one sensor to generate a new image. The new integrated image should both have the spectral information and detail information of different sensor images or different band images. With the rapid development of image processing, multi-source remote sensing image fusion has become a hot research and application field in remote sensing. A series of fusion experiments were carried out using Quick Bird and TM multispectral and panchromatic images as the source data based on comparative analysis of the existing traditional fusion algorithms in this paper. The main work and conclusions are as follows:
(1) The advantages and disadvantages of traditional fusion techniques such as ISH transform fusion, principal component analysis (PCA) fusion, Brovey fusion, wavelet transform fusion and high-pass filter (HPF) fusion were comparatively analyzed through experiments. The problems that exist in the traditional fusion algorithms were presented.
(2) The empirical mode decomposition method was applied to high-low frequency information separation in remote sensing image fusion and a new remote sensing image fusion algorithm based on the two-dimensional empirical mode decomposition was proposed. Firstly, the empirical mode decomposition based on row and column was used in image fusion. As the belt noise was introduced by the new fusion method, a fusion algorithm based on the two-dimensional empirical mode decomposition was used to separate the high-low frequency information. The fusion algorithm based on the two-dimensional empirical mode decomposition can effectively remove the belt noise was proved by experiment. And compared with traditional fusion algorithms, this method can enhance the image detail while maintaining excellent spectral information.
(3) The Savitzky-Golay method was extended to two-dimension, and then was used to the high-low frequency information separation. A new remote sensing image fusion algorithm based on Savitzky-Golay method was proposed. A series of Savitzky-Golay operators with different order and power were designed. Then a detailed comparison was carried out between the new fusion algorithm and wavelet transform fusion. The conclusion that the number of the order of Savitzky-Golay operator and the number of decomposition of wavelet transform has the same effect in image fusion was presented. Higher order of Savitzky-Golay operator and bigger of number of wavelet transform would both result in more detail information but less spectral information.
(4) The fusion rules that used in traditional fusion algorithms were analyzed and summarized. A new fusion rule for the fusion methods based on two-dimensional empirical mode decomposition and Savitzky-Golay was proposed to make the fusion algorithms self-adaptive. Lastly, the conclusion that the fusion rule could harmonize the detail information and spectral information well was presented.
引文
Aschbacher,J., and Lichtenegger,J. Complementary nature of SAR and optical data:a case study in the Tropics[J].Earth Observation Quarterly,1990,31,4-8.
Bloom,A.,Fielding,E.,and Fu,X. A demonstration of stereophotogrammetry with combined SIR-B and Landsat-TM images[J].International Journal of Remote Sensing,1988,9,1023-1038.
Carper J.W., Lillesand T.M.,and Kiefer R.W. The use of Intensity-Hue-Saturation transformations for merging SPOT panchromatic and multispectral image data[J].Photogrammetric Engineering and Remote Sensing,1990,56(4):459-467.
Carter D.B. Analysis of Multiresolution Data Fusion Techniques[Z].1998, Faculty of the Virginia Polytechnic Institute and State University master's degree monograph.
Chavez, P.S.,Jr., and Kwarteng, A.W. Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis[J].Photogrammetric Engineering and Remote Sensing,1989,55(3):339-348.
Chavez,P.S.Sides,S.C.,and Anderson,J.A. Comparison of three different methods to merge multiresolution and multispectral data:TM&SPOT pan[J].Photogrammetric Engineering and Remote Sensing,1991,57,295-303.
Chavez,P.S.,Sides,S.C., and Anderson,J.A. Comparison of three different methods to merge multiresolution and multispectral data:TM&SPOT pan[J].Photogrammetric Engineering and Remote Sensing,1991,57,295-303.
Deasy J O.Denoising of electron beam Monte Carlo dose distributions using digital filtering techniques[J].Physics in Medicine and Biology,2000,45:1765-1779.
Duguay,G., Holder,G., Howarth,P., and Ledrew,E. Integrating of remotely sensed data from different sensors for change detection[J].Proceeding of the IEEE International Geoscience and Remotely Sensing Symposium (IGARSS'87),1987,333-340.
Gonzalez R.C.,Woods R.E.(阮秋琦,阮宇智等译).数字图像处理[M],北京：电子工业出版社,2006.
Harris J.R.,Murray R.IHS transform for the integration of Radar Imagery with other remotely sensed data[J].Photogrammetric Engineering and Remote Sensing,1990,56(1):1631-1641.
Haydn R.,Dalke G.W.,and Henkel J.Application of the ISH color transform to the processing of multisensor data and image enhancement[J].Proceeding International Symposium on Remote Sensing of Arid and Semi-arid Land,1982,599-616.
