遥感数据融合研究进展与文献定量分析(1992—2018)
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摘要
近年来,遥感应用的快速发展推动了遥感载荷指标性能的不断提升。但由于遥感传感器的硬件技术瓶颈,遥感数据无法同时具有高空间分辨率、高光谱分辨率、高时间分辨率的指标特性。遥感数据融合是解决该问题的有效方法。为了深入了解目前遥感数据融合技术的研究进展情况,本文对国内外1992年—2018年间在该领域有一定影响力的相关成果进行了调研、分析与归纳总结。首先对遥感数据融合相关论文的年发文量、发文国家与机构、发表刊物以及关键词等进行了统计,梳理其发展历史及趋势;系统性的总结了各类数据融合算法,将其分为面向空间维提升的融合算法、面向光谱维提升的融合算法以及面向时间维提升的融合算法3类,并对各类算法的优势与适用性进行了分析;归纳总结了遥感数据融合的质量评价指标,包括有参考影像的融合评价指标以及无参考影像的融合评价指标;最后对遥感数据融合进展进行了总结与展望。
Remote sensing applications have been promoting the development of satellite sensors and their performances. The satellite sensors are now possessing higher and higher data resolutions, and it in turn greatly facilitating remote sensing applications. The improvement of spatial resolution makes it possible to precisely record textures and spatial features of land covers. The improvement of spectral resolution makes it possible to precisely classify and retrieve parameters. The improvement of temporal resolution makes it easier to record the temporal changes of land covers during different phases. However, due to the limitation of sensors, satellite data can not possess high spatial resolution, high temporal resolution and high spectral resolution at the same time, which greatly hinders further remote sensing applications.Remote sensing data fusion is one of the effective solutions to deal with the problem of limitation in sensors' resolutions, which is to integrate data from different sources and with different resolutions using algorithm methods to get richer information than one single image data. In recent years, the remote sensing data fusion methods have been greatly developed and related articles boost in growing numbers and importance. Thus, this article aims to systematically introduce the remote sensing data fusion and its current progress. Former reviews of remote sensing data fusion divides fusion methods into different categories. Based on processing levels, the remote sensing data fusion methods can be divided into pixel-level fusion, feature-level fusion and decision-level fusion. And based on the data sources, the remote sensing data fusion methods can be divided into homogeneous data fusion, heterogeneous data fusion, fusion for remote sensing observation and station data, and fusion for remote sensing observation and non-observed data. And this article adopts another category system which makes it more systematic and comprehensive in mathematical principles.In order to systematically indicate the current progress and developed history of remote sensing data fusion methods, this article first did bibliometric analysis on the remote sensing data fusion articles from data sources from Web of Science(WOS) and Chinese National Knowledge Infrastructure(CNKI). The data was preprocessed and has removed duplicates. Analysis including the aspects of yearly publishments, countries and organizations, journals and key words to see the history and trends have been done by Histcite, Bibliometric, Citespace and Endnote. Then the remote sensing fusion methods are systematically introduced. According to different resolution improvements, this article firstly divided the fusion methods into three categories: fusion methods emphasizing improvements of spatial resolution development,spectral development and temporal development, and based on that we further divide them into subcategories according to different mathematical principles. And the basic principles, some of the important methods, the advantages and flaws of these methods are systematically introduced in this section. Also, we did detailed instructions on fusion result assessment methods including methods with reference image and without reference image. Under these two categories we further introduced different assessment metrics and its applicable scenarios. And finally, this article made summaries of these fusion methods and described its features and directions. And finally makes predictions of future trends in remote sensing data fusion methods.
Remote sensing applications have been promoting the development of satellite sensors and their performances. The satellite sensors are now possessing higher and higher data resolutions, and it in turn greatly facilitating remote sensing applications. The improvement of spatial resolution makes it possible to precisely record textures and spatial features of land covers. The improvement of spectral resolution makes it possible to precisely classify and retrieve parameters. The improvement of temporal resolution makes it easier to record the temporal changes of land covers during different phases. However, due to the limitation of sensors, satellite data can not possess high spatial resolution, high temporal resolution and high spectral resolution at the same time, which greatly hinders further remote sensing applications.Remote sensing data fusion is one of the effective solutions to deal with the problem of limitation in sensors' resolutions, which is to integrate data from different sources and with different resolutions using algorithm methods to get richer information than one single image data. In recent years, the remote sensing data fusion methods have been greatly developed and related articles boost in growing numbers and importance. Thus, this article aims to systematically introduce the remote sensing data fusion and its current progress. Former reviews of remote sensing data fusion divides fusion methods into different categories. Based on processing levels, the remote sensing data fusion methods can be divided into pixel-level fusion, feature-level fusion and decision-level fusion. And based on the data sources, the remote sensing data fusion methods can be divided into homogeneous data fusion, heterogeneous data fusion, fusion for remote sensing observation and station data, and fusion for remote sensing observation and non-observed data. And this article adopts another category system which makes it more systematic and comprehensive in mathematical principles.In order to systematically indicate the current progress and developed history of remote sensing data fusion methods, this article first did bibliometric analysis on the remote sensing data fusion articles from data sources from Web of Science(WOS) and Chinese National Knowledge Infrastructure(CNKI). The data was preprocessed and has removed duplicates. Analysis including the aspects of yearly publishments, countries and organizations, journals and key words to see the history and trends have been done by Histcite, Bibliometric, Citespace and Endnote. Then the remote sensing fusion methods are systematically introduced. According to different resolution improvements, this article firstly divided the fusion methods into three categories: fusion methods emphasizing improvements of spatial resolution development,spectral development and temporal development, and based on that we further divide them into subcategories according to different mathematical principles. And the basic principles, some of the important methods, the advantages and flaws of these methods are systematically introduced in this section. Also, we did detailed instructions on fusion result assessment methods including methods with reference image and without reference image. Under these two categories we further introduced different assessment metrics and its applicable scenarios. And finally, this article made summaries of these fusion methods and described its features and directions. And finally makes predictions of future trends in remote sensing data fusion methods.
