A modified flexible spatiotemporal data fusion model

作者：Jia Tang,Jingyu Zeng,Li Zhang,Rongrong Zhang,Jinghan Li,Xingrong Li,Jie Zou,Yue Zeng,Zhanghua Xu,Qianfeng Wang,Qing Zhang

摘要：Remote sensing spatiotemporal fusion models blend multi-source images of different spatial resolutions to create synthetic images with high resolution and frequency,contributing to time series research where high quality observations are not available with sufficient frequency.However,existing models are vulnerable to spatial heterogeneity and land cover changes,which are frequent in human-dominated regions.To obtain quality time series of satellite images in a human-dominated region,this study developed the Modified Flexible Spatial-temporal Data Fusion(MFSDAF)approach based on the Flexible Spatial-temporal Data Fusion(FSDAF)model by using the enhanced linear regression(ELR).Multiple experiments of various land cover change scenarios were conducted based on both actual and simulated satellite images,respectively.The proposed MFSDAF model was validated by using the correlation coefficient(r),relative root mean square error(RRMSE),and structural similarity(SSIM),and was then compared with the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model(ESTARFM)and FSDAF models.Results show that in the presence of significant land cover change,MFSDAF showed a maximum increase in r,RRMSE,and SSIM of 0.0313,0.0109 and 0.049,respectively,compared to FSDAF,while ESTARFM performed best with less temporal difference of in the input images.In conditions of stable landscape changes,the three performance statistics indicated a small advantage of MFSDAF over FSDAF,but were 0.0286,0.0102,0.0317 higher than for ESTARFM,respectively.MFSDAF showed greater accuracy of capturing subtle changes and created high-precision images from both actual and simulated satellite images.

发文机构：Key Laboratory of Remote Sensing of Soil Erosion and Disaster Protection Key Laboratory of Digital Earth Science College of Geography and Tourism College of Geomatics Spatial Information Research Center of Fujian Province Joint Global Change Research Institute

关键词：MFSDAFenhancedlinearregressionlandcoverchangeHETEROGENEOUStime-series

分类号： TP3[自动化与计算机技术—计算机科学与技术]