Huang N E,Shen Z,Long S R.Anew view of nonlinear water waves:the Hilbert spectrum[J].Ann.Rev.Mech.,1999,31:417-457.
Huang N E,Shen Z,Long S R.The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis[J].Proc.of the Royal Society of London,1998, 454:903-995.
Jutz,S.L.,and Chorowicz,J.Geological mapping and detection of oblique extension structures in the Kenyan Rift Valley with a SPOT/Landsat-TM datamerge[J].International Journal of Remote Sensing,1993,14,1677-1688.
Koopmans,B.N. and Forero,G.R. Airborne SAR and Landsat MSS as complementary information source for geological hazard mapping[J].ISPRS Journal of Photogrammetry and Remote Sensing,1993,48,28-37.
Leckie,D.G. Synergism of SAR and visible/infrared data for forest type discrimination[J]. Photogrammetric Engineering and Remote Sensing,1990,56,1237-1246.
Ling,Y.,Ehlers,M. FFT-enhanced IHS transformation method for fusing high-resolution satellite images[J]. ISPRS Journal of Photogrammetry & Remote Sensing,2007,61,381-392.
Madden H H. Comments on the Savitzky-Golay convolution method for least-squares-fit smoothing and differentiation of digital data[J].Analytical Chemistry,1978,50(9):1383-1386.
Maiter H, Bloch I. Image fusion[J].Vistas in Astronomy,1997,41(3):329-335.
Opescu,N.,Serban,F.,Braescu,C. and Necsoiu,M. Technological researches and developments, regarding the integration of SAR-ERS data with other types of satellite and ground information, as well as determining their complementarity, with applications over the continental platform of the Black Sea, the Danube Delta and the seismic zone Vrancea; some contributions to data calibration and validation[J].Proceedings of First ERS-1 Pilot Project Workshop,1994,199-204.
Park H J,Kim K K,Yang Y K. Image fusion using multiresolution analysis[C].///In Proc ofGeoscience and Remote Sensing Symposium,New Jersey,2001:864-866.
Pellemans A.H.,Jordans R.W.,Allewijin,R. Merging multi-spectral and panchromatic SPOT images with respect to the radiometric properties of the sensor[J].Photogrammetric Engineering and Remote Sensing,1993,59(1):81-87.
Pohl,C., Van Genderen,J.L. Multisensor image fusion in remote sensing:concepts,methods and applications[J].International Journal of Remote Sensing,1998,19(5):823-854.
Qiu Z. C. The study on the remote sensing data fusion[J].Acta Geodactica et Cartographica Sinica,1990,19(4):290-296.
Ranchin,T., Wald,L. Fusion of high spatial and spectral resolution images:the ARSIS concepts and its implementation[J].Photogrammetric Engineering and Remote Sensing,2000,66(1):49-61.
Rast,M., Jaskolla,M., and Arandson,F.K. Comparative digital analysis of Seasat-SAR and Landsat-TM data for Iceland[J].International Journal of Remote Sensing,1991,12,527-544.
Savitzky A, Golay M J. Smoothing and Differentiation of Data by Simplified Least Squares Procedures[J].Analytical Chemistry,1964,36(8):1627-1639.
Schistad-Solberg,A.H.,Jain,A.K.,and Taxt,T.Multisource classification of remotely sensed data: fusion of Landsat TM and SAR images[J].IEEE transactions on Geoscience and Remote Sensing,1994,32,768-778.
Schowengerdt R.A. Reconstruction of multispatial, multispectral image data using spatial frequency content[J].Photogrammetric Engineering and Remote Sensing,1980,46,1325-1334.
Shettigara V.K. A generalized component substitution technique for spatial enhancement of multispectral images using a higher resolution data set[J].Photogrammetric Engineering and Remote Sensing,1992,58(5):561-567.
Smara,Y.,Belhadj-Aissa,A., and Sansal,B. Decorrelation and IHS color transforms applied for multisource satellite images integration[J].Fusion of Erath Data, Proceedings of EARSeL Conference,1996,107-112.
Steinier J,Termonia Y,Deltour J.Comments on Smoothing and differentiation of data by simplified least square procedure[J].Analytical Chemistry,1972,44(11):1906-1909.
Strobl,D.,Raggam,J.,and Buchroithner,M.F. Terrain correction geocoding of a multi-sensor image data set[J].Proceedings 10th EARSeL Symposium,1990,98-107.
Suits,G.,Malila,W.,and Weller,T. Procedures for using signals form one sensor as substitutes for signals of another[J].Remote Sensing of Environment,1988,25,395-408.
