Name
Title | ||
|---|---|---|
A Combination of TsHARP and Thin Plate Spline Interpolation for Spatial Sharpening of Thermal Imagery |
Abstract | ||
|---|---|---|
There have been many studies and much attention paid to spatial sharpening for thermal imagery. Among them, TsHARP, based on the good correlation between vegetation index and land surface temperature (LST), is regarded as a standard technique because of its operational simplicity and effectiveness. However, as LST is affected by other factors (e.g., soil moisture) in the areas with low vegetation cover, these areas cannot be well sharpened by TsHARP. Thin plate spline (TPS) is another popular downscaling technique for surface data. It has been shown to be accurate and robust for different datasets; however, it has not yet been attempted in thermal sharpening. This paper proposes to combine the TsHARP and TPS methods to enhance the advantages of each. The spatially explicit errors of these two methods were firstly estimated in theory, and then the results of TPS and TsHARP were combined with the estimation of their errors. The experiments performed across various landscapes and data showed that the proposed combined method performs more robustly and accurately than TsHARP. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.3390/rs6042845 | REMOTE SENSING |
Keywords | Field | DocType |
thermal sharpening,land surface temperature,TsHARP,thin plate spline,error estimation | Sharpening,Land surface temperature,Downscaling,Computer vision,Thermal,Thin plate spline,Vegetation Index,Remote sensing,Interpolation,Artificial intelligence,Geology,Vegetation cover | Journal |
Volume | Issue | Citations |
6 | 4 | 8 |
PageRank | References | Authors |
0.61 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuehong Chen | 1 | 47 | 11.12 |
| Wentao Li | 2 | 153 | 7.66 |
| Jin Chen | 3 | 259 | 31.87 |
| yuhan rao | 4 | 21 | 2.70 |
| Yasushi Yamaguchi | 5 | 130 | 21.77 |