Super-resolution mapping via multi-dictionary based sparse representation

Based on the spatial dependence assumption, super-resolution mapping can predict the spatial location of land cover classes within mixed pixels. In this paper, we propose a novel super-resolution mapping method via multi-dictionary based sparse representation, which is robust to noise in both the learning and class allocation process. To better distinguish different classes, the distribution modes of different classes are learned separately. A spectral distortion constraint is introduced, combining with reconstruction errors as metrics to perform classification. The experiments prove that our method is superior to other related methods.