Sentinel-2卫星影像融合方法与质量评价分析
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摘要
Sentinel-2卫星传感器获取的是3种不同空间分辨率的光学遥感影像,如何通过融合方法提高较低空间分辨率波段的空间分辨率成为Sentinel-2数据应用面临的问题之一。以Sentinel-2B影像为数据源,利用相关系数最大法、中心波长最近邻法、像元值最大法和主成分分析等4种方法从4个10 m分辨率的波段中产生一个高分辨率波段;采用主成分分析、高通滤波、小波变换、Gram-Schmidt变换以及Pansharp共5种融合方法,对产生的高分辨率数据和6个20 m分辨率的多光谱数据进行融合,并从定性、定量(信息熵、平均梯度、光谱相关系数、均方根误差和通用图像质量指数)以及融合影像的分类精度3个方面对融合效果进行评价,结果表明:相关系数最大法的Pansharp方法融合图像质量优于其他融合方法,分类精度略低于最高的像元值最大法的GS方法,并且远高于4个原始10 m分辨率的多光谱影像的分类精度。从实验数据的分类精度分析,不同融合方法在不同地物提取中各有优势,在实际应用中,应根据实际研究需要,选择适宜的方案。该研究可为Sentinel-2卫星以及相似卫星数据处理和应用提供参考。
Sentinel-2 satellite sensors acquire three kinds of optical remote sensing images with different spatial resolutions.How to improve the spatial resolution of lower spatial resolution bands by fusion method is one of the problems faced by Sentinel-2 applications.Taking the Sentinel-2 B image as the data source,a high spatial resolution band was generated or selected from the four 10 m spatial resolution bands by four methods:the maximum correlation coefficient,the central wavelength nearest neighbor,the pixel maximum and the principal component analysis.We fused the one high spatial resolution band produced and six multispectral bands with 20 m spatial resolution by the five fusion methods of PCA,HPF,WT,GS and Pansharp to produce six multispectral bands with 10 m spatial resolution and the fusion results were evaluated from three aspects:qualitative and quantitative(information entropy,average gradient,spectral correlation coefficient,root mean square error and general image quality index) and classification accuracy of fused images.Results show that the fusion quality of Pansharp with the maximum correlation coefficient is better than other fusion methods,and the classification accuracy is slightly lower than the GS with the pixel maximum of the highest classification accuracy and far higher than the original four multispectral image with 10 m spatial resolution.According to the classification accuracy of experimental data,different fusion methods have different advantages in extraction of different ground objects.In application,appropriate schemes should be selected according to actual research needs.This research can provide reference for Sentinel-2 satellite and similar satellite data processing and application.
Sentinel-2 satellite sensors acquire three kinds of optical remote sensing images with different spatial resolutions.How to improve the spatial resolution of lower spatial resolution bands by fusion method is one of the problems faced by Sentinel-2 applications.Taking the Sentinel-2 B image as the data source,a high spatial resolution band was generated or selected from the four 10 m spatial resolution bands by four methods:the maximum correlation coefficient,the central wavelength nearest neighbor,the pixel maximum and the principal component analysis.We fused the one high spatial resolution band produced and six multispectral bands with 20 m spatial resolution by the five fusion methods of PCA,HPF,WT,GS and Pansharp to produce six multispectral bands with 10 m spatial resolution and the fusion results were evaluated from three aspects:qualitative and quantitative(information entropy,average gradient,spectral correlation coefficient,root mean square error and general image quality index) and classification accuracy of fused images.Results show that the fusion quality of Pansharp with the maximum correlation coefficient is better than other fusion methods,and the classification accuracy is slightly lower than the GS with the pixel maximum of the highest classification accuracy and far higher than the original four multispectral image with 10 m spatial resolution.According to the classification accuracy of experimental data,different fusion methods have different advantages in extraction of different ground objects.In application,appropriate schemes should be selected according to actual research needs.This research can provide reference for Sentinel-2 satellite and similar satellite data processing and application.
引文
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[19] Zhu Xia.Research on Building Edge Spatial Information Extraction based on High Spatial Resolution Images[D].Hangzhou:Zhejiang University,2014.[朱霞.基于高空间分辨率影像的建筑物边缘空间信息提取研究[D].杭州:浙江大学,2014.]
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[21] Li Xia,Zhu Qian,Zhang Kun,et al.Quality Evaluation of TH-1 Remote Sensing Image Fusion in Different Geomorphology[J].Computer Engineering and Application,2016,52(4):200-207.[李霞,朱倩,张昆,等.不同地貌TH-1遥感影像融合质量评价[J].计算机工程与应用,2016,52(4):200-207.]
[22] Li Jun,Zhou Yueqin.Wavelet Transform for the Fusion of High Resolution Panchromatic Images and Multispectral Images[J].Journal of Remote Sensing,1999,3(2):116-121.[李军,周月琴.小波变换用于高分辨率全色影像与多光谱影像的融合研究[J].遥感学报,1999,3(2):116-121.]
