高分五号高光谱数据融合方法比较
Comparison of fusion methods on GF-5 hyperspectral data
- 2022年26卷第4期 页码：632-645
DOI： 10.11834/jrs.20229318
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摘要
数据融合是解决高光谱卫星在时空分辨率等指标上受限的有效途径，探讨不同方法在GF-5高光谱数据上的融合效果，对GF-5高光谱数据的信息挖掘与推广应用有着重要意义。本文本着算法简单易用、适于推广的原则，采用GS（Gram-Schmidt）葛兰—施密特正交变换融合算法、GSA（GS Adaptive）自适应GS融合算法、CNMF（Coupled Non-negative Matrix Factorization）耦合非负矩阵分解融合算法、CRISP-W（Color Resolution Improvement Software Package with Wavelet transform）基于小波变换和CRISP-B（Color Resolution Improvement Software Package with Butterworth）基于巴特沃斯滤波器的分辨率提升融合算法、GLP（Generalized Laplacian Pyramid）广义拉普拉斯金字塔融合算法共6种融合方法，分别对BJ-2、GF-2、GF-1、GF-1C、GF-1D国产卫星多光谱数据与GF-5高光谱数据进行融合实验。通过目视分析、指标评价（相关系数、通用图像质量指标、峰值信噪比、光谱角、全局综合误差）、分类应用、时间成本4种方式对融合结果进行综合比较分析。结果表明，相融合的一组图像系列相同、空间分辨率相差越小，融合结果越好。CRISP-B、CRISP-W、GLP在提升空间分辨率、光谱保真度方面能达到较好的平衡，空间重建方面，GLP稍优且更稳定，CRISP-B、CRISP-W则在光谱信息保持方面稳定性更强且效果更好。数据源会对融合方法产生一定的影响，在光谱特征信息提取、分析等对光谱保真度要求高的工作中，GLP更适合同源数据（如GF-5与GF-1/1C/1D/2）融合，而在多源数据间（如GF-5与BJ-2）进行融合时，则优先选择CRISP-W。CNMF存在一定程度的色彩畸变，且运行时间较长。GSA、GS融合效果最差，其中，GSA不论是光谱保持能力还是空间分辨率提升能力均较GS更稳定。在小样本高光谱图像分类应用中，CRISP-B融合结果分类效果稳定，分类精度较高。GSA融合结果空间细节丰富，虽光谱失真较为严重，但同时增大了地物光谱分离度，仍适用于准确勾勒建筑物、道路等地物。本研究为GF-5高光谱数据与其他国产卫星多光谱数据融合方法的选择提供参考，有助于高分五号高光谱数据的应用与推广。
Abstract
Data fusion is an effective way to solve the limitation of hyperspectral satellites on temporal and spatial resolution. Discussing the fusion effects of different methods on GF-5 hyperspectral data is highly important for information mining and promotion application of GF-5 hyperspectral data.,In this study, based on the principle that the algorithm is easy to use and suitable for generalization, six fusion methods, namely, GS (Gram-Schmidt), GSA (GS Adaptive), CNMF (Coupled Non-negative Matrix Factorization), CRISP-B, CRISP-W (Color Resolution Improvement Software Package with Butterworth or Wavelet transform), GLP (Generalized Laplacian Pyramid) are separately used to perform fusion experiments on GF-5 hyperspectral data and multispectral data from BJ-2, GF-2, and GF-1/1C/1D domestic satellites. Visual interpretation, five indicators (correlation coefficient, universal image quality index, spectral angle mapper, erreur relative globale adimensionnelle de synthèse, and peak signal-to-noise ratio), classification application, and time costs are used to comprehensively evaluate the fusion results.,Results show that the fusion image series are the same and the smaller the spatial resolution difference, the better the fusion result. CRISP-B, CRISP-W, and GLP can achieve a good balance in improving spatial resolution and spectral fidelity. In terms of spatial reconstruction, GLP is slightly better and more stable, while CRISP-B and CRISP-W are more stable and effective in maintaining spectral information. The data source will have a certain effect on the fusion method. In the tasks that require high spectral fidelity, such as spectral feature information extraction and analysis, GLP is more suitable for the fusion of homologous data (such as GF-5 and GF-1/1C/1D/2). When the multi-source images (GF-5 and BJ-2) are merged, CRISP-W is preferred. CNMF has a certain degree of color distortion and takes a long time to run. GSA and GS have the worst fusion effect. The spectral retention and the spatial resolution improvement ability of GSA are more stable than those of GS. Based on a small sample, the classification effect of the CRISP-B fusion result is stable and highly accurate. The GSA fusion results are rich in spatial details. Although the spectral distortion is relatively serious, it also increases the spectral distinction of the ground objects, which is still suitable for accurately drawing buildings and roads.,This study provides method decision support for the fusion of GF-5 hyperspectral data and other domestic satellite multispectral data, which is helpful for the application and promotion of GF-5 hyperspectral data.