Tauch,R.,and Kahler,M. Improving the quality of satellite image maps by various processing techniques. International Archives of Photogrammetry and Remote Sensing, Proceedings XVI ISPRS Congress, Kyoto, Japan,1988,238-247.
Taxt T, Solberg A H S.Information Fusion in Remote Sensing[J].Vistas in Astronomy,1997, 41(3):337-342.
Vrabel J.Multispectral imagery advanced band sharpening study[J].Photogrammetric Engineering and Remote Sensing,2000,66(1):73-79.
Yesou,H.,Besnus,Y., and Rolet,J. Merging Seasat and SPOT imagery for the study of geologic structures in a temperate agriculture region[J]. Remote Sensing of Environment,1993b,43,265-280.
Yesou,H.,Besnus,Y.,Rolet,J.,and Poin,J.C. Extraction of spectral information from Landsat TM data and merger with SPOT panchromatic imagery-a contribution to the study of geological structures[J].ISPRS Journal of Photogrammetry and Remote Sensing,1993a,48,23-36.
Yildimi,H., Alparstan,E.,and Ozel,M.E. Data fusion of sharpened difference image by IHS transform[J].Fusion of Erath Data, Proceedings of EARSeL Conference,1996,125-130.
Yochy D.A. Multiresolution wavelet decomposition image merger of Lansat thematic mapper and SPOT panchromatic data[J]. Photogrammetric Engineering and Remote Sensing,1996,62(9):1067-1074.
Yocky D.A. Image merging and data fusion using the discrete two-dimension wavelet transform[J]. Journal of the Optical Society of America Am1995,12(9):1834-1841.
Zhang,Y.,Hong,G. An IHS and wavelet integrated approach to improve pan-sharpening visual quality of natural colour IKONOS and QuickBird images[J].Information fusion,2005,6,225-234.
曹闻,李弼程,彭天强.一种基于小波包变换的遥感影像融合方法[J].遥感技术与应用,2003,18(4)：248-253.
曹闻,张勇.基于小波包变换与IHS变换的遥感图像融合[J].测绘学院学报,2004,21(2)：114-117.
晁锐,张科,李言俊.一种基于小波变换的图像融合算法[J].电子学报,2004,32(5)：750-753.
陈桂红,唐伶俐,戴昌达等.洪涝灾情快速反应的星载SAR与TM数据的融合处理[J].地球信息科学,2003,1,103-108.
陈怀新,王连亮.基于PCA的小波多分辨率图像融合方法[J].电讯技术,2006,1,79-82.
陈荣元,刘国英,王雷光等.基于数据同化的全色和多光谱遥感影像融合[J].武汉大学学报·信息科学版,2009,34(8)：919-923.
陈文辉.经验模态分解及其在图像分割中的应用[Z].2008,长沙理工大学硕士学位论文.
陈志刚,束炯.高光谱图像光谱域噪声去除的经验模态分解方法[J].红外与毫米波学报,2008,27(5)：378-382.
代华冰,李春干.森林资源监测中SPOT5数据融合方法的比较[J].林业资源管理,2005,3,76-79.
单新建,叶洪,陈国光等.利用多源数据融合研究长白山天池火山的喷发泉期次及空间分布[J].自然灾害学报,2001,10(2)：107-112.
邓磊,陈云浩,李京.一种基于小波变换的可调节遥感影像融合方法[J].红外与毫米波学报,2005,24(1)：34-38.
邓磊,李京,陈云浩等.几种小波融合方法在遥感影像融合中的应用与比较[J].遥感信息,2007,6,23-27.
董长虹等.Matlab小波分析工具箱原理与应用[M].北京：国防工业出版社,2004.
顾娟,李新,黄春林NDVI时间序列数据集重建方法述评[J].遥感技术与应用,2006,21(4)：391-395.
郭彤颖,吴成东,曲道奎.小波变换理论应用进展[J].信息与控制,2004,33(1)：67-71.
韩震,金亚秋.ERS-2 SAR和Landsat ETM+数据融合提取崇明东滩典型地物信息与分类[J].海洋环境科学,2006,35(3)：21-24.
何贵青,郝重阳等.基于灰色关联分析和IHS变换的图像融合算法[J].计算机应用研究,2007,24(7)：312-314.
胡钢,刘哲,高瑞,徐小平.基于小波变换的自适应图像融合算法[J].西安理工大学学报,2007,23(3)：286-290.
胡子付,曾志远等.小波与IHS结合实现ETM图像波段融合[J].地球信息科学,2005,7(4)：29-32.
黄金,潘泉等.基于区域特征加权的IHS图像融合方法[J].计算机工程与应用,2006,6,39-41.
贾永红,李德仁,孙家抦,刘继林.四种IHS变换用于SAR与TM影像复合的比较[J].遥感学报,1998,2(2)：103-106.
