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包科迪，孟祥超，邵枫，叶蒙梦，金康俊，彭振宇.2022.全色/多光谱融合影像的MVG无参考质量评价.遥感学报，26（3）： 568-578
Bao K D，Meng X C，Shao F，Ye M M，Jin K J and Peng Z Y. 2022. A MVG-based non-reference quality evaluation method for Pan/MS Fusion. National Remote Sensing Bulletin， 26（3）：568-578
同时具有高空间分辨率和高光谱保真度的全色/多光谱融合影像具有广泛的应用前景。然而，一方面全色/多光谱融合方法通常会产生不同的空间畸变或光谱失真；另一方面，受卫星成像系统限制，无法获取真实高空间分辨率多光谱参考影像，因此，如何在无真实参考影像情况下有效评估融合影像质量具有重要意义。为此，本文提出了一种基于多元高斯模型（MVG）的全色/多光谱融合影像无参考质量评价方法，本方法基于全色/多光谱遥感影像融合大型数据库，构建了包含多种卫星类型和专题地物的基准数据集、测试数据集、验证数据集，用于MVG模型设计、测试、验证；在此基础上，充分考虑融合影像的空间畸变和光谱失真，针对性提取影像的空间和光谱敏感特征，通过大量空、谱特征的MVG模型训练拟合，建立基准MVG评价模型，此外，在基准MVG训练中，选择高方差子影像进行模型训练，以增强模型的稳健性；最终，以基准MVG模型与融合影像MVG模型之间的相对距离计算融合影像质量。实验结果表明，本研究方法相较于传统无参考质量评价方法具有一定优越性，有利于全色/多光谱融合影像质量评价的应用研究。
Pansharpening aims to sharpen a Low-spatial-Resolution (LR) multispectral (MS) image using a High-spatial-Resolution (HR) panchromatic (PAN) image to obtain a HR MS image. The pansharpened image with high spatial resolution and spectral fidelity had a wide application prospect. However
on the one hand
pansharpening methods usually produce different spatial or spectral distortions; on the other hand
the real HR MS reference image for the quality evaluation of the fused image cannot be obtained due to the limitation of satellite imaging systems. Therefore
the effective evaluation the quality of the fusion image without the real reference image is of great significance.
In this paper
we propose a new non-reference quality evaluation method for pansharpened image on the basis of the Multivariate Gaussian Model (MVG). On the basis of a large-scale pansharpening database
this study constructed benchmark
test
and verification data sets
including various satellite sensors and thematic types. Then
a novel benchmark MVG evaluation model was constructed on the basis of the benchmark data set. In the proposed method
the images were first divided into sub-image blocks
and the spatial and spectral sensitive features of each sub-image block were first extracted. Then
many spatial and spectral characteristics were trained to establish the benchmark MVG evaluation model. In addition
in the benchmark MVG training
the sub-image with a high variance was selected for model training to enhance the robustness of the benchmark MVG model. Then
the testing MVG evaluation model for the fused image was established by comprehensively considering the spatial and spectral distortions. Finally
the relative distance between the benchmark and testing MVG models of the fused image was used to calculate the fused image quality.
Experimental results show that the proposed method has better performance than the traditional non-reference quality evaluation methods in most satellite- and thematic-based data sets. The proposed method is based on Wald’s protocol and cannot be extended well to the full resolution evaluation of the fused image. Therefore
in future work
we will continue to study the full resolution evaluation method for pansharpening
which is more challenging and meaningful.
遥感影像融合无参考质量评价多元高斯模型（MVG）全色影像多光谱影像
remote sensingimage fusionnon-reference quality evaluationMultivariate Gaussian Model (MVG)panchromatic imagemultispectral image
Aiazzi B, Alparone L, Baronti S and Garzelli A. 2002. Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis. IEEE Transactions on Geoscience and Remote Sensing, 40(10): 2300-2312 [DOI: 10.1109/TGRS.2002.803623http://dx.doi.org/10.1109/TGRS.2002.803623]
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]
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.74.2.193http://dx.doi.org/10.14358/PERS.74.2.193]
Alparone L, Baronti S, Garzelli A and Nencini F. 2004. A global quality measurement of pan-sharpened multispectral imagery. IEEE Geoscience and Remote Sensing Letters, 1(4): 313-317 [DOI: 10.1109/LGRS.2004.836784http://dx.doi.org/10.1109/LGRS.2004.836784]
Bishop C M. 2006. Pattern Recognition and Machine Learning. New York: Springer
Canny J. 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6): 679-698 [DOI: 10.1109/TPAMI.1986.4767851http://dx.doi.org/10.1109/TPAMI.1986.4767851]
Chavez 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
Fukunaga K. 1972. Introduction to Statistical Pattern Recognition. New York, NY, USA: Academic Press
