Object-Based Window Strategy in Thermal Sharpening
文献类型：期刊论文
| 作者 | Xia, Haiping1; Chen, Yunhao1; Quan, Jinling2; Li, Jing1
|
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2019-03-02 |
| 卷号 | 11期号:6页码:16 |
| 关键词 | segmentation object-based downscaling remote sensing land surface temperature |
| ISSN号 | 2072-4292 |
| DOI | 10.3390/rs11060634 |
| 通讯作者 | Chen, Yunhao(cyh@bnu.edu.cn) |
| 英文摘要 | The trade-off between spatial and temporal resolutions has led to the disaggregation of remotely sensed land surface temperatures (LSTs) for better applications. The window used for regression is one of the primary factors affecting the disaggregation accuracy. Global window strategies (GWSs) and local window strategies (LWSs) have been widely used and discussed, while object-based window strategies (OWSs) have rarely been considered. Therefore, this study presents an OWS based on a segmentation algorithm and provides a basis for selecting an optimal window size balancing both accuracy and efficiency. The OWS is tested with Landsat 8 data and simulated data via the aggregation-then-disaggregation strategy, and compared with the GWS and LWS. Results tested with the Landsat 8 data indicate that the proposed OWS can accurately and efficiently generate high-resolution LSTs. In comparison to the GWS, the OWS improves the mean accuracy by 0.19 K at different downscaling ratios, in particular by 0.30 K over urban areas; compared with the LWS, the OWS performs better in most cases but performs slightly worse due to the increasing downscaling ratio in some cases. Results tested with the simulated data indicate that the OWS is always superior to both GWS and LWS regardless of the downscaling ratios, and the OWS improves the mean accuracy by 0.44 K and 0.19 K in comparison to the GWS and LWS, respectively. These findings suggest the potential ability of the OWS to generate super-high-resolution LSTs over heterogeneous regions when the pixels within the object-based windows derived via segmentation algorithms are more homogenous. |
| WOS关键词 | URBAN HEAT-ISLAND ; SURFACE TEMPERATURES ; SPATIAL-RESOLUTION ; INDEX ; IMAGERY ; FLUXES |
| 资助项目 | National Natural Science Foundation of China[41771448] ; National Natural Science Foundation of China[41571342] ; Project of State Key Laboratory of Earth Surface Processes and Resource Ecology[2017-ZY-03] ; Science and Technology Plans of Ministry of Housing and Urban-Rural Development of the People's Republic of China ; Opening Projects of Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture[UDC2017030212] ; Opening Projects of Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture[UDC201650100] ; Beijing Laboratory of Water Resources Security |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000465615300033 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China ; Project of State Key Laboratory of Earth Surface Processes and Resource Ecology ; Science and Technology Plans of Ministry of Housing and Urban-Rural Development of the People's Republic of China ; Opening Projects of Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture ; Beijing Laboratory of Water Resources Security |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/59542]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Chen, Yunhao |
| 作者单位 | 1.Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xia, Haiping,Chen, Yunhao,Quan, Jinling,et al. Object-Based Window Strategy in Thermal Sharpening[J]. REMOTE SENSING,2019,11(6):16. |
| APA | Xia, Haiping,Chen, Yunhao,Quan, Jinling,&Li, Jing.(2019).Object-Based Window Strategy in Thermal Sharpening.REMOTE SENSING,11(6),16. |
| MLA | Xia, Haiping,et al."Object-Based Window Strategy in Thermal Sharpening".REMOTE SENSING 11.6(2019):16. |
入库方式： OAI收割
来源：地理科学与资源研究所
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