贾永红.多源遥感影像数据融合技术[M],北京：测绘出版社,2005.
柯小玲.一种HPF的图像融合方法[J].闽江学院学报,2008,29(2)：83-86.
黎新亮,赵书河,柯常青等.遥感图像融合定量评价方法及实验研究[J].遥感技术与应用,2007,22(3)：460-465.
李清顺,王京民,张忠义等.多分辨率遥感数据融合及其在林业调查区划中的应用研究[J].陕西林业科学,2007,4,52-54.
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,2002,23(6)：651-654.
廖文峰等.基于数据融合的土地遥感分类技术研究[J].新疆气象,2004,27(4)：22-24.
林桂芬,罗立民.基于三维Savitzky-Golay滤波的蒙特卡罗剂量分布去噪[J].数据采集与处理,2006,21(4)：439-443.
林卉,景海涛,张连蓬.a'Trous小波与PCA变换结合的遥感影像融合分析[J].地球信息科学,2008,10(2)：269-272.
刘斌,彭嘉雄.基于小波包变换的区域图像融合方法[J].计算机工程与应用,2004,21：36-39.
刘红毅,韦志辉.基于分数阶样条小波与IHS变换的图像融合[J].南京理工大学学报,2006,30(1)：81-84.
刘凯,黎夏,王树功等.基于神经网络和数据融合的红树林群落分类研究[J].遥感信息,2006,3,32-35.
刘清华,石军南,熊珂.遥感图像融合技术在森林资源调查中的应用[J].四川林堪设计,2008,2,66-70.
刘松涛,沈同圣,杨绍清.基于自适应融合规则的多分辨率图像融合算法[J].激光与红外,2007,37(8)：766-791.
刘星,胡光道.多源数据融合技术在成矿预测中的应用[J].地球学报,2003,24(5)：463-468.
刘扬.基于PCA和小波包的遥感图像融合算法[J].中国水运,2007,5(1)：140-141.
刘忠轩,彭思龙.方向EMD分解与其在纹理分割中的应用[J].中国科学E辑信息科学,2005,35(2)：113-123.
陆欢,吴庆宪,姜长生.基于PCA与小波变换的彩色图像融合算法[J].计算机仿真,2007,24(9)：202-205.
梅安新等.遥感导论[M],北京：高等教育出版社,2001.
苗军,邓钢,杨家邦.ETM全色与多光谱数据融合方法在地质调查中的比较研究[J].地矿测绘,2008,24(1)：26-29
秦勃,时鹏.经验模式多尺度图像分解[J].工程图学学报,2002,4,73-78.
沈滨,崔峰,彭思龙.二维EMD的纹理分析及图像瞬时频率估计[J].计算机辅助设计与图形学学报,2005,17(10)：2345-2352.
孙家柄等.多源遥感影像融合[J].遥感学报,1998,2(1)：47-50.
覃征等.数字图像融合[M],西安：西安交通大学出版社,2004.
王国峰,张科,李言俊.基于小波多分辨率分析的图像融合方法[J].红外与激光工程,2003,32(5)：513-515.
王海晖,彭嘉雄,吴巍等.多源遥感图像融合效果评价方法研究[J].计算机工程与应用,2003,25,33-37.
王建,鲁安新,郭庭天等.Brovey图像融合在引大灌区土地覆盖调查中的应用[J].遥感技术与应用,2001,16(3)：173：177.
王建梅,李德仁.QuickBird全色与多光谱数据融合方法用于土地覆盖分类中的比较研究[J].测绘通报,2005,10,37-40.
王强,束炯.高光谱图像光谱域噪声检测与去除的DSGF方法[J].红外与毫米波学报,2006,25(1)：29-32.
王胜利,余学襄.基于IHS变换的最优树分解在遥感图像融合中的应用[J].国土资源遥感,2007,72(2)：78-81.
王文武.应用主成分分解(PCA)法的图像融合技术[J].微计算机信息,2007,23(4)：285-286.
吴连喜,于水根,刘晓梅等.土地利用动态监测的LFF图像融合改进应用[J].地球信息科学,2004,6(3)
吴连喜等.不同年份的Radarsat与TM数据融合探测土地利用变更[J].土壤通报,2004,35(2)：126-129.
肖李,卢凌等.基于改进IHS变换的图像融合方法[J].武汉理工大学学报(交通科学与工程版),2003,27(1)：41-45.
徐建达,王洪华.基于IHS变换和小波变换的遥感影像融合[J].测绘学院学报,2002,19(3)：198-202.
杨垣,梁继民等.基于进化策略和IHS变换的图像融合方法[J].电子学报,2001,29(10)：1388-1391.
