葛艳琴;李彦荣;孙康;李大成;陈永红;李瑄;

• 1:太原理工大学
• 2:中国电子科技集团公司第五十四研究所
• 3:高分辨率对地观测系统山西数据与应用中心

摘要(Abstract)：

针对如何在时间序列尺度上利用多源时空融合方法高精度地重构高分辨率遥感影像的问题,该文提出了一种基于增强字典学习样本空间的单数据对稀疏学习融合算法,并利用现有稀疏学习算法、STARFM算法以及半物理模型对Landsat与MODIS卫星数据进行双向融合实验。结果表明:随着样本尺寸及空间的拓展,改进后的稀疏学习算法能够获得比原始算法、STARFM、半物理模型等算法更优的融合结果,其中ERGAS可达15.0以内、SSIM可达84%以上,并且融合质量对高、低分辨率图像间的空间尺度差异性不敏感。通过采用更高效的在线字典学习算法,该融合方法的处理效率与应用价值有望得到极大提升。

关键词(KeyWords)： 稀疏学习;字典训练;陆地卫星数据;地表反射率;时空融合

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(41501372);; 山西省高等学校科技创新项目(2016144)

作者(Authors): 葛艳琴;李彦荣;孙康;李大成;陈永红;李瑄;

DOI: 10.16251/j.cnki.1009-2307.2019.09.015

参考文献(References)：

• [1] WENG Q,FU P,GAO F.Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data[J].Remote Sensing of Environment,2014,145:55-67.
• [2] MENG J,DU X,WU B.Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation[J].International Journal of Digital Earth,2013,6(3):203-218.
• [3] CHEN B,HUANG B,XU B.Comparison of spatiotemporal fusion models:a review[J].Remote Sensing,2015,7(2):1798-1835.
• [4] CARPER W,LILLESAND T,KIEFER R.The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data[J].Photogrammetric Engineering and Remote Sensing,1990,56(4):459-467.
• [5] YESOU H,BESNUS Y,ROLET J.Extraction of spectral information from Landsat TM data and merger with SPOT panchromatic imagery-a contribution to the study of geological structures[J].ISPRS Journal of Photogrammetry and Remote Sensing,1993,48(5):23-36.
• [6] YOCKY D A.Multiresolution wavelet decomposition image merger of Landsat thematic mapper and SPOT panchromatic data[J].Photogrammetric Engineering &Remote Sensing,1996,62(9):1067-1074.
• [7] CHERCHALI S,AMRAM O,FLOUZAT G.Retrieval of temporal profiles of reflectances from simulated and real NOAA-AVHRR data over heterogeneous landscapes[J].International Journal of Remote Sensing,2000,21(4):753-775.
• [8] FORTIN J P,BERNIER M,LAPOINTE S,et al.Estimation of surface variables at the sub-pixel level for use as input to climate and hydrological models[M].INRS-Eau,1998.
• [9] GAO F,MASEK J,SCHWALLER M,et al.On the blending of the Landsat and MODIS surface reflectance:Predicting daily Landsat surface reflectance[J].IEEE Transactions on Geoscience and Remote Sensing,2006,44(8):2207-2218.
• [10]ZHU X,CHEN J,GAO F,et al.An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions[J].Remote Sensing of Environment,2010,114(11):2610-2623.
• [11]ROY D P,JU J,LEWIS P,et al. Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization,gap filling,and prediction of Landsat data[J].Remote Sensing of Environment,2008,112(6):3112-3130.
• [12]李大成,唐娉,胡昌苗,等.一种拓展的半物理时空融合算法及其初步应用[J].遥感学报,2014,18(2):307-319.(LI Dacheng,TANG Ping,HU Changmiao,et al.Spatial-temporal fusion algorithm based on an extended semi-physical model and its preliminary application[J].Journal of Remote Sensing,2014,18(2):307-319.)
• [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[J].红外与毫米波学报,2012,31(1):80-84.(WU Mingquan,WANG Jie,NIU Zheng,et al.A model for spatial and temporal data fusion[J].Journal of Infrared and Millimeter Waves,2012,31(1):80-84.)
• [14]SONG H,HUANG B.Spatiotemporal satellite image fusion through one-pair image learning[J].IEEE Transactions on Geoscience and Remote Sensing,2013,51(4):1883-1896.
• [15]HUANG B,SONG H.Spatiotemporal reflectance fusion via sparse representation[J].IEEE Transactions on Geoscience and Remote Sensing,2012,50(10):3707-3716.
• [16]YANG J,WRIGHT J,HUANG T S,et al.Image super-resolution via sparse representation[J].IEEE Transactions on Image Processing,2010,19(11):2861-2873.
• [17]WANG Z,BOVIK A C,SHEIKH H R,et al.Image quality assessment:from error visibility to structural similarity[J].IEEE Transactions on Image Processing,2004,13(4):600-611.
• [18]RENZA D,MARTINEZ E,ARQUERO A.A new approach to change detection in multispectral images by means of ERGAS index[J].IEEE Geoscience and Remote Sensing Letters,2013,10(1):76-80.