Downscaling GF-5 hyperspectral images by fusing with Sentinel-2 images
- Vol. 27, Issue 8, Pages: 1936-1950(2023)
Published： 07 August 2023
DOI： 10.11834/jrs.20211420
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摘要
高分五号（GF-5）是中国首颗实现对大气和陆地综合观测的全谱段高光谱卫星，其搭载的可见短波红外高光谱相机AHSI提供的遥感数据拥有极高的光谱分辨率。然而，AHSI数据的空间分辨率为30 m，较低的空间分辨率限制了应用场景。为实现GF-5数据的降尺度，本文通过融合10 m哨兵二号（Sentinel-2）多光谱数据，生成10 m GF-5高光谱数据。在方法上，针对现有先进的信息损失引导的图像融合方法ILGIF（Information Loss-Guided Image Fusion）在高光谱图像降尺度中计算时间成本高的问题，本文提出了其快速版本FILGIF（Fast ILGIF）。另一方面，在降尺度过程中，本文考虑并估计了30 m GF-5高光谱数据和10 m Sentinel-2数据之间的尺度转换点扩散函数PSF（Point Spread Function），提高融合数据质量。实验结果验证了融合Sentinel-2数据用于GF-5高光谱数据降尺度的可行性。同时，结果表明：在获得与ILGIF相当精度的前提下，FILGIF大幅提高了运行效率；尺度转换PSF对降尺度过程有着重要影响，其准确的估计有助于获得更高精度的降尺度结果。
Abstract
GF-5 is the first hyperspectral satellite in China that can acquire comprehensive observations of the atmosphere and land surface. The Advanced Hyper Spectral Imager (AHSI) onboard GF-5 is a sensor that can acquire data covering visible near-infrared (VNIR) and short-wave infrared (SWIR) wavelengths with a very fine spectral resolution (i.e.
5 nm for VNIR and 10 nm for SWIR). However
the spatial resolution of GF-5 AHSI data (i.e.
30 m) is relatively coarse for the extraction of land cover information in several cases
such as small-sized buildings and roads in urban areas.
To produce GF-5 data with fine spatial and spectral resolutions
in this paper
GF-5 hyperspectral images were downscaled to 10 m by spatial-spectral image fusion with 10 m Sentinel-2 multispectral images. To deal with the large computational burden of the advanced Information Loss Guided Image Fusion (ILGIF) method and the ubiquitous effect of the Point Spread Function (PSF)
this paper also introduced a fast and accurate method for downscaling GF-5 data.
A fast ILGIF (FILGIF) method was proposed. In this method
the original GF-5 hyperspectral data were transformed to a new feature space via principal component analysis (PCA)
and the ILGIF-based spatial-spectral image fusion was implemented for the first few principal components. The fused components coupled with the remaining ones were transformed back to the original space to produce the 10 m downscaled results. The scale transformation optimal PSF between 10 m Sentinel-2 and 30 m GF-5 data was estimated adaptively for each band of GF-5 to enhance downscaling.
Experimental results show that by fusing with the 10 m Sentinel-2 data
the 30 m GF-5 hyperspectral data can be downscaled effectively to 10 m. The FILGIF and ILGLF methods obtain greater accuracy than the area-to-point regression kriging (ATPRK) and approximate ATPRK (AATPRK) methods. Moreover
the computational cost of FILGIF is 30 times lower than that of ILGIF
and the accuracy of the downscaling results can be improved by considering the PSF effect adaptively for each band.
Sentinel-2 images are suitable for downscaling GF-5 hyperspectral images. The proposed FILGIF method can achieve a comparable accuracy compared with ILGIF while significantly reducing computational costs. Highly accurate downscaling results are obtained when the PSF effect is considered appropriately.
关键词
遥感高分五号（GF-5）哨兵二号（Sentinel-2）降尺度空谱融合地统计学点扩散函数（PSF）
Keywords
remote sensingGF-5Sentinel-2downscalingspatial-spectral image fusiongeostatisticsPoint Spread Function (PSF)
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]
Amolins K, Zhang Y and Dare P. 2007. Wavelet based image fusion techniques-An introduction, review and comparison. ISPRS Journal of Photogrammetry and Remote Sensing, 62(4): 249-263 [DOI: 10.1016/j.isprsjprs.2007.05.009http://dx.doi.org/10.1016/j.isprsjprs.2007.05.009]
Atkinson P M. 2013. Downscaling in remote sensing. International Journal of Applied Earth Observation and Geoinformation, 22: 106-114 [DOI: 10.1016/j.jag.2012.04.012http://dx.doi.org/10.1016/j.jag.2012.04.012]
Atkinson P M, Pardo-Iguzquiza E and Chica-Olmo M. 2008. Downscaling cokriging for super-resolution mapping of continua in remotely sensed images. IEEE Transactions on Geoscience and Remote Sensing, 46(2): 573-580 [DOI: 10.1109/tgrs.2007.909952http://dx.doi.org/10.1109/tgrs.2007.909952]
Brunsdon C, Fotheringham A S and Charlton M E. 1996. Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Analysis, 28(4): 281-298 [DOI: 10.1111/j.1538-4632.1996.tb00936.xhttp://dx.doi.org/10.1111/j.1538-4632.1996.tb00936.x]
Carper W J, Lillesand T M, Kiefer P W. 1990. The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data. Photogrammetric Engineering and Remote Sensing, 56(4): 459-467
Chavez P S, Sides S C, 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