杨烜,裴继红,杨万海.小波变换方法在高分辨率多光谱图像融合中存在的问题[J].红外与毫米波学报,2002,21(2)：77-80
叶宝军,徐士进,吴俊奇等.基于IHS变换和小波变换相结合的IKONOS影像融合技术[J].2005,23(2)：154-158.
翟军涛,那彦,孟捷.基于非采样的contourlet变换的多光谱和全色图像自适应融合算法[J].系统工程与电子技术,2009,31(4)：765-767.
张宁玉,张宝金.基于Quick Bird光栅图像的IHS和PCA融合研究[J].山东科技大学学报(自然科学版),2005,24(3)：43-46.
张永生等.天基多源遥感信息融合——理论、算法与应用系统[M],北京：科学出版社,2005.
章毓晋.图象工程上册：图象处理和分析[M].北京：清华大学出版社,1999.
周兰花,周付根.基于小波变换极大模的多模医学图像融合[J].中国体视学与图像分析,2003,8(4)：225-229.
周欣,李衷怡.基于二维经验模态分解的图像去噪[J].计算机与数字工程,2007,35(11),93-94.
Bloom,A.,Fielding,E.,and Fu,X. A demonstration of stereophotogrammetry with combined SIR-B and Landsat-TM images[J].International Journal of Remote Sensing,1988,9,1023-1038.
Carper J.W., Lillesand T.M.,and Kiefer R.W. The use of Intensity-Hue-Saturation transformations for merging SPOT panchromatic and multispectral image data[J].Photogrammetric Engineering and Remote Sensing,1990,56(4):459-467.
Carter D.B. Analysis of Multiresolution Data Fusion Techniques[Z].1998, Faculty of the Virginia Polytechnic Institute and State University master's degree monograph.
Chavez, P.S.,Jr., and Kwarteng, A.W. Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis[J].Photogrammetric Engineering and Remote Sensing,1989,55(3):339-348.
Chavez,P.S.Sides,S.C.,and Anderson,J.A. Comparison of three different methods to merge multiresolution and multispectral data:TM&SPOT pan[J].Photogrammetric Engineering and Remote Sensing,1991,57,295-303.
Chavez,P.S.,Sides,S.C., and Anderson,J.A. Comparison of three different methods to merge multiresolution and multispectral data:TM&SPOT pan[J].Photogrammetric Engineering and Remote Sensing,1991,57,295-303.
Deasy J O.Denoising of electron beam Monte Carlo dose distributions using digital filtering techniques[J].Physics in Medicine and Biology,2000,45:1765-1779.
Duguay,G., Holder,G., Howarth,P., and Ledrew,E. Integrating of remotely sensed data from different sensors for change detection[J].Proceeding of the IEEE International Geoscience and Remotely Sensing Symposium (IGARSS'87),1987,333-340.
Gonzalez R.C.,Woods R.E.(阮秋琦,阮宇智等译).数字图像处理[M],北京：电子工业出版社,2006.
Harris J.R.,Murray R.IHS transform for the integration of Radar Imagery with other remotely sensed data[J].Photogrammetric Engineering and Remote Sensing,1990,56(1):1631-1641.
Haydn R.,Dalke G.W.,and Henkel J.Application of the ISH color transform to the processing of multisensor data and image enhancement[J].Proceeding International Symposium on Remote Sensing of Arid and Semi-arid Land,1982,599-616.
Huang N E,Shen Z,Long S R.Anew view of nonlinear water waves:the Hilbert spectrum[J].Ann.Rev.Mech.,1999,31:417-457.
Huang N E,Shen Z,Long S R.The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis[J].Proc.of the Royal Society of London,1998, 454:903-995.
Jutz,S.L.,and Chorowicz,J.Geological mapping and detection of oblique extension structures in the Kenyan Rift Valley with a SPOT/Landsat-TM datamerge[J].International Journal of Remote Sensing,1993,14,1677-1688.
Koopmans,B.N. and Forero,G.R. Airborne SAR and Landsat MSS as complementary information source for geological hazard mapping[J].ISPRS Journal of Photogrammetry and Remote Sensing,1993,48,28-37.
Leckie,D.G. Synergism of SAR and visible/infrared data for forest type discrimination[J]. Photogrammetric Engineering and Remote Sensing,1990,56,1237-1246.
Ling,Y.,Ehlers,M. FFT-enhanced IHS transformation method for fusing high-resolution satellite images[J]. ISPRS Journal of Photogrammetry & Remote Sensing,2007,61,381-392.
