A two-step pansharpening of ETM+ TIR image based on SFIM and neural network regression

A two-step approach to enhance the resolution of remote sensing thermal infrared (TIR) images is proposed in this paper. For difference in imaging principles between TIR image and optical images, traditional image fusion techniques, such as component substation and MRA methods will not be proper. In our study, we use extreme learning machine (ELM) to regress the relationship between TIR image and optical images, then pansharpened multi spectral images are inputted to the already trained ELM network to produce TIR image at resolution of the panchromatic image. Since the approach considers directly about radiance values in a TIR image, the result can be conveniently used in physical applications, for example, creating more precise temperature distribution of ground surface.