Spatial resolution improvement of remote sensing images by fusion of subpixel-shifted multi-observation images

Multi-observation of satellite remote sensing provides the ability to achieve a higher spatial resolution image. Based on the relation between sensors of different spatial resolutions, this paper presents a multi-observation in spatial, called subpixel-shifted multi-observation, to acquire a more accurate image of higher spatial resolution than the original observations. In this kind of observation, the same area on the ground is observed repeatedly with a spatial resolution in a subpixel shifted way. All the acquired observation images are combined into a higher resolution image. This is formulated as a super-resolution equation. When comparing the existing super-resolution algorithms, we find that the Iterative Back-Projection (IBP) method suggested by Peleg et al. is an appropriate and effective method for solving this problem. Based on IBP, a pratical implementation is presented. Computer experiments on remote sensing images and error analysis show its effectiveness. Some problems, such as back-projection, undersampling, and fusion of observed samples, are discussed further. The resultant image from this method has both better quality and higher spatial resolution than the original observation.