Garzelli A and Nencini F. 2009. Hypercomplex quality assessment of multi/hyperspectral images. IEEE Geoscience and Remote Sensing Letters, 6(4): 662-665 [DOI: 10.1109/LGRS.2009.2022650http://dx.doi.org/10.1109/LGRS.2009.2022650]
Garzelli A, Nencini F and Capobianco L. 2008. Optimal MMSE pan sharpening of very high resolution multispectral images. IEEE Transactions on Geoscience and Remote Sensing, 46(1): 228-236 [DOI: 10.1109/TGRS.2007.907604http://dx.doi.org/10.1109/TGRS.2007.907604]
Han P, Gong J Y, Li Z L and Cheng L. 2008. Comparing the effect of aggregation methods for remote sensing image. Journal of Remote Sensing, 12(6): 964-971
韩鹏, 龚健雅, 李志林, 程亮. 2008. 遥感影像空间尺度上推方法的评价. 遥感学报, 12(6): 964-971
Kwan C, Budavari B, Bovik A C and Marchisio G. 2017. Blind quality assessment of fused WorldView-3 images by using the combinations of pansharpening and hypersharpening paradigms. IEEE Geoscience and Remote Sensing Letters, 14(10): 1835-1839 [DOI: 10.1109/LGRS.2017.2737820http://dx.doi.org/10.1109/LGRS.2017.2737820]
Laben C A and Brower B V. 2000. Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. U.S., No. 6011875
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]
Meng X C, Xiong Y M, Shao F, Shen H F, Sun W W, Yang G, Yuan Q Q, Fu R D and Zhang H Y. 2021. A large-scale benchmark data set for evaluating pansharpening performance: overview and implementation. IEEE Geoscience and Remote Sensing Magazine, 9(1): 18-52 [DOI: 10.1109/MGRS.2020.2976696http://dx.doi.org/10.1109/MGRS.2020.2976696]
Ojala T, Pietikainen M and Maenpaa T. 2002. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7): 971-987 [DOI: 10.1109/TPAMI.2002.1017623http://dx.doi.org/10.1109/TPAMI.2002.1017623]
Ranchin T and Wald L. 2000. Fusion of high spatial and spectral resolution images: the ARSIS concept and its implementation. Photogrammetric Engineering and Remote Sensing, 66(1): 49-61
Sales M H R, Souza C M and Kyriakidis P C. 2013. Fusion of MODIS images using Kriging with external drift. IEEE Transactions on Geoscience and Remote Sensing, 51(4): 2250-2259 [DOI: 10.1109/TGRS.2012.2208467http://dx.doi.org/10.1109/TGRS.2012.2208467]
Sheikh H R, Sabir M F and Bovik A C. 2006. A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Transactions on Image Processing, 15(11): 3440-3451 [DOI: 10.1109/TIP.2006.881959http://dx.doi.org/10.1109/TIP.2006.881959]
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]
Vivone G, Restaino R and Chanussot J. 2018. A Bayesian procedure for full-resolution quality assessment of pansharpened products. IEEE Transactions on Geoscience and Remote Sensing, 56(8): 4820-4834 [DOI: 10.1109/TGRS.2018.2839564http://dx.doi.org/10.1109/TGRS.2018.2839564]
Wald L. 2000. Quality of high resolution synthesised images: is there a simple criterion?//Proceedings of the Third Conference. Nice, France: SEE/URISCA: 99-103
Wald L, Ranchin T and 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 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]
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.2003.819861http://dx.doi.org/10.1109/TIP.2003.819861]
Xiong Y M, Shao F, Meng X C, Jiang Q P, Sun W W, Fu R D and Ho Y S. 2020. A large-scale remote sensing database for subjective and objective quality assessment of pansharpened images. Journal of Visual Communication and Image Representation, 73: 102947 [DOI: 10.1016/j.jvcir.2020.102947http://dx.doi.org/10.1016/j.jvcir.2020.102947]
Yang J X, Zhao Y Q, Yi C and Chan J C W. 2017. No-reference hyperspectral image quality assessment via quality-sensitive features learning. Remote Sensing, 9(4): 305 [DOI: 10.3390/rs9040305http://dx.doi.org/10.3390/rs9040305]
Yuhas R H, Goetz A F H and Boardman J W. 1992. Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm//Summaries of the Third Annual JPL Airborne Geoscience Workshop. [s.l.]: JPL: 147-149
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]
Zheng J, Wang J and Song S M. 2019. Advantages of Canny double threshold operator in edge detection. Geospatial Information, 17(11): 128-130
郑健, 王继, 宋世铭. 2019. Canny双阈值算子在边缘提取中的优势. 地理空间信息, 17(11): 128-130 [DOI: 10.3969/j.issn.1672-4623.2019.11.036http://dx.doi.org/10.3969/j.issn.1672-4623.2019.11.036]
Zhou B Z, Shao F, Meng X C, Fu R D and Ho Y S. 2019. No-reference quality assessment for pansharpened images via opinion-unaware learning. IEEE Access, 7: 40388-40401 [DOI: 10.1109/ACCESS.2019.2905615http://dx.doi.org/10.1109/ACCESS.2019.2905615]
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