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李树涛，李聪妤，康旭东.2021.多源遥感图像融合发展现状与未来展望.遥感学报，25（1）： 148-166
Li S T，Li C Y and Kang X D. 2021. Development status and future prospects of multi-source remote sensing image fusion. National Remote Sensing Bulletin， 25（1）：148-166
近年来，随着遥感技术的发展，高光谱、红外、雷达等多源遥感成像手段在精准农业、资源调查、环境监测、军事国防等重要领域发挥着越来越重要的作用。同一场景多源遥感图像观测的地物对象相同，但观测的维度不同，图像的空间、光谱与时间分辨率存在差异，提供的信息既具有冗余性，又具有互补性和合作性。多源遥感图像融合能够综合利用不同来源获取的遥感图像信息，实现更精准、更全面的对地观测，是遥感对地观测领域的核心关键技术。本文从多源遥感图像的数据来源出发，综述了多源遥感图像融合的研究现状与未来发展趋势：首先介绍了国内外现有多源遥感图像的主要来源、图像特性与典型应用；然后，对不同类型多源遥感图像融合的研究现状和挑战性难题进行了归纳和总结；最后，对多源遥感图像融合的未来发展趋势进行了展望。
The development of multispectral
hyperspectral
infrared
radar
and other sensing technologies in recent years has facilitated the use of remote sensing methods in precision agriculture
resource investigation
environmental monitoring
military defense
and other fields. Multi-source remote sensing images in the same scene can capture the same ground objects
while the dimensions of the observations are independent of each other. Therefore
the imaging scale
spatial resolution
time resolution
and target characteristics may be quite different in different observations. The information provided by massive multi-source remote sensing data is redundant
complementary
and cooperative. Multi-source remote sensing image fusion can utilize the complementary information obtained from different sources to achieve accurate and comprehensive Earth observations. Thus
it is one of the key technologies in remote sensing.
From the perspective of data sources
this review summarizes the research status and future development trends of multi-source remote sensing image fusion. In the introduction
the importance of multi-source image fusion and the motivation of this review are illustrated briefly. The second section outlines the main sources and image characteristics of nine typical remote sensing data: panchromatic images
multispectral images
hyperspectral images
infrared images
nighttime light images
stereo images
video images
Synthetic Aperture Radar (SAR) images
and light detection and ranging (LiDAR) images. The typical applications of these multi-source data are also briefly concluded while introducing the characteristics of these multi-source remote sensing images separately. Moreover
the development trend of multi-source remote sensing image fusion is evaluated according to the number of publications. In the third section
latest studies on multi-source remote sensing image fusion are introduced in detail in the order of optical image fusion
optical and SAR image fusion
optical and LiDAR image fusion
and other types of remote sensing image fusion. The third section also puts forward some challenging problems in remote sensing image fusion. For example
the registration problem of multi-source images
the application problem of fusion in specific domain
and the representation of features during cross-modal fusion are all important problems that need to be solved urgently. In the conclusion section
this review summarizes the research status of the multi-source remote sensing image fusion field. This also section prospects the future development trend of multi-source remote sensing image fusion.
First
the study of related fusion technologies for new types of remote sensing images will be a major future research. Second
the integration of data acquisition and image fusion techniques can reduce the difficulty and improve the performance of image fusion with the help of novel hardware designs. Therefore
multi-modal fusion-based computational imaging systems should be designed. Third
fusing multi-source images with other types of data
such as geographical
ground station
and web data
is an interesting research topic in addition to the fusion of remote sensing images. Finally
evaluating the performance of image fusion is an important problem. Image fusion aims to help better understand the land covers from different dimensions of Earth observations. Whether the fusion can help the understanding of the Earth is unclear. Therefore
the improvement in application performance
such as detection or classification accuracy
may be an important index compared with the enhancement in the quality of the fused image.
