An Inproved Up-scaling Algorithm Combined TSA and PSF

2019 2nd International Conference on Advanced Materials, Intelligent Manufacturing and Automation

Bai, Xuejiao^1 ; Sang, Lingzhi^1 ; Bai, Guichen^1 ; Kang, Hongxia^1 ; Liu, Zhen^1

China Transport Telecommunications and Information Center, National Engineering Laboratory for Transportation Safety and Emergency Informatics, Beijing

100011, China^1

关键词: Coarser resolution;    Correlation coefficient;    Drought monitoring;    Root mean square errors;    Structural similarity;    Technical support;    Trend-surface analysis;    Vegetation temperature condition index;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/569/5/052003/pdf
DOI  :  10.1088/1757-899X/569/5/052003

来源: IOP

【 摘 要 】

Taking the Guanzhong Plain of Shanxi Province as the research area, the combine method of trend surface analysis method (TSA) and point spread function (PSF) (TSA+PSF) were used to up-scale the vegetation temperature condition index (VTCI) retrieved from Landsat 8 images (Landsat-VTCI) from a finer resolution to a coarser resolution. The up-scaled results were compared with VTCI images retrieved from Aqua MODIS (MODIS-VTCI) to provide technical support for the comprehensive application of drought monitoring results on two spatial scales. Meanwhile, a range of indicators, such as the semivariogram function (SVF), the structural similarity (SSIM), the correlation coefficients (r), root mean square errors (RMSE) were used to systematically compared the up-scaled methods. The results show that TSA+PSF performed better than TSA in terms of SSIM, the correlation and RMSE, the up-scaling model TSA+PSF has the higher accuracy, and it is more effective and robust than TSA. The model that uses PSF to analyze trend surface constructed by TSA is an improvement for up-scaling Landsat- VTCI images from a finer resolutions to a coarser resolutions.