融合HJ-1 CCD和MODIS数据生成高分辨率影像方法对比
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摘要
高时空分辨率遥感影像的反演可有效解决南方云雨地区的数据缺失问题。以广西典型丘陵山地为试验区,利用时空自适应反射率融合模型(spatial and temporal adaptive reflectance fusion model,STRAFM)和增强型时空自适应反射率融合模型(enhanced spatial and temporal adaptive reflectance fusion model,ESTRAFM)两种融合算法,选取小范围的国产环境减灾卫星(HJ-1 CCD)和中分辨率成像光谱仪(moderate-resolution imaging spectroradiometer,MODIS)数据,比较分析两种融合算法所生成的高空间分辨率影像的优劣。与真实HJ-1 CCD的红、近红外(near-infrared,NIR)波段影像相比,STRAFM和ESTRAFM预测影像在空间分布上均具有较好的一致性,R值均为极显著相关,差分图像98. 94%以上像元反射率差值小于0. 1,平均绝对差值(average absolute difference,AAD)、平均差值(average difference,AD)、标准差(standard deviation,SD)均较小,融合效果好。与STRAFM相比较,ESTRAFM对真实HJ-1 CCD影像的细节捕捉能力更强,高低反射率区域没有明显缩小或放大现象,破碎地物边界更清晰,不存在斑块。ESTRAFM预测影像与真实HJ-1 CCD红、近红外波段影像的相关性均高于STRAFM,相关系数(pearson correlation coefficient,R)分别为0. 930、0. 885。ESTRAFM预测影像与真实HJ-1 CCD影像差异小于STRAFM,其差分影像的AD、AAD、SD分别为-0. 005、0. 013、0. 017。
Inversion of high temporal-spatial resolution remote sensing data can effectively solve the problem of missing data due to the cloudy weather in the southern China. Taking Guangxi typical hilly area as the research area,using Chinese HJ-1 CCD and moderate-resolution imaging spectroradiometer( MODIS) data,two fusion models include moderate-resolution imaging spectroradiometer( STRAFM) and enhanced spatial and temporal adaptive reflectance fusion model( ESTRAFM) for generating high spatial-temporal resolution data were compared. The results show that,compared to the actual HJ-1 CCD images,there was a good consistency in spatial distribution for prediction red and NIR band images by STRAFM and ESTRAFM. Correlation coefficients between real images and prediction images were high and significant. The reflectance values of the difference image were less than 0. 1 for more than 99. 94% pixels. The absolute difference( AD),average absolute difference( AAD) and standard deviation( SD) were all very small. In short,the fusion effect was good for both STRAFM and ESTRAFM. However,compared with STRAFM,ESTRAFM fusion algorithm can capture more details on real HJ-1 CCD image. There was no significant reduction or magnification for high or low reflectance regions in prediction images by ESTRAFM. Besides,boundary of broken object was more clearly. Moreover,no blocks existed on the ESTRAFM prediction images. For the ESTRAFM predicted red and NIR band images,R was 0. 930 and 0. 885 respectively,smaller than those of the STRAFM. The difference between the real images and ESTRAFM predicted images were also smaller than that of the STRAFM,with its AD,AAD and SD are-0. 005,0. 013 and 0. 017 respectively.
Inversion of high temporal-spatial resolution remote sensing data can effectively solve the problem of missing data due to the cloudy weather in the southern China. Taking Guangxi typical hilly area as the research area,using Chinese HJ-1 CCD and moderate-resolution imaging spectroradiometer( MODIS) data,two fusion models include moderate-resolution imaging spectroradiometer( STRAFM) and enhanced spatial and temporal adaptive reflectance fusion model( ESTRAFM) for generating high spatial-temporal resolution data were compared. The results show that,compared to the actual HJ-1 CCD images,there was a good consistency in spatial distribution for prediction red and NIR band images by STRAFM and ESTRAFM. Correlation coefficients between real images and prediction images were high and significant. The reflectance values of the difference image were less than 0. 1 for more than 99. 94% pixels. The absolute difference( AD),average absolute difference( AAD) and standard deviation( SD) were all very small. In short,the fusion effect was good for both STRAFM and ESTRAFM. However,compared with STRAFM,ESTRAFM fusion algorithm can capture more details on real HJ-1 CCD image. There was no significant reduction or magnification for high or low reflectance regions in prediction images by ESTRAFM. Besides,boundary of broken object was more clearly. Moreover,no blocks existed on the ESTRAFM prediction images. For the ESTRAFM predicted red and NIR band images,R was 0. 930 and 0. 885 respectively,smaller than those of the STRAFM. The difference between the real images and ESTRAFM predicted images were also smaller than that of the STRAFM,with its AD,AAD and SD are-0. 005,0. 013 and 0. 017 respectively.