关键词
高光谱遥感高分五号国产卫星数据融合融合方法评价
Keywords
hyperspectral remote sensingGF-5domestic satellitedata fusionfusion method evaluation
references
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.2007.901007http://dx.doi.org/10.1109/TGRS.2007.901007]
Alparone L, Wald L, Chanussot J, Thomas C, Gamba P and Bruce L M. 2007. Comparison of pansharpening algorithms: outcome of the 2006 GRS-S data-fusion contest. IEEE Transactions on Geoscience and Remote Sensing, 45(10): 3012-3021 [DOI: 10.1109/TGRS.2007.904923http://dx.doi.org/10.1109/TGRS.2007.904923]
Ghassemian H. 2016. A review of remote sensing image fusion methods. Information Fusion, 32: 75-89 [DOI: 10.1016/j.inffus.2016.03.003http://dx.doi.org/10.1016/j.inffus.2016.03.003]
Gomez R B, Jazaeri A and Kafatos M. 2001. Wavelet-based hyperspectral and multispectral image fusion//Proceedings of SPIE 4383, Geo-Spatial Image and Data Exploitation II. Orlando, FL, United States: SPIE: 36-42 [DOI: 10.1117/12.428249http://dx.doi.org/10.1117/12.428249]
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:20080522http://dx.doi.org/10.1049/el:20080522]
Laben C A and Brower B V. 2000. Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. U.S., 6011875
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.2012.6229639http://dx.doi.org/10.1109/I2MTC.2012.6229639]
Li X T, Lu J X, Song X N, Sun Y Y, Li L, Lei T J and Qu W. 2018. Application of the GF satellite data in flood disaster monitoring//Proceedings of 2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia, Spain: IEEE: 7293-7296 [DOI: 10.1109/IGARSS.2018.8519548http://dx.doi.org/10.1109/IGARSS.2018.8519548]
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]
Ren K, Sun W W, Meng X C, Yang G and Du Q. 2020. Fusing China GF-5 hyperspectral data with GF-1, GF-2 and sentinel-2A multispectral data: which methods should be used? Remote Sensing, 12(5): 882 [DOI: 10.3390/rs12050882http://dx.doi.org/10.3390/rs12050882]
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.2016.2561021http://dx.doi.org/10.1109/MGRS.2016.2561021]
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.2014.2356512http://dx.doi.org/10.1109/jstars.2014.2356512]
Sun Y Z, Jiang G W, Li Y D, Yang Y, Dai H S, He J, Ye Q H, Cao Q, Dong C Z, Zhao S H and Wang W H. 2018. GF-5 Satellite: overview and application prospects. Spacecraft Recovery and Remote Sensing, 39(3): 1-13
孙允珠, 蒋光伟, 李云端, 杨勇, 代海山, 何军, 叶擎昊, 曹琼, 董长哲, 赵少华, 王维和. 2018. “高分五号”卫星概况及应用前景展望. 航天返回与遥感, 39(3): 1-13 [DOI: 10.3969/j.issn.1009-8518.2018.03.001http://dx.doi.org/10.3969/j.issn.1009-8518.2018.03.001]
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.2013.2267204http://dx.doi.org/10.1109/jstars.2013.2267204]
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: Thierry Ranchin, Lucien Wald Editors: 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]
Winter M E, Winter E M, Beaven S G and Ratkowski A J. 2007. Hyperspectral image sharpening using multispectral data//Proceedings of 2007 IEEE Aerospace Conference. Big Sky, MT: IEEE: 1-9 [DOI: 10.1109/AERO.2007.353060http://dx.doi.org/10.1109/AERO.2007.353060]
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]
Yokoya N, Grohnfeldt C and Chanussot J. 2017. Hyperspectral and multispectral data fusion: a comparative review of the recent literature. IEEE Geoscience and Remote Sensing Magazine, 5(2): 29-56 [DOI: 10.1109/MGRS.2016.2637824http://dx.doi.org/10.1109/MGRS.2016.2637824]
Yuan Q Q, Wei Y C, Meng X C, Shen H F and Zhang L P. 2018. A multiscale 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.2018.2794888http://dx.doi.org/10.1109/jstars.2018.2794888]
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. Proceedings of the 1992 Summaries of the Third Annual JPL Airborne Geoscience Workshop. Pasadena: JPL, 1992: 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 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.2009.2017737http://dx.doi.org/10.1109/tgrs.2009.2017737]
Zurita-Milla R, Clevers J G P W and Schaepman M E. 2008. Unmixing-based Landsat TM and MERIS FR data fusion. IEEE Geoscience and Remote Sensing Letters, 5(3): 453-457 [DOI: 10.1109/lgrs.2008.919685http://dx.doi.org/10.1109/lgrs.2008.919685]
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