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纸质出版日期： 2020-04-07 ，
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孟祥超,孙伟伟,任凯,杨刚,邵枫,符冉迪.2020.基于多分辨率分析的GF-5和GF-1遥感影像空—谱融合.遥感学报,24(4): 379-387
MENG Xiangchao,SUN Weiwei,REN Kai,YANG Gang,SHAO Feng,FU Randi. 2020. Spatial-spectral fusion of GF-5/GF-1 remote sensing images based on multiresolution analysis. Journal of Remote Sensing(Chinese). 24(4): 379-387
针对空间分辨率比率较大尺度差异下的高分五号(GF-5)与高分一号（GF-1）卫星影像的空—谱融合问题，提出多传感器影像融合策略：一方面，通过现有空—谱融合方法的分步融合得到融合影像；另一方面，在分步融合理论基础上，推导得出一体化融合基础框架，并进一步提出基于多分辨率分析的多传感器一体化融合方法，缓解现有方法因空间分辨率比率过大导致影像空、谱互补信息难以有效集成的问题。其中，提出的一体化融合方法基于调制传递函数MTF(Modulation Transfer Function)滤波对多传感器影像空间（高频）和光谱（低频）分量进行分解提取，并充分考虑多传感器高空间分辨率影像与高光谱分辨率影像之间的关系，以及高光谱分辨率影像波段间关系，设计合理的融合权重，最终可得到具有最高空间分辨率和最高光谱分辨率的融合影像。通过GF-1全色影像、GF-1多光谱影像、GF-5高光谱影像数据对提出方法进行实验验证，结果表明：本文方法可有效集成多传感器影像间的空、谱互补信息，得到较优融合结果。
This study proposed a multisensor image fusion solution for the GF-5/GF-1 spatial-spectral fusion with large spatial resolution ratio. We aimed to obtain the fused image through step-by-step fusion of multisensor remote sensing images. A unified fusion framework for multisensor image fusion was derived on the basis of step-by-step fusion theory. An integrated multisensor image fusion method based on multiresolution analysis theory was proposed in accordance with the unified framework. The proposed method can overcome the difficulty of integrating complementary high spatial and spectral information of multisource images under high spatial resolution ratio. In the proposed method
a modulation transfer function was applied to separate the spatial (high frequency) and spectral components (low frequency) of multisource images. The fusion weight was constructed by comprehensively considering the relationship between multisensor high spatial resolution images and high spectral resolution images and the relationship among the spectral bands of the high spectral resolution image. Fused images with the highest spatial and spectral resolutions can be obtained. The GF-1 panchromatic
GF-1 multispectral
and GF-5 hyperspectral images were used in the experiments. Experimental results show that the proposed multisensor spatial–spectral fusion can effectively integrate the complementary spatial and spectral information to obtain the comparative fused results.
遥感高分五号（GF-5）空—谱融合大空间分辨率差异多传感器
remote sensingGF-5 satellitespatial-spectral fusionlarge spatial resolution differencemulti-sensor
Aiazzi B, Alparone L, Baronti S, Garzelli A and Selva M . 2006. MTF-tailored multiscale fusion of high-resolution MS and Pan imagery. Photogrammetric Engineering and Remote Sensing, 72(5): 591-596 [DOI: 10.14358/PERS.72.5.591http://dx.doi.org/10.14358/PERS.72.5.591 ]
Cetin M and Musaoglu N . 2009. Merging hyperspectral and panchromatic image data: qualitative and quantitative analysis. International Journal of Remote Sensing, 30(7): 1779-1804 [DOI: 10.1080/01431160802639525http://dx.doi.org/10.1080/01431160802639525 ]
Chavez Jr P S, 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 [DOI: 10.1306/44b4c288-170a-11d7-8645000102c1865dhttp://dx.doi.org/10.1306/44b4c288-170a-11d7-8645000102c1865d ]
Huang W, Xiao L, Wei Z H, Liu H Y and Tang S Z . 2015. A new pan-Sharpening method with deep neural networks. IEEE Geoscience and Remote Sensing Letters, 12(5): 1037-1041 [DOI: 10.1109/LGRS.2014.2376034http://dx.doi.org/10.1109/LGRS.2014.2376034 ]
Laben C A and Brower B V . 2000. Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. U.S., No. 6011875
Liu Y N . 2018. Visible-shortwave infrared hyperspectral imager of GF-5 satellite. Spacecraft Recovery and Remote Sensing, 39(3): 25-28
刘银年 . 2018. “高分五号”卫星可见短波红外高光谱相机的研制. 航天返回与遥感, 39(3): 25-28） [DOI: 10.3969/j.issn.1009-8518.2018.03.003http://dx.doi.org/10.3969/j.issn.1009-8518.2018.03.003 ]