引文
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8蔡德文,牛铮,王力.遥感数据时空融合技术在农作物监测中的适应性研究.遥感技术与应用,2012;27(6):927-932Cai Dewen,Niu Zheng,Wang Li.Adaptability research of spatial and temporal remote sensing data fusion technology in crop monitoring.Remote Sensing Technology and Application,2012;27(6):927-932
9邬明权,王长耀,牛铮.利用多源时序遥感数据提取大范围水稻种植面积.农业工程学报,2010;26(7):240-244Wu Mingquan,Wang Changyao,Niu Zheng.Mapping paddy fields in large areas,based on time series multi-sensors data.Transactions of the CSAE,2010;26(7):240-244
10邬明权,牛铮,王长耀.多源遥感数据时空融合模型应用分析.地球信息科学学报,2014;16(5):776-783Wu Mingquan,Niu Zheng,Wang Changyao.Assessing the accuracy of spatial and temporal image fusion model of complex area in south China.Journal of Geo-Information Science,2014;16(5):776-783
11张猛,曾永年.基于多时相Landsat数据融合的洞庭湖区水稻面积提取.农业工程学报,2015;31(13):178-185Zhang Meng,Zeng Yongnian.Mapping paddy fields of Dongting Lake area by fusing Landsat and MODIS data.Transactions of the CSAE,2015;31(13):178-185
12赵艳丽,李大成,贾明,等.STARFM算法生成湿地类型TM反射率数据的应用评价.计算机应用与软件,2016;33(3):267-270Zhao Yanli,Li Dacheng,Jia Ming,et al.Application evaluation of STARFM algorithm in generating wetland-type TM reflectance data.Computer Applications and Software,2016;33(3):267-270
13邬明权,王洁,牛铮,等.融合MODIS与Landsat数据生成高时间分辨率Landsat数据.红外与毫米波学报,2012;31(1):80-84Wu Mingquan,Wang Jie,Niu Zheng,et al.A model for spatial and temporal data fusion.Journal of Infrared and Millimeter Waves,2012;31(1):80-84
14石月婵,杨贵军,李鑫川,等.融合多源遥感数据生成高时空分辨率数据的方法对比.红外与毫米波学报,2015;34(1):92-99Shi Yuechan,Yang Guijun,Li Xinchuan,et al.Intercomparison of different fusion methods for generating high spatial-temporal resolution data.Journal of Infrared and Millimeter Waves,2015;34(1):92-99
15李玉东,黄永喜.三种TM与MODIS数据融合方法在山区的适用性研究.测绘与空间地理信息,2013;(9):124-127Li Yudong,Huang Yongxi.Application research of three fusion data methods of TM and MODIS in mountain areas.Geomatics&Spatial Information Technology,2013;(9):124-127
16孙锐,荣媛,苏红波,等.MODIS和HJ-1CCD数据时空融合重构NDVI时间序列.遥感学报,2016;20(3):361-373Sun Rui,Rong Yuan,Su Hongbo,et al.NDVI time-series reconstruction based on MODIS and HJ-1 data spatial-temporal fusin.Journal of Remote Sensing,2016;20(3):361-373
2柳文祎,何国金,张兆明,等.ALOS全色波段与多光谱影像融合方法的比较研究.科学技术与工程,2008;8(11):2864-2869Liu Wenyi,He Guojin,Zhang Zhaoming,et al.Comparison of fusion algorithms for ALOS panchromatic and multispectral images.Science Technology and Engineering,2008;8(11):2864-2869
3刘建波,马勇,武易天,等.遥感高时空融合方法的研究进展及应用现状.遥感学报,2016;20(5):1038-1049Liu Jianbo,Ma Yong,Wu Yitian,et al.Review of methods and applications of high spatiotemporal fusion of remote sensing data.Journal of Remote Sensing,2016;20(5):1038-1049
4 Gao F,Masek J,Schwaller M,et al.On the blending of the Landsat and MODIS surface reflectance:Predicting daily Landsat surface reflectance.IEEE Transactions on Geoscience&Remote Sensing,2006;44(8):2207-2218
