Optimization-Based Hyperspectral Spatiotemporal Super-Resolution

Due to hardware limitations and financial considerations, it is challenging to acquire fine spatial and temporal resolution (FSFT) images, which leads to the interest in spatiotemporal fusion problems. Instead of directly obtaining costly FSFT images, an alternative is to fuse fine spatial, coarse temporal resolution (FSCT) images with coarse spatial, fine temporal resolution (CSFT) images. Unlike existing spatiotemporal methods which are only designed for multispectral images, this paper first proposes a new fusion framework for hyperspectral spatiotemporal super-resolution, termed HSTSR. In this paper, we first deal with the coarse temporal resolution issue by adopting the fast iterative shrinkage-thresholding algorithm (FISTA) to estimate the missing images at the intermediate time series. Then, we fuse the hyperspectral image and the multispectral image in each time series via coupled nonnegative matrix factorization (CNMF) to get FSFT hyperspectral images. Importantly, we can automatically estimate the associated spatial blurring and spectral downsampling matrices without prior satellite hardware information. Compared with other extended multispectral spatiotemporal methods, our method not only attains satisfying qualities significantly faster, but also requires much less input data.