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 hyperspectral c...     »