Multi-Spectral Super-Resolution of Thermal Infrared Data Products for Urban Heat Applications

We evaluate recent developments in deep-learning based Single-Image Super Resolution (SISR) on two multi-spectral datasets in the setting of urban heat analysis. The datasets target a Land Surface Temperature (LST) product and top-of-the-atmosphere (TOA) LWIR radiance, respectively. In doing so, we demonstrate the potential of generative modeling approaches, particularly a Super Resolution Generative Adversarial Network (SRGAN), to increase the spatial resolution of thermal data products. We extend the original SRGAN model with additional bands from the visible optical spectrum to increase the spatial resolution up to four times and estimate the model’s predictive uncertainty. This Multi-Spectral Super Resolution (MSSR) approach yields increases in Peak Signal Noise Ratio (PSNR) of 3dB to 6dB when compared against bicubic upsampling, which is comparable with proceedings in state-of-the-art Multi-Image Super Resolution (MISR) [1]. We further discuss the transferability to other sensors and the limitations of using this approach.