An Auto-Adjusted Kernel Method for Thermal Sharpening With Local and Object-Based Window Strategies

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Qiang Chen¹  Long Liang²  Jing Li²  Yunhao Chen²  Haiping Xia² 

[1] Beijing University of Civil Engineering and Architecture, Beijing, China;State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Faculty of Geographical Science, Beijing Normal University, Beijing, China;

关键词: Land surface temperature (LST);    local window strategy (LWS);    object-based window strategy (OWS);    remote sensing;    thermal sharpening;   

DOI  :  10.1109/JSTARS.2021.3067349

来源: DOAJ

【 摘 要 】

Thermal sharpening expands the application of land surface temperature due to the trade-off between spatial and temporal resolutions. Fixed kernels (FK) are widely applied in two window strategies: local window strategies (LWS) and object-based window strategies (OWS). However, the fixed regression kernel effect on LWS and OWS has rarely been considered in existing studies. Due to the heterogeneity among different windows, it is important to select suitable kernels for each window either in OWS or LWS. This article presents an auto-adjusted kernel (AAK) method to resolve this issue and examines certain simple kernel selection rules in thermal sharpening aiming to balance accuracy and efficiency. We test the AAK method with Landsat 8 data and compare it to the FK method with both OWS and LWS. The results reveal that the AAK method generally performs better than the FK method. Compared to the FK method, the AAK method improves the OWS accuracy by 0.283 K on average at three downscaling ratios, and the accuracy improvement increases with increasing downscaling ratio (from 3 to 9). Especially when the downscaling ratio reaches to 9, there is an evident improvement with 0.425 K of AAK. Moreover, the AAK method enhances the mean LWS accuracy by 0.179 K overall and decreases the difference between OWS and LWS. Furthermore, the AAK method increases the accuracy in specific areas and reduces extreme-value points. These findings indicate the potential of the AAK method in thermal sharpening with OWS and LWS, which resolves kernel selection problems.

【 授权许可】

Unknown