引文
Adams J B,Smith M O and Johnson P E.1986.Spectral mixture modeling:a new analysis of rock and soil types at the Viking Lander 1sites.Journal of Geophysical Research:Solid Earth,91(B8):8098-8112[DOI:10.1029/JB091iB08p08098]
Aiazzi B,Baronti S and Selva M.2007.Improving component substitution pansharpening through multivariate regression of MS+Pan data.IEEE Transactions on Geoscience and Remote Sensing,45(10):3230-3239[DOI:10.1109/tgrs.2007.901007]
Alparone L,Aiazzi B,Baronti S,Garzelli A,Nencini F and Selva M.2008.Multispectral and panchromatic data fusion assessment without reference.Photogrammetric Engineering and Remote Sensing,74(2):193-200[DOI:10.14358/pers.74.2.193]
Alparone L,Wald L,Chanussot J,Thomas C,Gamba P and Bruce L M.2007.Comparison of pansharpening algorithms:outcome of the2006 GRS-S data-fusion contest.IEEE Transactions on Geoscience and Remote Sensing,45(10):3012-3021[DOI:10.1109/TGRS.2007.904923]
Amorós-López J,Gómez-Chova L,Alonso L,Guanter L,Zurita-Milla R,Moreno J and Camps-Valls G.2013.Multitemporal fusion of Landsat/TM and ENVISAT/MERIS for crop monitoring.International Journal of Applied Earth Observation and Geoinformation,23:132-141[DOI:10.1016/j.jag.2012.12.004]
Azarang A and Ghassemian H.2017.A new pansharpening method using multi resolution analysis framework and deep neural networks//Proceedings of the 3rd International Conference on Pattern Recognition and Image Analysis.Shahrekord,Iran:IEEE:1-6[DOI:10.1109/PRIA.2017.7983017]
Bendoumi M A,He M Y and Mei S H.2014.Hyperspectral image resolution enhancement using high-resolution multispectral image based on spectral unmixing.IEEE Transactions on Geoscience and Remote Sensing,52(10):6574-6583[DOI:10.1109/tgrs.2014.2298056]
Chang C I.1999.Spectral information divergence for hyperspectral image analysis//Proceedings of 1999 International Geoscience and Remote Sensing Symposium.Hamburg,Germany:IEEE[DOI:10.1109/IGARSS.1999.773549]
Chavez P C Jr,Sides S C and Anderson J A.1991.Comparison of three different methods to merge multiresolution and multispectral data:Landsat TM and SPOT panchromatic.Photogrammetric Engineering and Remote Sensing,57(3):295-303
Chen Z,Pu H Y,Wang B and Jiang G M.2014.Fusion of hyperspectral and multispectral images:a novel framework based on generalization of pan-sharpening methods.IEEE Geoscience and Remote Sensing Letters,11(8):1418-1422[DOI:10.1109/LGRS.2013.2294476]
Chen Z X,Ren J Q,Tang H J,Shi Y,Leng P,Liu J,Wang L M,Wu WB,Yao Y M and Hasiyuya.2016.Progress and perspectives on agricultural remote sensing research and applications in China.Journal of Remote Sensing,20(5):748-767(陈仲新,任建强,唐华俊,史云,冷佩,刘佳,王利民,吴文斌,姚艳敏,哈斯图亚.2016.农业遥感研究应用进展与展望.遥感学报,20(5):748-767)[DOI:10.11834/jrs.20166214]
Choi M.2006.A new intensity-hue-saturation fusion approach to image fusion with a tradeoff parameter.IEEE Transactions on Geoscience and Remote Sensing,44(6):1672-1682[DOI:10.1109/tgrs.2006.869923]
Do M N and Vetterli M.2005.The contourlet transform:an efficient directional multiresolution image representation.IEEE Transactions on Image Processing,14(12):2091-2106[DOI:10.1109/TIP.2005.859376]
Eismann M T and Hardie R C.2003.Resolution enhancement of hyperspectral imagery using coincident panchromatic imagery and a stochastic mixing model//Proceedings of IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data.Greenbelt,MD,USA:IEEE:282-289[DOI:10.1109/WARSD.2003.1295206]
Eismann M T and Hardie R C.2005.Hyperspectral resolution enhancement using high-resolution multispectral imagery with arbitrary response functions.IEEE Transactions on Geoscience and Remote Sensing,43(3):455-465[DOI:10.1109/tgrs.2004.837324]
Elad M,Figueiredo M A T and Ma Y.2010.On the role of sparse and redundant representations in image processing.Proceedings of the IEEE,98(6):972-982[DOI:10.1109/jproc.2009.2037655]
Fu D J,Chen B Z,Wang J,Zhu X L and Hilker T.2013.An improved image fusion approach based on enhanced spatial and temporal the adaptive reflectance fusion model.Remote Sensing,5(12):6346-6360[DOI:10.3390/rs5126346]
Gao F,Masek J,Schwaller M and Hall F.2006.On the blending of the Landsat and MODIS surface reflectance:predicting daily Landsat surface reflectance.IEEE Transactions on Geoscience and Remote Sensing,44(8):2207-2218[DOI:10.1109/TGRS.2006.872081]
Ge Z R,Wang B and Zhang L M.2007.Remote sensing image fusion based on Bayesian linear estimation.Science in China Series F:Information Sciences,50(2):227-240[DOI:10.1007/s11432-007-0008-7]
Ghassemian H.2016.A review of remote sensing image fusion methods.Information Fusion,32:75-89[DOI:10.1016/j.inffus.2016.03.003]
Gross H N and Schott J R.1998.Application of spectral mixture analysis and image fusion techniques for image sharpening.Remote Sensing of Environment,63(2):85-94[DOI:10.1016/S0034-4257(97)00090-4]