5 Zhu X,Chen J,Gao F,et al.An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions.Remote Sensing of Environment,2010;114(11):2610-2623
6 Walker J J,Beurs K M D,Wynne R H.Dryland vegetation phenology across an elevation gradient in Arizona,USA,investigated with fused MODIS and Landsat data.Remote Sensing of Environment,2014;144(1):85-97
7 Chen B,Ge Q,Fu D,et al.A data-model fusion approach for upscaling gross ecosystem productivity to the landscape scale based on remote sensing and flux footprint modelling.Biogeosciences,2010;7(9):2943-2958
8蔡德文,牛铮,王力.遥感数据时空融合技术在农作物监测中的适应性研究.遥感技术与应用,2012;27(6):927-932Cai Dewen,Niu Zheng,Wang Li.Adaptability research of spatial and temporal remote sensing data fusion technology in crop monitoring.Remote Sensing Technology and Application,2012;27(6):927-932
9邬明权,王长耀,牛铮.利用多源时序遥感数据提取大范围水稻种植面积.农业工程学报,2010;26(7):240-244Wu Mingquan,Wang Changyao,Niu Zheng.Mapping paddy fields in large areas,based on time series multi-sensors data.Transactions of the CSAE,2010;26(7):240-244
10邬明权,牛铮,王长耀.多源遥感数据时空融合模型应用分析.地球信息科学学报,2014;16(5):776-783Wu Mingquan,Niu Zheng,Wang Changyao.Assessing the accuracy of spatial and temporal image fusion model of complex area in south China.Journal of Geo-Information Science,2014;16(5):776-783
11张猛,曾永年.基于多时相Landsat数据融合的洞庭湖区水稻面积提取.农业工程学报,2015;31(13):178-185Zhang Meng,Zeng Yongnian.Mapping paddy fields of Dongting Lake area by fusing Landsat and MODIS data.Transactions of the CSAE,2015;31(13):178-185
12赵艳丽,李大成,贾明,等.STARFM算法生成湿地类型TM反射率数据的应用评价.计算机应用与软件,2016;33(3):267-270Zhao Yanli,Li Dacheng,Jia Ming,et al.Application evaluation of STARFM algorithm in generating wetland-type TM reflectance data.Computer Applications and Software,2016;33(3):267-270
13邬明权,王洁,牛铮,等.融合MODIS与Landsat数据生成高时间分辨率Landsat数据.红外与毫米波学报,2012;31(1):80-84Wu Mingquan,Wang Jie,Niu Zheng,et al.A model for spatial and temporal data fusion.Journal of Infrared and Millimeter Waves,2012;31(1):80-84
14石月婵,杨贵军,李鑫川,等.融合多源遥感数据生成高时空分辨率数据的方法对比.红外与毫米波学报,2015;34(1):92-99Shi Yuechan,Yang Guijun,Li Xinchuan,et al.Intercomparison of different fusion methods for generating high spatial-temporal resolution data.Journal of Infrared and Millimeter Waves,2015;34(1):92-99
15李玉东,黄永喜.三种TM与MODIS数据融合方法在山区的适用性研究.测绘与空间地理信息,2013;(9):124-127Li Yudong,Huang Yongxi.Application research of three fusion data methods of TM and MODIS in mountain areas.Geomatics&Spatial Information Technology,2013;(9):124-127
16孙锐,荣媛,苏红波,等.MODIS和HJ-1CCD数据时空融合重构NDVI时间序列.遥感学报,2016;20(3):361-373Sun Rui,Rong Yuan,Su Hongbo,et al.NDVI time-series reconstruction based on MODIS and HJ-1 data spatial-temporal fusin.Journal of Remote Sensing,2016;20(3):361-373