改进的三层分解模型热红外影像空间降尺度研究
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摘要
地表温度(Land surface temperature,LST)是地-气相互作用和能量交换的重要参数之一.为了获取高空间分辨率地表温度数据,研究改进了一种热红外遥感数据降尺度方法,并以上海市Landsat8 OLI/TIRS影像为数据源进行了实验验证,归一化植被指数(Normalized Difference Vegetation Index,NDVI)被分解为低频层、边缘层和细节层,其中边缘层和细节层按比例增加到热红外数据中.并与经典的热红外降尺度方法 Dis Trad算法和Ts HARP算法作为对比,将模拟的地表温度(270 m)作为降尺度数据源实现LST降尺度(90 m).实验结果表明,三种降尺度方法都保留原有的地表温度的空间特征,但Dis Trad算法和Ts HARP算法增加了真实数据中并不存在的温度差异;改进的三层分解模型地表温度的均方根误差为0. 913 K,与Dis Trad方法和Ts HARP算法相比精度分别提高了0. 937 K和0. 832K.
Land surface temperature is one of the important parameters of geogas interaction and energy exhange. In order to obtain the land surface temperature data with high spatial resolution,this research improved a method of downscaling thermal infrared remote image,and was verified using Shanghai Landsat 8 OLI/TIRS image as the data source. The Normalized Difference Vegetation Index( NDVI)was decomposed into lowfrequency layer,edge layer and detail layer,in which edge layer and detail layer are scaled up to the thermal infrared data. The proposed algorithm used simulated LST( 270 m) as a downscaling data source to achieve downscaling LST( 90 m),and compared with the classical thermal infrared downscaling method DisTrad algorithm and TsHARP algorithm. The results showthat all three downscaling methods preserve the spatial characteristics of the original land surface temperature,but the DisTrad algorithm and the TsHARP algorithm add the detailed information that does not exist in original land surface temperature data. The improved three-layers decomposition model has a root mean square error of 0. 913 K,which is 0. 937 K and 0. 832 K higher than the DisTrad method and the TsHARP method.
Land surface temperature is one of the important parameters of geogas interaction and energy exhange. In order to obtain the land surface temperature data with high spatial resolution,this research improved a method of downscaling thermal infrared remote image,and was verified using Shanghai Landsat 8 OLI/TIRS image as the data source. The Normalized Difference Vegetation Index( NDVI)was decomposed into lowfrequency layer,edge layer and detail layer,in which edge layer and detail layer are scaled up to the thermal infrared data. The proposed algorithm used simulated LST( 270 m) as a downscaling data source to achieve downscaling LST( 90 m),and compared with the classical thermal infrared downscaling method DisTrad algorithm and TsHARP algorithm. The results showthat all three downscaling methods preserve the spatial characteristics of the original land surface temperature,but the DisTrad algorithm and the TsHARP algorithm add the detailed information that does not exist in original land surface temperature data. The improved three-layers decomposition model has a root mean square error of 0. 913 K,which is 0. 937 K and 0. 832 K higher than the DisTrad method and the TsHARP method.
引文
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[18]LI Miao-Miao.The method of vegetation fraction estimation by remote sensing[D].Graduate University of Chinese A-cademy of Sciences(Institute of Remote Sensing Application)2003.(李苗苗.植被覆盖度的遥感估算方法研究.中国科学院研究生院(遥感应用研究所),2003.
[19]He K,Sun J,Tang X.Guided image filtering[C]//European conference on computer vision.Springer,Berlin,Heidelberg,2010:1-14.
[2]Kustas W P,Norman J M,Anderson M C,et al.Estimating subpixel surface temperatures and energy fluxes from the vegetation index-radiometric temperature relationship[J].Remote sensing of Environment,2003,85(4):429-440.
[3]Agam N,Kustas W P,Anderson M C,et al.A vegetation index based technique for spatial sharpening of thermal imagery[J].Remote Sens.Environ.2007,107:545-558.
[4]Guo L J,Moore J M M.Pixel block intensity modulation:adding spatial detail to TM band 6 thermal imagery[J].International Journal of Remote Sensing,1998,19(13):2477-2491.
[5]Stathopoulou M,Cartalis C.Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation[J].Remote Sensing of Environment,2009,113(12):2592-2605.
[6]Dominguez A,Kleissl J,Luvall J C,et al.High-resolution urban thermal sharpener(HUTS)[J].Remote Sensing of Environment,2011,115(7):1772-1780.
[7]Essa W,Verbeiren B,van der Kwast J,et al.Evaluation of the Dis Trad thermal sharpening methodology for urban areas[J].International Journal of Applied Earth Observation and Geoinformation,2012,19:163-172.
[8]Rodriguez-Galiano V,Pardo-Igúzquiza E,Sanchez-Castillo M,et al.Downscaling Landsat 7 ETM+thermal imagery using land surface temperature and NDVI images[J].International Journal of Applied Earth Observation and Geoinformation,2012,18:515-527.
[9]Moosavi V,Talebi A,Mokhtari M H,et al.A wavelet-artificial intelligence fusion approach(WAIFA)for blending Landsat and MODIS surface temperature[J].Remote Sensing of Environment,2015,169:243-254.
[10]Hutengs C,Vohland M.Downscaling land surface temperatures at regional scales with random forest regression[J].Remote Sensing of Environment,2016,178:127-141.
[11]Meng X,Li J,Shen H,et al.Pansharpening with a guided filter based on three-layer decomposition[J].Sensors,2016,16(7):1068.
[12]Bennett E P,Mason J L,McMillan L.Multispectral bilateral video fusion[J].IEEE Trans.Image Proc.2007,16,1185-1194.
[13]Bonafoni S.Downscaling of Landsat and MODIS land surface temperature over the heterogeneous urban area of Milan[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2016,9(5):2019-2027.
[14]Price J C.Using spatial context in satellite data to infer regional scale evapotranspiration[J].IEEE transactions on Geoscience and Remote Sensing,1990,28(5):940-948.
[15]Roy D P,Wulder M,Loveland T R,et al.Landsat-8:Science and product vision for terrestrial global change research[J].Remote Sens.Environ.2014,145:154-172.
[16]LIU Qin-Huo,XU Xi-Ru,CHEN Jia-Yi.The retrieval of land surface tempture and emissivity by remote sensing data:theory and digital simulation[J].Journal of Remote Sensing(柳钦火,徐希孺,陈家宜.遥测地表温度与比辐射率的迭代反演方法─理论推导与数值模拟.遥感学报,1998,1:1-9.
[17]Sobrino J A,Jiménez-Mu1oz J C,Paolini L.Land surface temperature retrieval from LANDSAT TM 5[J].Remote Sensing of Environment,2004,90(4):434-440.
[18]LI Miao-Miao.The method of vegetation fraction estimation by remote sensing[D].Graduate University of Chinese A-cademy of Sciences(Institute of Remote Sensing Application)2003.(李苗苗.植被覆盖度的遥感估算方法研究.中国科学院研究生院(遥感应用研究所),2003.
[19]He K,Sun J,Tang X.Guided image filtering[C]//European conference on computer vision.Springer,Berlin,Heidelberg,2010:1-14.