[23] Li Lin,She Mengyuan,Luo Heng.Comparison of Panchromatic and Multispectral Image Fusion Methods for ZY-3 Satellite[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(16):157-167.[李霖,佘梦媛,罗恒.ZY-3卫星全色与多光谱影像融合方法比较[J].农业工程学报,2014,30(16):157-167.]
[24] Liu Erjia,Luo Zhidong,Zhang Xiaoli,et al.Comparision of Fusion Algorithms for GF-1 Data from Extracted of Distribution Information on Production and Construction Projects[J].Journal of Soil and Water Conservation,2018,32(3):358-363.[刘二佳,罗志东,张晓丽等.面向GF-1数据不同融合方法对扰动地块的提取[J].水土保持学报,2018,32(3):358-363.]
[25] Wang Le.Remote Sensed Imagery Fusion and Quality Evaluation[D].Changchun:Jilin University.2012.[王乐.遥感影像融合及其质量评价[D].长春:吉林大学,2012.]
[26] Loncan L,Almeida L B D,Bioucas D J M,et al.Hyperspectral Pansharpening:A Review[J].IEEE Geoscience & Remote Sensing Magazine,2015,3(3):27-46.
[27] Wang Z,Bovik A C.A Universal Image Quality Index[J].IEEE Signal Processing Letters,2002,9(3):81-84.
[28] Liu Kun,Fu Jingying,Li Fei.Evaluate Study of Four Fusion Methods of GF-1 PAN and Multi-spectral Images[J].Remote Sensing Technology and Application,2015,30(5):980-986.[刘锟,付晶莹,李飞.高分一号卫星4种融合方法评价[J].遥感技术与应用,2015,30(5):980-986.]
[2] Zhang Liangpei,Shen Huanfeng.Progress and Prospect of Remote Sensing Data Fusion[J].Journal of Remote Sensing,2016,20 (5):1050-1061.[张良培,沈焕锋.遥感数据融合的进展与前瞻[J].遥感学报,2016,20(5):1050-1061.]
[3] Gomez R B,Kafatos M.Wavelet-based Hyperspectral and Multispectral Image Fusion[J].Proceeding of SPIE,2001,4383:36-42.
[4] Eismann M T,Hardie R C.Application of the Stochastic Mixing Model to Hyperspectral Resolution Enhancement[J].IEEE Transactions on Geoscience & Remote Sensing,2004,42(9):1924-1933.
[5] Hardie R C,Eismann M T,Wilson G L.MAP Estimation for Hyperspectral Image Resolution Enhancement Using an Auxiliary Sensor[J].IEEE Transactions on Image Processing,2004,13(9):1174-1184.
[6] Hardie R C,Eismann M T.Hyperspectral Resolution Enhancement Using High-resolution Multispectral Imagery with Arbitrary Response Functions[J].IEEE Trans anctions on Geoscience and Remote Sensing,2005,43(3):455-465.
[7] Kwan C,Budaarl B,Bovik A C,et al.Blind Quality Assessment of Fused WorldView-3 Images by Using the Combinations of Pansharpening and Hypersharpening Paradigms[J].IEEE Geoscience & Remote Sensing Letters,2017(99):1-5.
[8] Wang Q,Shi W,Li Z,et al.Fusion of Sentinel-2 Images[J].Remote Sensing of Environment,2016,187:241-252.
[9] Hu Yang,Xi Xiaohuan,Wang Cheng,et al.Pleiades Satellite Image Fusion Method and Quality Qvaluation[J].Remote Sensing Technology and Application,2014,29 (3):476-481.[胡洋,习晓环,王成,等.Pleiades卫星影像融合方法与质量评价[J].遥感技术与应用,2014,29(3):476-481.]
[10] Zhou Weifeng,Cao Li,Li Xiaoshu,et al.Assessments of Fusion Methods Using WorldView-2 Satelite Images for Coastal Oyster Culture Observation[J].Remote Sensing Technology and Application,2018,33(1):103-109.[周为峰,曹利,李小恕,等.沿海牡蛎养殖的WordView-2影像融合方法评价[J].遥感技术与应用,2018,33(1):103-109.]
[11] Sun Pan,Dong Yusen,Chen Weitao,et al.Fusion and Quality Evaluation of GF-2 Image[J].Remote Sensing for Land & Resources,2016,28(4):108-13.[孙攀,董玉森,陈伟涛,等.高分二号卫星影像融合及质量评价[J].国土资源遥感,2016,28(4):108-113.]
[12] Pang Jinjiao,Sun Rui,Wang Wen.Influence of Registration Error on Land Cover Classification and Change Detection in High-resolution Remote Sensing Image[J].Remote Sensing Technology and Application,2014,29(3):498-505.[逄锦娇,孙睿,王汶.高分辨率影像配准误差对土地覆盖分类和变化检测的影响[J].遥感技术与应用,2014,29(3):498-505.]