Han C,Zhang H Y,Gao C X,Jiang C,Sang N and Zhang L P.2016.Aremote sensing image fusion method based on the analysis sparse model.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,9(1):439-453[DOI:10.1109/JSTARS.2015.2507859]
Hardie R C,Eismann M T and Wilson G L.2004.MAP estimation for hyperspectral image resolution enhancement using an auxiliary sensor.IEEE Transactions on Image Processing,13(9):1174-1184[DOI:10.1109/TIP.2004.829779]
Hilker T,Wulder M A,Coops N C,Linke J,McDermid G,Masek J G,Gao F and White J C.2009.A new data fusion model for high spatial-and temporal-resolution mapping of forest disturbance based on Landsat and MODIS.Remote Sensing of Environment,113(8):1613-1627[DOI:10.1016/j.rse.2009.03.007]
Huang B and Song H H.2012.Spatiotemporal reflectance fusion via sparse representation.IEEE Transactions on Geoscience and Remote Sensing,50(10):3707-3716[DOI:10.1109/TGRS.2012.2186638]
Huang B,Song H H,Cui H B,Peng J G and Xu Z B.2014.Spatial and spectral image fusion using sparse matrix factorization.IEEETransactions on Geoscience and Remote Sensing,52(3):1693-1704[DOI:10.1109/TGRS.2013.2253612]
Huang B,Wang J,Song H H,Fu D J and Wong K.2013.Generating high spatiotemporal resolution land surface temperature for urban heat island monitoring.IEEE Geoscience and Remote Sensing Letters,10(5):1011-1015[DOI:10.1109/LGRS.2012.2227930]
Huang W,Xiao L,Wei Z H,Liu H Y and Tang S Z.2015.A new pansharpening method with deep neural networks.IEEE Geoscience and Remote Sensing Letters,12(5):1037-1041[DOI:10.1109/LGRS.2014.2376034]
Huang Z,Yu X C,Wang G A and Wang Z N.2008.Application of several non-negative matrix factorization-based methods in remote sensing image fusion//Proceedings of the Fifth International Conference on Fuzzy Systems and Knowledge Discovery.Shandong,China:IEEE[DOI:10.1109/FSKD.2008.439]
Huynh-Thu Q and Ghanbari M.2008.Scope of validity of PSNR in image/video quality assessment.Electronics Letters,44(13):800-801[DOI:10.1049/el:20080522]
Jagalingam P and Hegde A V.2015.A review of quality metrics for fused image.Aquatic Procedia,4:133-142[DOI:10.1016/j.aqpro.2015.02.019]
Li D,Hao M F,Zhang J Q,Hu B and Lu Q Y.2012.A universal hypercomplex color image quality index//Proceedings of 2012 IEEE International Instrumentation and Measurement Technology Conference.Graz,Austria:IEEE:985-990[DOI:10.1109/I2MTC.2012.6229639]
Li S T and Yang B.2011.A new pan-sharpening method using a compressed sensing technique.IEEE Transactions on Geoscience and Remote Sensing,49(2):738-746[DOI:10.1109/TGRS.2010.2067219]
Liao C H,Wang J F,Pritchard I,Liu J G and Shang J L.2017.A spatio-temporal data fusion model for generating NDVI time series in heterogeneous regions.Remote Sensing,9(11):1125[DOI:10.3390/rs9111125]
Liao L M,Song J L,Wang J D,Xiao Z Q and Wang J.2016.Bayesian method for building frequent Landsat-like NDVI datasets by integrating MODIS and Landsat NDVI.Remote Sensing,8(6):452[DOI:10.3390/rs8060452]
Liu J G.2000.Smoothing filter-based intensity modulation:a spectral preserve image fusion technique for improving spatial details.International Journal of Remote Sensing,21(18):3461-3472[DOI:10.1080/014311600750037499]
Liu X,Deng C W,Wang S G,Huang G B,Zhao B J and Lauren P.2016.Fast and accurate spatiotemporal fusion based upon extreme learning machine.IEEE Geoscience and Remote Sensing Letters,13(12):2039-2043[DOI:10.1109/LGRS.2016.2622726]
Lu M,Chen J,Tang H J,Rao Y H,Yang P and Wu W B.2016.Land cover change detection by integrating object-based data blending model of Landsat and MODIS.Remote Sensing of Environment,184:374-386[DOI:10.1016/j.rse.2016.07.028]
Masi G,Cozzolino D,Verdoliva L and Scarpa G.2016.Pansharpening by convolutional neural networks.Remote Sensing,8(7):594[DOI:10.3390/rs8070594]
Moosavi V,Talebi A,Mokhtari M H,Shamsi S R F and Niazi Y.2015.A wavelet-artificial intelligence fusion approach(WAIFA)for blending Landsat and MODIS surface temperature.Remote Sensing of Environment,169:243-254[DOI:10.1016/j.rse.2015.08.015]
Nencini F,Garzelli A,Baronti S and Alparone L.2007.Remote sensing image fusion using the curvelet transform.Information Fusion,8(2):143-156[DOI:10.1016/j.inffus.2006.02.001]
Nunez J,Otazu X,Fors O,Prades A,PalàV and Arbiol R.1999.Multiresolution-based image fusion with additive wavelet decomposition.IEEE Transactions on Geoscience and Remote Sensing,37(3):1204-1211[DOI:10.1109/36.763274]
Palsson F,Sveinsson J R and Ulfarsson M O.2017.Multispectral and hyperspectral image fusion using a 3-D-convolutional neural network.IEEE Geoscience and Remote Sensing Letters,14(5):639-643[DOI:10.1109/LGRS.2017.2668299]
Pohl C and Van Genderen J L.1998.Review article Multisensor image fusion in remote sensing:concepts,methods and applications.International Journal of Remote Sensing,19(5):823-854[DOI:10.1080/014311698215748]
Schmitt M and Zhu X X.2016.Data fusion and remote sensing:an ever-growing relationship.IEEE Geoscience and Remote Sensing Magazine,4(4):6-23[DOI:10.1109/MGRS.2016.2561021]