遥感图像图像融合多模态对地观测
remote sensing imageimage fusionmulti-modalground observation
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张国亮, 朱瑞飞, 杜一博, 曲春梅, 李贝贝. 2020. 吉林一号高分辨率夜光遥感影像在城市监测中的应用. 卫星应用, (3): 27-33
Zhang H, Shen H F and Zhang L P. 2016. Fusion of multispectral and SAR images using sparse representation//Proceedings of 2016 IEEE International Geoscience and Remote Sensing Symposium. Beijing, China: IEEE: 7200-7203 [DOI: 10.1109/IGARSS.2016.7730878http://dx.doi.org/10.1109/IGARSS.2016.7730878]
Zhang J Y, Ma Y, Zhang Z and Liang J. 2015. Research on inversion method of deep stereoscopic perspective image of shallow sea water of islands and reefs based on decision fusion//Proceedings of Academic Papers of Chinese Ocean Society in 2015. Beijing: Chinese Society for Oceanography: 111-119
张靖宇, 马毅, 张震, 梁建. 2015. 基于决策融合的海岛礁浅海水深立体遥感影像反演方法研究//“一带一路”战略与海洋科技创新——中国海洋学会2015年学术论文集. 北京: 中国海洋学会: 111-119
Zhang K, Wang M and Yang S Y. 2017. Multispectral and hyperspectral image fusion based on group spectral embedding and low-rank factorization. IEEE Transactions on Geoscience and Remote Sensing, 55(3): 1363-1371 [DOI: 10.1109/TGRS.2016.2623626http://dx.doi.org/10.1109/TGRS.2016.2623626]
Zhang L F, Peng M Y, Sun X J, Cen Y and Tong Q X. 2019. Progress and bibliometric analysis of remote sensing data fusion methods (1992—2018). Journal of Remote Sensing, 23(4): 603-619
张立福, 彭明媛, 孙雪剑, 岑奕, 童庆禧. 2019. 遥感数据融合研究进展与文献定量分析(1992—2018). 遥感学报, 23(4): 603-619[DOI: 10.11834/jrs.20199073http://dx.doi.org/10.11834/jrs.20199073]
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.20166243http://dx.doi.org/10.11834/jrs.20166243].
Zhang M and Zeng Y N. 2018. Net primary production estimation by using fusion remote sensing data with high spatial and temporal resolution. Journal of Remote Sensing, 22(1): 143-152
张猛, 曾永年. 2018. 融合高时空分辨率数据估算植被净初级生产力. 遥感学报, 22(1): 143-152 [DOI: 10.11834/jrs.20186499http://dx.doi.org/10.11834/jrs.20186499]
Zhang Y and Hong G. 2005. An IHS and wavelet integrated approach to improve pan-sharpening visual quality of natural colour IKONOS and QuickBird images. Information Fusion, 6(3): 225-234 [DOI: 10.1016/j.inffus.2004.06.009http://dx.doi.org/10.1016/j.inffus.2004.06.009]
Zhang Y J, Zheng M T, Xiong J X, Lu Y H and Xiong X D. 2014. On-Orbit geometric calibration of ZY-3 three-line array imagery with multistrip data sets. IEEE Transactions on Geoscience and Remote Sensing, 52(1): 224-234 [DOI: 10.1109/TGRS.2013.2237781http://dx.doi.org/10.1109/TGRS.2013.2237781]
Zhong Y F, Cao Q, Zhao J, Ma A L, Zhao B and Zhang L P. 2017. Optimal decision fusion for urban land-use/land-cover classification based on adaptive differential evolution using hyperspectral and LiDAR data. Remote Sensing, 9(8): 868 [DOI: 10.3390/rs9080868http://dx.doi.org/10.3390/rs9080868]
Zhu X L, Helmer E H, Gao F, Liu D S, Chen J and Lefsky M A. 2016. A flexible spatiotemporal method for fusing satellite images with different resolutions. Remote Sensing of Environment, 172: 165-177 [DOI: 10.1016/j.rse.2015.11.016http://dx.doi.org/10.1016/j.rse.2015.11.016]
Zhu X X and Bamler R. 2013. A sparse image fusion algorithm with application to pan-sharpening. IEEE Transactions on Geoscience and Remote Sensing, 51(5): 2827-2836 [DOI: 10.1109/TGRS.2012.2213604http://dx.doi.org/10.1109/TGRS.2012.2213604]
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