Hyperspectral and multispectral data fusion via nonlocal low-rank learning

Low-spatial-resolution hyperspectral images have rich spectral information and poor spatial resolution, whereas high-spatial-resolution multispectral images have high spatial resolution and limited spectral information. Current methods cannot simultaneously consider the structure of spatial and spectral domains of an hyperspectral image cube. To solve this problem, we propose a fusion model based on spatial nonlocal similarity and low-rank prior learning. The proposed method first extracts a series of fixed-size, full-band tensor blocks, select their corresponding reference blocks to search similar full-band blocks in its search window, list similar blocks to form tensor groups, and then use a four-dimensional tensor structure to extract local full-band blocks. Next, a low-rank regularization term is introduced to the fusion model. Finally, the fusion problem is transformed to a low-rank regularization optimization problem, which is solved by the alternating direction multiplier method. Comparison with state-of-the-art methods demonstrates the method’s effectiveness.