Dictionary Learning Strategies for Sparse Representation Based Hyperspectral Image Enhancement

The fusion of hyperspectral data with a corresponding higher resolution multispectral image has become an increasingly active research field. The goal is to create a hyperspectral image that has the spatial resolution of the multispectral image. This work aims at combining two established fusion algorithms, namely J-{SparseFI}-{HM} and {CNMF}, to a new method which features their individual advantages. The sparse representation based J-{SparseFI}-{HM} algorithm is used to pre-process those hyperspectral channels that have a strong spectral overlap with the multispectral instrument. Then, three modified versions of the matrix factorization and unmixing based {CNMF} method are used for post-processing. The results are assessed and compared to the individual products of J-{SparseFI}-{HM} and {CNMF}.     «

The fusion of hyperspectral data with a corresponding higher resolution multispectral image has become an increasingly active research field. The goal is to create a hyperspectral image that has the spatial resolution of the multispectral image. This work aims at combining two established fusion algorithms, namely J-{SparseFI}-{HM} and {CNMF}, to a new method which features their individual advantages. The sparse representation based J-{SparseFI}-{HM} algorithm is used to pre-process those hyper...     »