Hyperspectral image super-resolution extending: An effective fusion based method without knowing the spatial transformation matrix

Hyperspectral image (HSI) super-resolution, a technique to obtain higher (often spatial) resolution image from the original image, has been extensively studied and applied to lots of fields such as computer vision, remote sensing, etc. Though fusion based method has achieved state-of-the-art result, it always assume the spatial transformation matrix is given in advance, whereas such a matrix is actually unknown in reality. An unsuitable given matrix will deteriorate the superresolution result greatly. To address this issue, we propose a novel fusion based HSI super-resolution method without knowing the spatial transformation matrix. Specifically, we incorporate super-resolution and spatial transformation matrix estimation into a unified framework. We alternately estimate the matrix and the higher spatial resolution HSI. We find that without given the spatial transformation matrix, the proposed method can obtain more accurate reconstruction result compared with other competing methods. Experimental results demonstrate the effectiveness of the proposed method.