Pan-Sharpening via Coupled Unitary Dictionary Learning

Shumiao Chen, Liang Xiao, Zhihui Wei, Wei Huang

Abstract: In this paper, we propose a new pan-sharpening method by coupled unitary dictionary learning and clustered sparse representation. First, we randomly sample image patch pairs from the training images exclude the smooth patches, and divide these patch pairs into different groups by K-means clustering. Then, we learn sub-dictionaries offline from corresponding group patch pairs. Particularly, we use the principal component analysis (PCA) technique to learn sub-dictionaries. For a given LR MS patch, we adaptively select one sub-dictionary to reconstruct the HR MS patch online. Experiments show that the proposed method produces images with higher spectral resolution while maintaining the high-quality spatial resolution and gives better visual perception compared with the conventional methods.