[13] Xue Dongjian,Zhang Donghui,He Zhengwei,et al.Application of Multi Source Remote Sensing Image Fusion Technology in Eological Disaster Survey[J].Remote Sensing Technology and Application,2011,26 (5):664-669.[薛东剑,张东辉,何政伟,等.多源遥感影像融合技术在地质灾害调查中的应用[J].遥感技术与应用,2011,26(5):664-669.]
[14] Zhou Jianan.Research on Remote Sensing Image Fusion Method based on GPU[D].Lanzhou:Lanzhou Jiaotong University,2015.[周嘉男.基于GPU的遥感影像融合方法研究[D].兰州:兰州交通大学,2015.]
[15] Ma Xudong.Research on Quality Evaluation of Optical Remote Sensing Image Compression and Fusion[D].Wuhan:Wuhan University,2014.[马旭东.光学遥感影像压缩及融合的质量评价研究[D].武汉:武汉大学,2014.]
[16] Li Cunjun,Liu Liangyun,Wang Jihua,et al.Comparison of Two High Fidelity Remote Sensing Image Fusion Methods[J].Journal of Image and Graphics,2004,9 (11):1376-1385.[李存军,刘良云,王纪华,等.两种高保真遥感影像融合方法比较[J].中国图象图形学报,2004,9(11):1376-1385.]
[17] Cangkofner U G,Pradhan P S,Holcomb D W.Optimizing the High-Pass Filter Addition Technique for Image Fusion[J].Photogrammetric Engineering & Remote Sensing,2008,74(9):1107-1118.
[18] Psjr C,Sides S C,Anderson J A.Comparison of Three Different Methods to Merge Multiresolution and Multispectral Data Landsat TM and SPOT Panchromatic[J].Photogrammetric Engineering & Remote Sensing,1991,57(3):265-303.
[19] Zhu Xia.Research on Building Edge Spatial Information Extraction based on High Spatial Resolution Images[D].Hangzhou:Zhejiang University,2014.[朱霞.基于高空间分辨率影像的建筑物边缘空间信息提取研究[D].杭州:浙江大学,2014.]
[20] Zheng Zhong,Qi Yuan,Zhang Jinlong.Compariing with Different Remote Sensing Image Fusion Method based on Evaluation Index of Spectral Angle and Spectral Distance[J].Remote Sensing Technology and Application,2013,28(3):437-443.[郑中,祁元,张金龙.基于光谱角和光谱距离评价指标的遥感影像融合方法比较研究[J].遥感技术与应用,2013,28(3):437-443.]
[21] Li Xia,Zhu Qian,Zhang Kun,et al.Quality Evaluation of TH-1 Remote Sensing Image Fusion in Different Geomorphology[J].Computer Engineering and Application,2016,52(4):200-207.[李霞,朱倩,张昆,等.不同地貌TH-1遥感影像融合质量评价[J].计算机工程与应用,2016,52(4):200-207.]
[22] Li Jun,Zhou Yueqin.Wavelet Transform for the Fusion of High Resolution Panchromatic Images and Multispectral Images[J].Journal of Remote Sensing,1999,3(2):116-121.[李军,周月琴.小波变换用于高分辨率全色影像与多光谱影像的融合研究[J].遥感学报,1999,3(2):116-121.]
[23] Li Lin,She Mengyuan,Luo Heng.Comparison of Panchromatic and Multispectral Image Fusion Methods for ZY-3 Satellite[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(16):157-167.[李霖,佘梦媛,罗恒.ZY-3卫星全色与多光谱影像融合方法比较[J].农业工程学报,2014,30(16):157-167.]
[24] Liu Erjia,Luo Zhidong,Zhang Xiaoli,et al.Comparision of Fusion Algorithms for GF-1 Data from Extracted of Distribution Information on Production and Construction Projects[J].Journal of Soil and Water Conservation,2018,32(3):358-363.[刘二佳,罗志东,张晓丽等.面向GF-1数据不同融合方法对扰动地块的提取[J].水土保持学报,2018,32(3):358-363.]
[25] Wang Le.Remote Sensed Imagery Fusion and Quality Evaluation[D].Changchun:Jilin University.2012.[王乐.遥感影像融合及其质量评价[D].长春:吉林大学,2012.]
[26] Loncan L,Almeida L B D,Bioucas D J M,et al.Hyperspectral Pansharpening:A Review[J].IEEE Geoscience & Remote Sensing Magazine,2015,3(3):27-46.
[27] Wang Z,Bovik A C.A Universal Image Quality Index[J].IEEE Signal Processing Letters,2002,9(3):81-84.
[28] Liu Kun,Fu Jingying,Li Fei.Evaluate Study of Four Fusion Methods of GF-1 PAN and Multi-spectral Images[J].Remote Sensing Technology and Application,2015,30(5):980-986.[刘锟,付晶莹,李飞.高分一号卫星4种融合方法评价[J].遥感技术与应用,2015,30(5):980-986.]