Madden H H. Comments on the Savitzky-Golay convolution method for least-squares-fit smoothing and differentiation of digital data[J].Analytical Chemistry,1978,50(9):1383-1386.
Maiter H, Bloch I. Image fusion[J].Vistas in Astronomy,1997,41(3):329-335.
Opescu,N.,Serban,F.,Braescu,C. and Necsoiu,M. Technological researches and developments, regarding the integration of SAR-ERS data with other types of satellite and ground information, as well as determining their complementarity, with applications over the continental platform of the Black Sea, the Danube Delta and the seismic zone Vrancea; some contributions to data calibration and validation[J].Proceedings of First ERS-1 Pilot Project Workshop,1994,199-204.
Park H J,Kim K K,Yang Y K. Image fusion using multiresolution analysis[C].///In Proc ofGeoscience and Remote Sensing Symposium,New Jersey,2001:864-866.
Pellemans A.H.,Jordans R.W.,Allewijin,R. Merging multi-spectral and panchromatic SPOT images with respect to the radiometric properties of the sensor[J].Photogrammetric Engineering and Remote Sensing,1993,59(1):81-87.
Pohl,C., Van Genderen,J.L. Multisensor image fusion in remote sensing:concepts,methods and applications[J].International Journal of Remote Sensing,1998,19(5):823-854.
Qiu Z. C. The study on the remote sensing data fusion[J].Acta Geodactica et Cartographica Sinica,1990,19(4):290-296.
Ranchin,T., Wald,L. Fusion of high spatial and spectral resolution images:the ARSIS concepts and its implementation[J].Photogrammetric Engineering and Remote Sensing,2000,66(1):49-61.
Rast,M., Jaskolla,M., and Arandson,F.K. Comparative digital analysis of Seasat-SAR and Landsat-TM data for Iceland[J].International Journal of Remote Sensing,1991,12,527-544.
Savitzky A, Golay M J. Smoothing and Differentiation of Data by Simplified Least Squares Procedures[J].Analytical Chemistry,1964,36(8):1627-1639.
Schistad-Solberg,A.H.,Jain,A.K.,and Taxt,T.Multisource classification of remotely sensed data: fusion of Landsat TM and SAR images[J].IEEE transactions on Geoscience and Remote Sensing,1994,32,768-778.
Schowengerdt R.A. Reconstruction of multispatial, multispectral image data using spatial frequency content[J].Photogrammetric Engineering and Remote Sensing,1980,46,1325-1334.
Shettigara V.K. A generalized component substitution technique for spatial enhancement of multispectral images using a higher resolution data set[J].Photogrammetric Engineering and Remote Sensing,1992,58(5):561-567.
Smara,Y.,Belhadj-Aissa,A., and Sansal,B. Decorrelation and IHS color transforms applied for multisource satellite images integration[J].Fusion of Erath Data, Proceedings of EARSeL Conference,1996,107-112.
Steinier J,Termonia Y,Deltour J.Comments on Smoothing and differentiation of data by simplified least square procedure[J].Analytical Chemistry,1972,44(11):1906-1909.
Strobl,D.,Raggam,J.,and Buchroithner,M.F. Terrain correction geocoding of a multi-sensor image data set[J].Proceedings 10th EARSeL Symposium,1990,98-107.
Suits,G.,Malila,W.,and Weller,T. Procedures for using signals form one sensor as substitutes for signals of another[J].Remote Sensing of Environment,1988,25,395-408.
Tauch,R.,and Kahler,M. Improving the quality of satellite image maps by various processing techniques. International Archives of Photogrammetry and Remote Sensing, Proceedings XVI ISPRS Congress, Kyoto, Japan,1988,238-247.
Taxt T, Solberg A H S.Information Fusion in Remote Sensing[J].Vistas in Astronomy,1997, 41(3):337-342.
Vrabel J.Multispectral imagery advanced band sharpening study[J].Photogrammetric Engineering and Remote Sensing,2000,66(1):73-79.
Yesou,H.,Besnus,Y., and Rolet,J. Merging Seasat and SPOT imagery for the study of geologic structures in a temperate agriculture region[J]. Remote Sensing of Environment,1993b,43,265-280.
Yesou,H.,Besnus,Y.,Rolet,J.,and Poin,J.C. Extraction of spectral information from Landsat TM data and merger with SPOT panchromatic imagery-a contribution to the study of geological structures[J].ISPRS Journal of Photogrammetry and Remote Sensing,1993a,48,23-36.
Yildimi,H., Alparstan,E.,and Ozel,M.E. Data fusion of sharpened difference image by IHS transform[J].Fusion of Erath Data, Proceedings of EARSeL Conference,1996,125-130.
