Improving the Spatial Resolution of Thermal Images by using SFIM and T-Sharp-Dis-Trade Techniques to Investigate Land Surface Temperature

Investigating the relationship between land surface temperature and urban land uses can be used for urban management. However, one of the main problems in this field is the low spatial resolution of thermal images. This research aims to evaluate and select the best existing algorithm for achieving a high spatial resolution of thermal images to investigate and analyze changes in land surface temperature in Region 4 of Ahvaz.

For this purpose, the split window algorithm was used as one of the most common suitable algorithms to calculate land surface temperature, and SFIM and T sharp DisTrade algorithms in urban areas were applied to improve spatial resolution.

Results show that the spatial resolution of the output image obtained by Split Window, T Sharp DisTrade, and SFIM algorithms is 30, 100, and 45 meters, respectively. The T Sharp DisTrade algorithm presented the output images with very good resolution so that different land uses could be separated according to their surface temperature. Split Window and SFIM algorithms did not provide acceptable results in land use evaluation. Also, the average land surface temperature values obtained from T Sharp DisTrade, Split Window algorithm, and SFIM are equal to 17.5, 23.5, and 28.25 degrees Celsius, respectively. This temperature difference of these algorithms is due to utilizing the fusion process.

As a result, T Sharp DisTrade algorithm was more effective in improving the spatial resolution of thermal images. 

Innovations of this research are: - simultaneous use of three mentioned algorithms for increasing spatial resolution of thermal images and discovering the best algorithm in this field, which has not been investigated in previous research, - improving spatial resolution of thermal images for evaluating urban land uses by using T Sharp DisTrade algorithm, and detail investigation of surface temperature changes.