Foody G M. 2003. Geographical weighting as a further refinement to regression modelling: an example focused on the NDVI-rainfall relationship. Remote Sensing of Environment, 88(3): 283-293 [DOI: 10.1016/j.rse.2003.08.004http://dx.doi.org/10.1016/j.rse.2003.08.004]
Ghamisi P, Rasti B, Yokoya N, Wang Q M, Hofle B, Bruzzone L, Bovolo F, Chi M M, Anders K, Gloaguen R, Atkinson P M and Benediktsson J A. 2019. Multisource and multitemporal data fusion in remote sensing: a comprehensive review of the state of the art. IEEE Geoscience and Remote Sensing Magazine, 7(1): 6-39 [DOI: 10.1109/MGRS.2018.2890023http://dx.doi.org/10.1109/MGRS.2018.2890023]
Huang P S and Tu T M. 2007. Reply to Erratum to “A new look at IHS-like image fusion methods”. Information Fusion, 8(2): 218 [DOI: 10.1016/j.inffus.2006.10.006http://dx.doi.org/10.1016/j.inffus.2006.10.006]
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
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 [DOI: 10.11834/jrs.20210259http://dx.doi.org/10.11834/jrs.20210259]
Shah V P, Younan N H and King R L. 2008. An efficient pan-sharpening method via a combined adaptive PCA approach and contourlets. IEEE Transactions on Geoscience and Remote Sensing, 46(5): 1323-1335 [DOI: 10.1109/tgrs.2008.916211http://dx.doi.org/10.1109/tgrs.2008.916211]
Shen D B, Liu J J, Xiao Z Y, Yang J L and Xiao L. 2020. A twice optimizing net with matrix decomposition for hyperspectral and multispectral image fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 4095-4110 [DOI: 10.1109/jstars.2020.3009250http://dx.doi.org/10.1109/jstars.2020.3009250]
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.2018.2797894http://dx.doi.org/10.1109/JSTARS.2018.2797894]
Thomas C, Ranchin T, Wald L and Chanussot J. 2008. Synthesis of multispectral images to high spatial resolution: a critical review of fusion methods based on remote sensing physics. IEEE Transactions on Geoscience and Remote Sensing, 46(5): 1301-1312 [DOI: 10.1109/tgrs.2007.912448http://dx.doi.org/10.1109/tgrs.2007.912448]
Wang Q M, Shi W Z and Atkinson P M. 2016b. Area-to-point regression kriging for pan-sharpening. ISPRS Journal of Photogrammetry and Remote Sensing, 114: 151-165 [DOI: 10.1016/j.isprsjprs.2016.02.006http://dx.doi.org/10.1016/j.isprsjprs.2016.02.006]
Wang Q M, Shi W Z and Atkinson P M. 2020a. Information loss-guided multi-resolution image fusion. IEEE Transactions on Geoscience and Remote Sensing, 58(1): 45-57 [DOI: 10.1109/tgrs.2019.2930764http://dx.doi.org/10.1109/tgrs.2019.2930764]
Wang Q M, Shi W Z, Atkinson P M and Wei Q. 2017. Approximate area-to-point regression kriging for fast hyperspectral image sharpening. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(1): 286-295 [DOI: 10.1109/jstars.2016.2569480http://dx.doi.org/10.1109/jstars.2016.2569480]
Wang Q M, Shi W Z, Li Z B and Atkinson P M. 2016a. Fusion of Sentinel-2 images. Remote Sensing of Environment, 187: 241-252 [DOI: 10.1016/j.rse.2016.10.030http://dx.doi.org/10.1016/j.rse.2016.10.030]
Wang Q M, Tang Y J and Atkinson P M. 2020b. The effect of the point spread function on downscaling continua. ISPRS Journal of Photogrammetry and Remote Sensing, 168: 251-267 [DOI: 10.1016/j.isprsjprs.2020.08.016http://dx.doi.org/10.1016/j.isprsjprs.2020.08.016]
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]
Xiao L, Liu P F and Li H. 2020. Progress and challenges in the fusion of multisource spatial-spectral remote sensing images. Journal of Image and Graphics, 25(5): 851-863
肖亮, 刘鹏飞, 李恒. 2020. 多源空—谱遥感图像融合方法进展与挑战. 中国图象图形学报, 25(5): 851-863 [DOI: 10.11834/jig.190620http://dx.doi.org/10.11834/jig.190620]
Yang J X, Zhao Y Q and Chan J C W. 2018. Hyperspectral and multispectral image fusion via deep two-branches convolutional neural network. Remote Sensing, 10(5): 800 [DOI: 10.3390/rs10050800http://dx.doi.org/10.3390/rs10050800]
Yuan J W, Wu C, Du B, Zhang L P and Wang S G. 2020. Analysis of landscape pattern on urban land use based on GF-5 hyperspectral data. Journal of Remote Sensing (Chinese), 24(4): 465-478
袁静文, 武辰, 杜博, 张良培, 王树根. 2020. 高分五号高光谱遥感影像的城市土地利用景观格局分析. 遥感学报, 24(4): 465-478 [DOI: 10.11834/jrs.20209252http://dx.doi.org/10.11834/jrs.20209252]
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]
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College of Geodesy and Geomatics, Shandong University of Science and Technology
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Anhui Key Laboratory of Wetland Ecological Protection and Restoration, Anhui University
Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University
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