Shettigara V K.1992.A generalized component substitution technique for spatial enhancement of multispectral images using a higher resolution data set.Photogrammetric Engineering and Remote Sensing,58(5):561-567
Song H H,Liu Q S,Wang G J,Hang R L and Huang B.2018.Spatiotemporal satellite image fusion using deep convolutional neural networks.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,11(3):821-829[DOI:10.1109/JSTARS.2018.2797894]
Sun X J,Zhang L F,Yang H,Wu T X,Cen Y and Guo Y.2015.Enhancement of spectral resolution for remotely sensed multispectral image.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,8(5):2198-2211[DOI:10.1109/jstars.2014.2356512]
Tan Z Y,Yue P,Di L P and Tang J M.2018.Deriving high spatiotemporal remote sensing images using deep convolutional network.Remote Sensing,10(7):1066[DOI:10.3390/rs10071066]
Tang H J,Wu W B,Yang P,Zhou Q B and Chen Z X.2010.Recent progresses in monitoring crop spatial patterns by using remote sensing technologies.Scientia Agricultura Sinica,43(14):2879-2888(唐华俊,吴文斌,杨鹏,周清波,陈仲新.2010.农作物空间格局遥感监测研究进展.中国农业科学,43(14):2879-2888)[DOI:10.3864/j.issn.0578-1752.2010.14.006]
Thomas C and Wald L.2006.Analysis of changes in quality assessment with scale//Proceedings of the 9th International Conference on Information Fusion.Florence,Italy:IEEE[DOI:10.1109/ICIF.2006.301595]
Tong Q X,Xue Y Q and Zhang L F.2014.Progress in hyperspectral remote sensing science and technology in China over the past three decades.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,7(1):70-91[DOI:10.1109/jstars.2013.2267204]
Tu T M,Lee Y C,Chang C P and Huang P S.2005.Adjustable intensity-hue-saturation and Brovey transform fusion technique for IKONOS/QuickBird imagery.Optical Engineering,44(11):116201[DOI:10.1117/1.2124871]
Tu T M,Su S C,Shyu H C and Huang P S.2001.A new look at IHS-like image fusion methods.Information Fusion,2(3):177-186[DOI:10.1016/s1566-2535(01)00036-7]
Wald L,Ranchin T,Mangolini M.1997.Fusion of satellite images of different spatial resolutions:assessing the quality of resulting images.Photogrammetric Engineering and Remote Sensing,63(6):691-699
Wang J and Huang B.2017.A rigorously-weighted spatiotemporal fusion model with uncertainty analysis.Remote Sensing,9(10):990[DOI:10.3390/rs9100990]
Wang J Y,Wang Y M and Li C L.2010.Noise model of hyperspectral imaging system and influence on radiation sensitivity.Journal of Remote Sensing,14(4):607-620(王建宇,王跃明,李春来.2010.高光谱成像系统的噪声模型和对辐射灵敏度的影响.遥感学报,14(4):607-620)
Wang Z,Bovik A C,Sheikh H R and Simoncelli E P.2004.Image quality assessment:from error visibility to structural similarity.IEEE Transactions on Image Processing,13(4):600-612[DOI:10.1109/TIP.2003.819861]
Wei J B,Wang L Z,Liu P,Chen X D,Li W and Zomaya A Y.2017a.Spatiotemporal fusion of MODIS and Landsat-7 reflectance images via compressed sensing.IEEE Transactions on Geoscience and Remote Sensing,55(12):7126-7139[DOI:10.1109/TGRS.2017.2742529]
Wei J B,Wang L Z,Liu P and Song W J.2017b.Spatiotemporal fusion of remote sensing images with structural sparsity and semicoupled dictionary learning.Remote Sensing,9(1):21[DOI:10.3390/rs9010021]
Wei Q,Bioucas-Dias J,Dobigeon N and Tourneret J Y.2015.Hyperspectral and multispectral image fusion based on a sparse representation.IEEE Transactions on Geoscience and Remote Sensing,53(7):3658-3668[DOI:10.1109/TGRS.2014.2381272]
Wei Q,Dobigeon N and Tourneret J Y.2014a.Bayesian fusion of hyperspectral and multispectral images//Proceedings of 2014 IEEEInternational Conference on Acoustics,Speech and Signal Processing.Florence,Italy:IEEE:3176-3180[DOI:10.1109/ICAS-SP.2014.6854186]
Wei Q,Dobigeon N and Tourneret J Y.2014b.Bayesian fusion of multispectral and hyperspectral images with unknown sensor spectral response//Proceedings of 2014 IEEE International Conference on Image Processing.Paris,France:IEEE:698-702[DOI:10.1109/ICIP.2014.7025140]
Wei Y C,Yuan Q Q,Shen H F and Zhang L P.2017.Boosting the accuracy of multispectral image pansharpening by learning a deep residual network.IEEE Geoscience and Remote Sensing Letters,14(10):1795-1799[DOI:10.1109/LGRS.2017.2736020]
Weng Q H,Fu P and Gao F.2014.Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data.Remote Sensing of Environment,145:55-67[DOI:10.1016/j.rse.2014.02.003]
Wu B,Huang B and Zhang L P.2015.An error-bound-regularized sparse coding for spatiotemporal reflectance fusion.IEEE Transactions on Geoscience and Remote Sensing,53(12):6791-6803[DOI:10.1109/TGRS.2015.2448100]
Wu M Q,Huang W J,Niu Z and Wang C Y.2015.Generating daily synthetic Landsat imagery by combining Landsat and MODISdata.Sensors,15(9):24002-24025[DOI:10.3390/s150924002]
Wu M Q,Niu Z,Wang C Y,Wu C Y and Wang L.2012.Use of MOD-IS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model.Journal of Applied Remote Sensing,6(1):063507[DOI:10.1117/1.JRS.6.063507]
