北京师范大学学报（自然科学版）2017，Vol.53Issue(6)：727-734,封2-封3,10.DOI:10.16360/j.cnki.jbnuns.2017.06.015
面向地表特征变化区域的时空遥感数据融合方法研究
Fusion of spatiotemporal remotesensing data for changing surface characteristics
摘要
Abstract
High spatiotemporal remote sensing data can be used to describe biophysical and structural characteristics of vegetation and phonological changes,playing an important role in vegetation monitoring.The aim of the present work was to improve fusion-data performance in areas where land surface characteristics changes occur in different directions and to propose an easy and efficient method for spatiotemporal data fusion:Downscaling Difference Spatial and Temporal Data Fusion Algorithm (DDSTDFA),to fuse Landsat 8 OLI fine-resolution images and MODIS/NOAA coarse-resolution images.This method was compared with STDFA and FSDAF.DDSTDFA algorithm can predict changes in surface characteristics occurring in different directions simultaneously,and improve defects of un-mixing based algorithms.Compared with STDFA,DDSTDFA showed higher accuracies for different areas with changing land-surface characteristics.DDSTDFA-predicted images resembled real images by visual analysis.Block and patch effects found in previous methods were effectively avoided.Compared with FSDAF algorithm,efficiency of DDSTDFA was improved by 50-60％ with high accuracy.DDSTDFA is therefore more suitable for wide-range high spatiotemporal image datafusion,this will provide a wealth of remote-sensing image data sources for land surface dynamic monitoring.关键词
高时、空分辨率影像融合/降尺度/薄板样条插值Key words
high spatiotemporal data fusion/downscaling/thin plate spline (TPS)分类
信息技术与安全科学引用本文复制引用
袁周米琪,张锦水..面向地表特征变化区域的时空遥感数据融合方法研究[J].北京师范大学学报（自然科学版）,2017,53(6):727-734,封2-封3,10.基金项目
国家重点研发计划“粮食丰产增效科技创新专项子课题”资助项目(2017YFD0300402-6) （2017YFD0300402-6）
