Superresolution Mapping Using Multiple Dictionaries by Sparse Representation

Superresolution mapping can predict the spatial location of land cover classes within mixed pixels based on the spatial dependence assumption. We propose a novel superresolution mapping method via multidictionary-based sparse representation, which is robust to noise in both the learning and class-allocation process. In the proposed method, the subpixel number belonging to each class is obtained according to the degree of spectral distortion, and the distribution modes of different classes are treated discriminatorily. The subpixel classification is performed according to the normalized reconstruction errors by the learned multiple distribution dictionaries. The experimental results show that the proposed method has improved accuracy and robustness for real imagery.