Wu P H,Shen H F,Zhang L P and G?ttsche F M.2015.Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature.Remote Sensing of Environment,156:169-181[DOI:10.1016/j.rse.2014.09.013]
Xue J,Leung Y and Fung T.2017.A Bayesian data fusion approach to spatio-temporal fusion of remotely sensed images.Remote Sensing,9(12):1310[DOI:10.3390/rs9121310]
Yokoya N,Yairi T and Iwasaki A.2012.Coupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion.IEEE Transactions on Geoscience and Remote Sensing,50(2):528-537[DOI:10.1109/tgrs.2011.2161320]
Yuan Q Q,Wei Y C,Meng X C,Shen H F and Zhang L P.2018.Amultiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,11(3):978-989[DOI:10.1109/JSTARS.2018.2794888]
Yusuf Y,Sumantyo J T S and Kuze H.2013.Spectral information analysis of image fusion data for remote sensing applications.Geocarto International,28(4):291-310[DOI:10.1080/10106049.2012.692396]
Zhang L P and Shen H F.2016.Progress and future of remote sensing data fusion.Journal of Remote Sensing,20(5):1050-1061(张良培,沈焕锋.2016.遥感数据融合的进展与前瞻.遥感学报,20(5):1050-1061)[DOI:10.11834/jrs.20166243]
Zhang W,Li A N,Jin H A,Bian J H,Zhang Z J,Lei G B,Qin Z H and Huang C Q.2013.An enhanced spatial and temporal data fusion model for fusing Landsat and MODIS surface reflectance to generate high temporal Landsat-like data.Remote Sensing,5(10):5346-5368[DOI:10.3390/rs5105346]
Zhang Y F.2012.Wavelet-based Bayesian fusion of multispectral and hyperspectral images using Gaussian scale mixture model.International Journal of Image and Data Fusion,3(1):23-37[DOI:10.1080/19479832.2010.551522]
Zhang Y F,De Backer S and Scheunders P.2009.Noise-resistant wavelet-based Bayesian fusion of multispectral and hyperspectral images.IEEE Transactions on Geoscience and Remote Sensing,47(11):3834-3843[DOI:10.1109/tgrs.2009.2017737]
Zhu X L,Cai F Y,Tian J Q and Williams T K A.2018.Spatiotemporal fusion of multisource remote sensing data:literature survey,taxonomy,principles,applications,and future directions.Remote Sensing,10(4):527[DOI:10.3390/rs10040527]
Zhu X L,Chen J,Gao F,Chen X H and Masek J G.2010.An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions.Remote Sensing of Environment,114(11):2610-2623[DOI:10.1016/j.rse.2010.05.032]
Zhukov B,Oertel D,Lanzl F and Reinhackel G.1999.Unmixing-based multisensor multiresolution image fusion.IEEE Transactions on Geoscience and Remote Sensing,37(3):1212-1226[DOI:10.1109/36.763276]
Zurita-Milla R,Clevers J G P W and Schaepman M E.2008.Unmixing-based Landsat TM and MERIS FR data fusion.IEEEGeoscience and Remote Sensing Letters,5(3):453-457[DOI:10.1109/lgrs.2008.919685]
Aiazzi B,Baronti S and Selva M.2007.Improving component substitution pansharpening through multivariate regression of MS+Pan data.IEEE Transactions on Geoscience and Remote Sensing,45(10):3230-3239[DOI:10.1109/tgrs.2007.901007]
Alparone L,Aiazzi B,Baronti S,Garzelli A,Nencini F and Selva M.2008.Multispectral and panchromatic data fusion assessment without reference.Photogrammetric Engineering and Remote Sensing,74(2):193-200[DOI:10.14358/pers.74.2.193]
Alparone L,Wald L,Chanussot J,Thomas C,Gamba P and Bruce L M.2007.Comparison of pansharpening algorithms:outcome of the2006 GRS-S data-fusion contest.IEEE Transactions on Geoscience and Remote Sensing,45(10):3012-3021[DOI:10.1109/TGRS.2007.904923]
Amorós-López J,Gómez-Chova L,Alonso L,Guanter L,Zurita-Milla R,Moreno J and Camps-Valls G.2013.Multitemporal fusion of Landsat/TM and ENVISAT/MERIS for crop monitoring.International Journal of Applied Earth Observation and Geoinformation,23:132-141[DOI:10.1016/j.jag.2012.12.004]
Azarang A and Ghassemian H.2017.A new pansharpening method using multi resolution analysis framework and deep neural networks//Proceedings of the 3rd International Conference on Pattern Recognition and Image Analysis.Shahrekord,Iran:IEEE:1-6[DOI:10.1109/PRIA.2017.7983017]
Bendoumi M A,He M Y and Mei S H.2014.Hyperspectral image resolution enhancement using high-resolution multispectral image based on spectral unmixing.IEEE Transactions on Geoscience and Remote Sensing,52(10):6574-6583[DOI:10.1109/tgrs.2014.2298056]
Chang C I.1999.Spectral information divergence for hyperspectral image analysis//Proceedings of 1999 International Geoscience and Remote Sensing Symposium.Hamburg,Germany:IEEE[DOI:10.1109/IGARSS.1999.773549]
Chavez P C Jr,Sides S C and Anderson J A.1991.Comparison of three different methods to merge multiresolution and multispectral data:Landsat TM and SPOT panchromatic.Photogrammetric Engineering and Remote Sensing,57(3):295-303
Chen Z,Pu H Y,Wang B and Jiang G M.2014.Fusion of hyperspectral and multispectral images:a novel framework based on generalization of pan-sharpening methods.IEEE Geoscience and Remote Sensing Letters,11(8):1418-1422[DOI:10.1109/LGRS.2013.2294476]
Chen Z X,Ren J Q,Tang H J,Shi Y,Leng P,Liu J,Wang L M,Wu WB,Yao Y M and Hasiyuya.2016.Progress and perspectives on agricultural remote sensing research and applications in China.Journal of Remote Sensing,20(5):748-767(陈仲新,任建强,唐华俊,史云,冷佩,刘佳,王利民,吴文斌,姚艳敏,哈斯图亚.2016.农业遥感研究应用进展与展望.遥感学报,20(5):748-767)[DOI:10.11834/jrs.20166214]