Yochy D.A. Multiresolution wavelet decomposition image merger of Lansat thematic mapper and SPOT panchromatic data[J]. Photogrammetric Engineering and Remote Sensing,1996,62(9):1067-1074.
Yocky D.A. Image merging and data fusion using the discrete two-dimension wavelet transform[J]. Journal of the Optical Society of America Am1995,12(9):1834-1841.
Zhang,Y.,Hong,G. An IHS and wavelet integrated approach to improve pan-sharpening visual quality of natural colour IKONOS and QuickBird images[J].Information fusion,2005,6,225-234.
曹闻,李弼程,彭天强.一种基于小波包变换的遥感影像融合方法[J].遥感技术与应用,2003,18(4)：248-253.
曹闻,张勇.基于小波包变换与IHS变换的遥感图像融合[J].测绘学院学报,2004,21(2)：114-117.
晁锐,张科,李言俊.一种基于小波变换的图像融合算法[J].电子学报,2004,32(5)：750-753.
陈桂红,唐伶俐,戴昌达等.洪涝灾情快速反应的星载SAR与TM数据的融合处理[J].地球信息科学,2003,1,103-108.
陈怀新,王连亮.基于PCA的小波多分辨率图像融合方法[J].电讯技术,2006,1,79-82.
陈荣元,刘国英,王雷光等.基于数据同化的全色和多光谱遥感影像融合[J].武汉大学学报·信息科学版,2009,34(8)：919-923.
陈文辉.经验模态分解及其在图像分割中的应用[Z].2008,长沙理工大学硕士学位论文.
陈志刚,束炯.高光谱图像光谱域噪声去除的经验模态分解方法[J].红外与毫米波学报,2008,27(5)：378-382.
代华冰,李春干.森林资源监测中SPOT5数据融合方法的比较[J].林业资源管理,2005,3,76-79.
单新建,叶洪,陈国光等.利用多源数据融合研究长白山天池火山的喷发泉期次及空间分布[J].自然灾害学报,2001,10(2)：107-112.
邓磊,陈云浩,李京.一种基于小波变换的可调节遥感影像融合方法[J].红外与毫米波学报,2005,24(1)：34-38.
邓磊,李京,陈云浩等.几种小波融合方法在遥感影像融合中的应用与比较[J].遥感信息,2007,6,23-27.
董长虹等.Matlab小波分析工具箱原理与应用[M].北京：国防工业出版社,2004.
顾娟,李新,黄春林NDVI时间序列数据集重建方法述评[J].遥感技术与应用,2006,21(4)：391-395.
郭彤颖,吴成东,曲道奎.小波变换理论应用进展[J].信息与控制,2004,33(1)：67-71.
韩震,金亚秋.ERS-2 SAR和Landsat ETM+数据融合提取崇明东滩典型地物信息与分类[J].海洋环境科学,2006,35(3)：21-24.
何贵青,郝重阳等.基于灰色关联分析和IHS变换的图像融合算法[J].计算机应用研究,2007,24(7)：312-314.
胡钢,刘哲,高瑞,徐小平.基于小波变换的自适应图像融合算法[J].西安理工大学学报,2007,23(3)：286-290.
胡子付,曾志远等.小波与IHS结合实现ETM图像波段融合[J].地球信息科学,2005,7(4)：29-32.
黄金,潘泉等.基于区域特征加权的IHS图像融合方法[J].计算机工程与应用,2006,6,39-41.
贾永红,李德仁,孙家抦,刘继林.四种IHS变换用于SAR与TM影像复合的比较[J].遥感学报,1998,2(2)：103-106.
贾永红.多源遥感影像数据融合技术[M],北京：测绘出版社,2005.
柯小玲.一种HPF的图像融合方法[J].闽江学院学报,2008,29(2)：83-86.
黎新亮,赵书河,柯常青等.遥感图像融合定量评价方法及实验研究[J].遥感技术与应用,2007,22(3)：460-465.
李清顺,王京民,张忠义等.多分辨率遥感数据融合及其在林业调查区划中的应用研究[J].陕西林业科学,2007,4,52-54.
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,2002,23(6)：651-654.
廖文峰等.基于数据融合的土地遥感分类技术研究[J].新疆气象,2004,27(4)：22-24.
林桂芬,罗立民.基于三维Savitzky-Golay滤波的蒙特卡罗剂量分布去噪[J].数据采集与处理,2006,21(4)：439-443.
林卉,景海涛,张连蓬.a'Trous小波与PCA变换结合的遥感影像融合分析[J].地球信息科学,2008,10(2)：269-272.
刘斌,彭嘉雄.基于小波包变换的区域图像融合方法[J].计算机工程与应用,2004,21：36-39.