Loncan L, De Almeida L B, Bioucas-Dias J M, Briottet X, Chanussot J, Dobigeon N, Fabre S, Liao W Z, Licciardi G A, Simões M, Tourneret J Y, Veganzones M A, Vivone G, Wei Q and Yokoya N . 2015. Hyperspectral pansharpening: a review. IEEE Geoscience and Remote Sensing Magazine, 3(3): 27-46 [DOI: 10.1109/MGRS.2015.2440094http://dx.doi.org/10.1109/MGRS.2015.2440094 ]
Meng X C, Shen H F, Zhang L P, Yuan Q Q and Li H F . 2015. A unified framework for spatio-temporal-spectral fusion of remote sensing images//Proceedings of 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Milan, Italy: IEEE: 2584-2587 [DOI: 10.1109/IGARSS.2015.7326340]
Meng X C . 2017. The Variational Fusion Methods for Multisource Spatio-Temporal-Spectral Optical Remote Sensing Images. Wuhan: Wuhan University (孟祥超. 2017. 多源时—空—谱光学遥感影像的变分融合方法. 武汉: 武汉大学)
Meng X C, Shen H F, Li H F, Zhang L P and Fu R D . 2019. Review of the pansharpening methods for remote sensing images based on the idea of meta-analysis: practical discussion and challenges. Information Fusion, 46: 102-113 [DOI: 10.1016/j.inffus.2018.05.006http://dx.doi.org/10.1016/j.inffus.2018.05.006 ]
Meng X C, Shen H F, Yuan Q Q, Li H F, Zhang L P and Sun W W . 2019. Pansharpening for cloud-contaminated very high-resolution remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 57(5): 2840-2854 [DOI: 10.1109/TGRS.2018.2878007http://dx.doi.org/10.1109/TGRS.2018.2878007 ]
Otazu X, González-Audícana M, Fors O and Núñez J . 2005. Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods. IEEE Transactions on Geoscience and Remote Sensing, 43(10): 2376-2385 [DOI: 10.1109/TGRS.2005.856106http://dx.doi.org/10.1109/TGRS.2005.856106 ]
Shen H. 2012. Integrated fusion method for multiple temporal-spatial-spectral images//International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Melbourne, Australia: [s.n.]:407-410 [DOI: 10.5194/isprsarchives-XXXIX-B7-407-2012]
Shen H F, Meng X C and Zhang L P . 2016. An integrated framework for the spatio-temporal-spectral fusion of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 54(12): 7135-7148 [DOI: 10.1109/TGRS.2016.2596290http://dx.doi.org/10.1109/TGRS.2016.2596290 ]
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)00036-7http://dx.doi.org/10.1016/S1566-2535(01)00036-7 ]
Vivone G, Alparone L, Chanussot J, Mura M D, Garzelli A, Licciardi G A, Restaino R and Wald L . 2015. A critical comparison among pansharpening algorithms. IEEE Transactions on Geoscience and Remote Sensing, 53(5): 2565-2586 [DOI: 10.1109/TGRS.2014.2361734http://dx.doi.org/10.1109/TGRS.2014.2361734 ]
Wald L. 2000. Quality of high resolution synthesised images: is there a simple criterion?//Proceedings of the 3rd Conference “Fusion of Earth Data: Merging Point Measurements, Raster Maps and Remotely Sensed Images”. Sophia Antipolis, France: [s.n.]:99-103
Wang Z and Bovik A C . 2002. A universal image quality index. IEEE Signal Processing Letters, 9(3): 81-84 [DOI: 10.1109/97.995823http://dx.doi.org/10.1109/97.995823 ]
Xu J, Guan Z Q, He X F and Hu J W . 2009. Novel method for merging panchromatic and multi-spectral images based on sensor spectral response. Journal of Remote Sensing, 13(1): 97-102
徐佳, 关泽群, 何秀凤, 胡俊伟 . 2009. 基于传感器光谱特性的全色与多光谱图像融合. 遥感学报, 13(1): 97-102 [DOI: 10.3321/j.issn:1007-4619.2009.01.013http://dx.doi.org/10.3321/j.issn:1007-4619.2009.01.013 ]
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.2011.2161320http://dx.doi.org/10.1109/TGRS.2011.2161320 ]
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 ]
Zhu X X, Tuia D, Mou L C, Xia G S, Zhang L P, Xu F and Fraundorfer F . 2017. Deep learning in remote sensing: a comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5(4): 8-36 [DOI: 10.1109/MGRS.2017.2762307http://dx.doi.org/10.1109/MGRS.2017.2762307 ]
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