Choi M.2006.A new intensity-hue-saturation fusion approach to image fusion with a tradeoff parameter.IEEE Transactions on Geoscience and Remote Sensing,44(6):1672-1682[DOI:10.1109/tgrs.2006.869923]
Do M N and Vetterli M.2005.The contourlet transform:an efficient directional multiresolution image representation.IEEE Transactions on Image Processing,14(12):2091-2106[DOI:10.1109/TIP.2005.859376]
Eismann M T and Hardie R C.2003.Resolution enhancement of hyperspectral imagery using coincident panchromatic imagery and a stochastic mixing model//Proceedings of IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data.Greenbelt,MD,USA:IEEE:282-289[DOI:10.1109/WARSD.2003.1295206]
Eismann M T and Hardie R C.2005.Hyperspectral resolution enhancement using high-resolution multispectral imagery with arbitrary response functions.IEEE Transactions on Geoscience and Remote Sensing,43(3):455-465[DOI:10.1109/tgrs.2004.837324]
Elad M,Figueiredo M A T and Ma Y.2010.On the role of sparse and redundant representations in image processing.Proceedings of the IEEE,98(6):972-982[DOI:10.1109/jproc.2009.2037655]
Fu D J,Chen B Z,Wang J,Zhu X L and Hilker T.2013.An improved image fusion approach based on enhanced spatial and temporal the adaptive reflectance fusion model.Remote Sensing,5(12):6346-6360[DOI:10.3390/rs5126346]
Gao F,Masek J,Schwaller M and Hall F.2006.On the blending of the Landsat and MODIS surface reflectance:predicting daily Landsat surface reflectance.IEEE Transactions on Geoscience and Remote Sensing,44(8):2207-2218[DOI:10.1109/TGRS.2006.872081]
Ge Z R,Wang B and Zhang L M.2007.Remote sensing image fusion based on Bayesian linear estimation.Science in China Series F:Information Sciences,50(2):227-240[DOI:10.1007/s11432-007-0008-7]
Ghassemian H.2016.A review of remote sensing image fusion methods.Information Fusion,32:75-89[DOI:10.1016/j.inffus.2016.03.003]
Gross H N and Schott J R.1998.Application of spectral mixture analysis and image fusion techniques for image sharpening.Remote Sensing of Environment,63(2):85-94[DOI:10.1016/S0034-4257(97)00090-4]
Han C,Zhang H Y,Gao C X,Jiang C,Sang N and Zhang L P.2016.Aremote sensing image fusion method based on the analysis sparse model.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,9(1):439-453[DOI:10.1109/JSTARS.2015.2507859]
Hardie R C,Eismann M T and Wilson G L.2004.MAP estimation for hyperspectral image resolution enhancement using an auxiliary sensor.IEEE Transactions on Image Processing,13(9):1174-1184[DOI:10.1109/TIP.2004.829779]
Hilker T,Wulder M A,Coops N C,Linke J,McDermid G,Masek J G,Gao F and White J C.2009.A new data fusion model for high spatial-and temporal-resolution mapping of forest disturbance based on Landsat and MODIS.Remote Sensing of Environment,113(8):1613-1627[DOI:10.1016/j.rse.2009.03.007]
Huang B and Song H H.2012.Spatiotemporal reflectance fusion via sparse representation.IEEE Transactions on Geoscience and Remote Sensing,50(10):3707-3716[DOI:10.1109/TGRS.2012.2186638]
Huang B,Song H H,Cui H B,Peng J G and Xu Z B.2014.Spatial and spectral image fusion using sparse matrix factorization.IEEETransactions on Geoscience and Remote Sensing,52(3):1693-1704[DOI:10.1109/TGRS.2013.2253612]
Huang B,Wang J,Song H H,Fu D J and Wong K.2013.Generating high spatiotemporal resolution land surface temperature for urban heat island monitoring.IEEE Geoscience and Remote Sensing Letters,10(5):1011-1015[DOI:10.1109/LGRS.2012.2227930]
Huang W,Xiao L,Wei Z H,Liu H Y and Tang S Z.2015.A new pansharpening method with deep neural networks.IEEE Geoscience and Remote Sensing Letters,12(5):1037-1041[DOI:10.1109/LGRS.2014.2376034]
Huang Z,Yu X C,Wang G A and Wang Z N.2008.Application of several non-negative matrix factorization-based methods in remote sensing image fusion//Proceedings of the Fifth International Conference on Fuzzy Systems and Knowledge Discovery.Shandong,China:IEEE[DOI:10.1109/FSKD.2008.439]
Huynh-Thu Q and Ghanbari M.2008.Scope of validity of PSNR in image/video quality assessment.Electronics Letters,44(13):800-801[DOI:10.1049/el:20080522]
Jagalingam P and Hegde A V.2015.A review of quality metrics for fused image.Aquatic Procedia,4:133-142[DOI:10.1016/j.aqpro.2015.02.019]
Li D,Hao M F,Zhang J Q,Hu B and Lu Q Y.2012.A universal hypercomplex color image quality index//Proceedings of 2012 IEEE International Instrumentation and Measurement Technology Conference.Graz,Austria:IEEE:985-990[DOI:10.1109/I2MTC.2012.6229639]
Li S T and Yang B.2011.A new pan-sharpening method using a compressed sensing technique.IEEE Transactions on Geoscience and Remote Sensing,49(2):738-746[DOI:10.1109/TGRS.2010.2067219]
Liao C H,Wang J F,Pritchard I,Liu J G and Shang J L.2017.A spatio-temporal data fusion model for generating NDVI time series in heterogeneous regions.Remote Sensing,9(11):1125[DOI:10.3390/rs9111125]