刘红毅,韦志辉.基于分数阶样条小波与IHS变换的图像融合[J].南京理工大学学报,2006,30(1)：81-84.
刘凯,黎夏,王树功等.基于神经网络和数据融合的红树林群落分类研究[J].遥感信息,2006,3,32-35.
刘清华,石军南,熊珂.遥感图像融合技术在森林资源调查中的应用[J].四川林堪设计,2008,2,66-70.
刘松涛,沈同圣,杨绍清.基于自适应融合规则的多分辨率图像融合算法[J].激光与红外,2007,37(8)：766-791.
刘星,胡光道.多源数据融合技术在成矿预测中的应用[J].地球学报,2003,24(5)：463-468.
刘扬.基于PCA和小波包的遥感图像融合算法[J].中国水运,2007,5(1)：140-141.
刘忠轩,彭思龙.方向EMD分解与其在纹理分割中的应用[J].中国科学E辑信息科学,2005,35(2)：113-123.
陆欢,吴庆宪,姜长生.基于PCA与小波变换的彩色图像融合算法[J].计算机仿真,2007,24(9)：202-205.
梅安新等.遥感导论[M],北京：高等教育出版社,2001.
苗军,邓钢,杨家邦.ETM全色与多光谱数据融合方法在地质调查中的比较研究[J].地矿测绘,2008,24(1)：26-29
秦勃,时鹏.经验模式多尺度图像分解[J].工程图学学报,2002,4,73-78.
沈滨,崔峰,彭思龙.二维EMD的纹理分析及图像瞬时频率估计[J].计算机辅助设计与图形学学报,2005,17(10)：2345-2352.
孙家柄等.多源遥感影像融合[J].遥感学报,1998,2(1)：47-50.
覃征等.数字图像融合[M],西安：西安交通大学出版社,2004.
王国峰,张科,李言俊.基于小波多分辨率分析的图像融合方法[J].红外与激光工程,2003,32(5)：513-515.
王海晖,彭嘉雄,吴巍等.多源遥感图像融合效果评价方法研究[J].计算机工程与应用,2003,25,33-37.
王建,鲁安新,郭庭天等.Brovey图像融合在引大灌区土地覆盖调查中的应用[J].遥感技术与应用,2001,16(3)：173：177.
王建梅,李德仁.QuickBird全色与多光谱数据融合方法用于土地覆盖分类中的比较研究[J].测绘通报,2005,10,37-40.
王强,束炯.高光谱图像光谱域噪声检测与去除的DSGF方法[J].红外与毫米波学报,2006,25(1)：29-32.
王胜利,余学襄.基于IHS变换的最优树分解在遥感图像融合中的应用[J].国土资源遥感,2007,72(2)：78-81.
王文武.应用主成分分解(PCA)法的图像融合技术[J].微计算机信息,2007,23(4)：285-286.
吴连喜,于水根,刘晓梅等.土地利用动态监测的LFF图像融合改进应用[J].地球信息科学,2004,6(3)
吴连喜等.不同年份的Radarsat与TM数据融合探测土地利用变更[J].土壤通报,2004,35(2)：126-129.
肖李,卢凌等.基于改进IHS变换的图像融合方法[J].武汉理工大学学报(交通科学与工程版),2003,27(1)：41-45.
徐建达,王洪华.基于IHS变换和小波变换的遥感影像融合[J].测绘学院学报,2002,19(3)：198-202.
杨垣,梁继民等.基于进化策略和IHS变换的图像融合方法[J].电子学报,2001,29(10)：1388-1391.
杨烜,裴继红,杨万海.小波变换方法在高分辨率多光谱图像融合中存在的问题[J].红外与毫米波学报,2002,21(2)：77-80
叶宝军,徐士进,吴俊奇等.基于IHS变换和小波变换相结合的IKONOS影像融合技术[J].2005,23(2)：154-158.
翟军涛,那彦,孟捷.基于非采样的contourlet变换的多光谱和全色图像自适应融合算法[J].系统工程与电子技术,2009,31(4)：765-767.
张宁玉,张宝金.基于Quick Bird光栅图像的IHS和PCA融合研究[J].山东科技大学学报(自然科学版),2005,24(3)：43-46.
张永生等.天基多源遥感信息融合——理论、算法与应用系统[M],北京：科学出版社,2005.
章毓晋.图象工程上册：图象处理和分析[M].北京：清华大学出版社,1999.
周兰花,周付根.基于小波变换极大模的多模医学图像融合[J].中国体视学与图像分析,2003,8(4)：225-229.
周欣,李衷怡.基于二维经验模态分解的图像去噪[J].计算机与数字工程,2007,35(11),93-94.
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