Algorithm for Spectral-Spatial Remote Sensing Image Super-Resolution: Multi-Sensor Case

Multi-sensor remote sensing image super-resolution aims to provide better characteristics for different types of resolution and compensate the limitations of the particular imaging systems. However, existing super-resolution techniques consider spectral and spatial resolution enhancement separately, i.e. only spatial or only spectral resolution can be enhanced. Among spatial super-resolution methods maximum a posteriori estimation approach with B-TV regularization stands out as one of the best method for spatial resolution enhancement. But existing implementations were designed only for RGB and grayscale photographic imagery. Unlike photographic RGB imagery, multispectral remote sensing images captured by optical sensors often contain more than three spectral channels (red, green and blue) and, moreover, different remote sensing systems produce a different spectral response for the similar spectral components. Therefore, a more complex image acquisition model should be regarded to take into account the variations in bandwidth and number of spectral channels in the case of remote sensing images. In this article, we propose an algorithm aiming to provide spectral-spatial multi-sensor remote sensing image super-resolution. We apply a joint spectral-spatial image acquisition model, that is typical for remote sensing systems, and investigate the super-resolution algorithm streaming from this model and the maximum a posteriori estimation approach with B-TV regularization. We propose a simple way to adapt B-TV regularization in the case of multiple spectral channels. Our experimental results confirm the enhancement in the spectral and spatial resolution of the output image in comparison with the input images. The results of our research demonstrate that the proposed method achieves both spectral and spatial super-resolution.