Liao L M,Song J L,Wang J D,Xiao Z Q and Wang J.2016.Bayesian method for building frequent Landsat-like NDVI datasets by integrating MODIS and Landsat NDVI.Remote Sensing,8(6):452[DOI:10.3390/rs8060452]
Liu J G.2000.Smoothing filter-based intensity modulation:a spectral preserve image fusion technique for improving spatial details.International Journal of Remote Sensing,21(18):3461-3472[DOI:10.1080/014311600750037499]
Liu X,Deng C W,Wang S G,Huang G B,Zhao B J and Lauren P.2016.Fast and accurate spatiotemporal fusion based upon extreme learning machine.IEEE Geoscience and Remote Sensing Letters,13(12):2039-2043[DOI:10.1109/LGRS.2016.2622726]
Lu M,Chen J,Tang H J,Rao Y H,Yang P and Wu W B.2016.Land cover change detection by integrating object-based data blending model of Landsat and MODIS.Remote Sensing of Environment,184:374-386[DOI:10.1016/j.rse.2016.07.028]
Masi G,Cozzolino D,Verdoliva L and Scarpa G.2016.Pansharpening by convolutional neural networks.Remote Sensing,8(7):594[DOI:10.3390/rs8070594]
Moosavi V,Talebi A,Mokhtari M H,Shamsi S R F and Niazi Y.2015.A wavelet-artificial intelligence fusion approach(WAIFA)for blending Landsat and MODIS surface temperature.Remote Sensing of Environment,169:243-254[DOI:10.1016/j.rse.2015.08.015]
Nencini F,Garzelli A,Baronti S and Alparone L.2007.Remote sensing image fusion using the curvelet transform.Information Fusion,8(2):143-156[DOI:10.1016/j.inffus.2006.02.001]
Nunez J,Otazu X,Fors O,Prades A,PalàV and Arbiol R.1999.Multiresolution-based image fusion with additive wavelet decomposition.IEEE Transactions on Geoscience and Remote Sensing,37(3):1204-1211[DOI:10.1109/36.763274]
Palsson F,Sveinsson J R and Ulfarsson M O.2017.Multispectral and hyperspectral image fusion using a 3-D-convolutional neural network.IEEE Geoscience and Remote Sensing Letters,14(5):639-643[DOI:10.1109/LGRS.2017.2668299]
Pohl C and Van Genderen J L.1998.Review article Multisensor image fusion in remote sensing:concepts,methods and applications.International Journal of Remote Sensing,19(5):823-854[DOI:10.1080/014311698215748]
Schmitt M and Zhu X X.2016.Data fusion and remote sensing:an ever-growing relationship.IEEE Geoscience and Remote Sensing Magazine,4(4):6-23[DOI:10.1109/MGRS.2016.2561021]
Shettigara V K.1992.A generalized component substitution technique for spatial enhancement of multispectral images using a higher resolution data set.Photogrammetric Engineering and Remote Sensing,58(5):561-567
Song H H,Liu Q S,Wang G J,Hang R L and Huang B.2018.Spatiotemporal satellite image fusion using deep convolutional neural networks.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,11(3):821-829[DOI:10.1109/JSTARS.2018.2797894]
Sun X J,Zhang L F,Yang H,Wu T X,Cen Y and Guo Y.2015.Enhancement of spectral resolution for remotely sensed multispectral image.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,8(5):2198-2211[DOI:10.1109/jstars.2014.2356512]
Tan Z Y,Yue P,Di L P and Tang J M.2018.Deriving high spatiotemporal remote sensing images using deep convolutional network.Remote Sensing,10(7):1066[DOI:10.3390/rs10071066]
Tang H J,Wu W B,Yang P,Zhou Q B and Chen Z X.2010.Recent progresses in monitoring crop spatial patterns by using remote sensing technologies.Scientia Agricultura Sinica,43(14):2879-2888(唐华俊,吴文斌,杨鹏,周清波,陈仲新.2010.农作物空间格局遥感监测研究进展.中国农业科学,43(14):2879-2888)[DOI:10.3864/j.issn.0578-1752.2010.14.006]
Thomas C and Wald L.2006.Analysis of changes in quality assessment with scale//Proceedings of the 9th International Conference on Information Fusion.Florence,Italy:IEEE[DOI:10.1109/ICIF.2006.301595]
Tong Q X,Xue Y Q and Zhang L F.2014.Progress in hyperspectral remote sensing science and technology in China over the past three decades.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,7(1):70-91[DOI:10.1109/jstars.2013.2267204]
Tu T M,Lee Y C,Chang C P and Huang P S.2005.Adjustable intensity-hue-saturation and Brovey transform fusion technique for IKONOS/QuickBird imagery.Optical Engineering,44(11):116201[DOI:10.1117/1.2124871]
Tu T M,Su S C,Shyu H C and Huang P S.2001.A new look at IHS-like image fusion methods.Information Fusion,2(3):177-186[DOI:10.1016/s1566-2535(01)00036-7]
Wald L,Ranchin T,Mangolini M.1997.Fusion of satellite images of different spatial resolutions:assessing the quality of resulting images.Photogrammetric Engineering and Remote Sensing,63(6):691-699
Wang J and Huang B.2017.A rigorously-weighted spatiotemporal fusion model with uncertainty analysis.Remote Sensing,9(10):990[DOI:10.3390/rs9100990]
Wang J Y,Wang Y M and Li C L.2010.Noise model of hyperspectral imaging system and influence on radiation sensitivity.Journal of Remote Sensing,14(4):607-620(王建宇,王跃明,李春来.2010.高光谱成像系统的噪声模型和对辐射灵敏度的影响.遥感学报,14(4):607-620)
Wang Z,Bovik A C,Sheikh H R and Simoncelli E P.2004.Image quality assessment:from error visibility to structural similarity.IEEE Transactions on Image Processing,13(4):600-612[DOI:10.1109/TIP.2003.819861]
Wei J B,Wang L Z,Liu P,Chen X D,Li W and Zomaya A Y.2017a.Spatiotemporal fusion of MODIS and Landsat-7 reflectance images via compressed sensing.IEEE Transactions on Geoscience and Remote Sensing,55(12):7126-7139[DOI:10.1109/TGRS.2017.2742529]
Wei J B,Wang L Z,Liu P and Song W J.2017b.Spatiotemporal fusion of remote sensing images with structural sparsity and semicoupled dictionary learning.Remote Sensing,9(1):21[DOI:10.3390/rs9010021]
Wei Q,Bioucas-Dias J,Dobigeon N and Tourneret J Y.2015.Hyperspectral and multispectral image fusion based on a sparse representation.IEEE Transactions on Geoscience and Remote Sensing,53(7):3658-3668[DOI:10.1109/TGRS.2014.2381272]
Wei Q,Dobigeon N and Tourneret J Y.2014a.Bayesian fusion of hyperspectral and multispectral images//Proceedings of 2014 IEEEInternational Conference on Acoustics,Speech and Signal Processing.Florence,Italy:IEEE:3176-3180[DOI:10.1109/ICAS-SP.2014.6854186]
Wei Q,Dobigeon N and Tourneret J Y.2014b.Bayesian fusion of multispectral and hyperspectral images with unknown sensor spectral response//Proceedings of 2014 IEEE International Conference on Image Processing.Paris,France:IEEE:698-702[DOI:10.1109/ICIP.2014.7025140]
Wei Y C,Yuan Q Q,Shen H F and Zhang L P.2017.Boosting the accuracy of multispectral image pansharpening by learning a deep residual network.IEEE Geoscience and Remote Sensing Letters,14(10):1795-1799[DOI:10.1109/LGRS.2017.2736020]
Weng Q H,Fu P and Gao F.2014.Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data.Remote Sensing of Environment,145:55-67[DOI:10.1016/j.rse.2014.02.003]
Wu B,Huang B and Zhang L P.2015.An error-bound-regularized sparse coding for spatiotemporal reflectance fusion.IEEE Transactions on Geoscience and Remote Sensing,53(12):6791-6803[DOI:10.1109/TGRS.2015.2448100]
Wu M Q,Huang W J,Niu Z and Wang C Y.2015.Generating daily synthetic Landsat imagery by combining Landsat and MODISdata.Sensors,15(9):24002-24025[DOI:10.3390/s150924002]
Wu M Q,Niu Z,Wang C Y,Wu C Y and Wang L.2012.Use of MOD-IS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model.Journal of Applied Remote Sensing,6(1):063507[DOI:10.1117/1.JRS.6.063507]
Wu P H,Shen H F,Zhang L P and G?ttsche F M.2015.Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature.Remote Sensing of Environment,156:169-181[DOI:10.1016/j.rse.2014.09.013]
Xue J,Leung Y and Fung T.2017.A Bayesian data fusion approach to spatio-temporal fusion of remotely sensed images.Remote Sensing,9(12):1310[DOI:10.3390/rs9121310]
Yokoya N,Yairi T and Iwasaki A.2012.Coupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion.IEEE Transactions on Geoscience and Remote Sensing,50(2):528-537[DOI:10.1109/tgrs.2011.2161320]
Yuan Q Q,Wei Y C,Meng X C,Shen H F and Zhang L P.2018.Amultiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,11(3):978-989[DOI:10.1109/JSTARS.2018.2794888]
Yusuf Y,Sumantyo J T S and Kuze H.2013.Spectral information analysis of image fusion data for remote sensing applications.Geocarto International,28(4):291-310[DOI:10.1080/10106049.2012.692396]
Zhang L P and Shen H F.2016.Progress and future of remote sensing data fusion.Journal of Remote Sensing,20(5):1050-1061(张良培,沈焕锋.2016.遥感数据融合的进展与前瞻.遥感学报,20(5):1050-1061)[DOI:10.11834/jrs.20166243]
Zhang W,Li A N,Jin H A,Bian J H,Zhang Z J,Lei G B,Qin Z H and Huang C Q.2013.An enhanced spatial and temporal data fusion model for fusing Landsat and MODIS surface reflectance to generate high temporal Landsat-like data.Remote Sensing,5(10):5346-5368[DOI:10.3390/rs5105346]
Zhang Y F.2012.Wavelet-based Bayesian fusion of multispectral and hyperspectral images using Gaussian scale mixture model.International Journal of Image and Data Fusion,3(1):23-37[DOI:10.1080/19479832.2010.551522]
Zhang Y F,De Backer S and Scheunders P.2009.Noise-resistant wavelet-based Bayesian fusion of multispectral and hyperspectral images.IEEE Transactions on Geoscience and Remote Sensing,47(11):3834-3843[DOI:10.1109/tgrs.2009.2017737]
Zhu X L,Cai F Y,Tian J Q and Williams T K A.2018.Spatiotemporal fusion of multisource remote sensing data:literature survey,taxonomy,principles,applications,and future directions.Remote Sensing,10(4):527[DOI:10.3390/rs10040527]
Zhu X L,Chen J,Gao F,Chen X H and Masek J G.2010.An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions.Remote Sensing of Environment,114(11):2610-2623[DOI:10.1016/j.rse.2010.05.032]
Zhukov B,Oertel D,Lanzl F and Reinhackel G.1999.Unmixing-based multisensor multiresolution image fusion.IEEE Transactions on Geoscience and Remote Sensing,37(3):1212-1226[DOI:10.1109/36.763276]
Zurita-Milla R,Clevers J G P W and Schaepman M E.2008.Unmixing-based Landsat TM and MERIS FR data fusion.IEEEGeoscience and Remote Sensing Letters,5(3):453-457[DOI:10.1109/lgrs.2008.919685]
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