Data provided by most Earth observation satellites possess either high spatial or high spectral resolution. A high spatial resolution allows for accurate geometric analysis and rich spectral information is necessary for thematic interpretation. Therefore, remote sensing applications usually require both. The required solutions are sophisticated data fusion techniques that increase the spatial resolution of the images while introducing negligible spectral distortion. In this talk, a bunch of sparse image fusion (SparseFI) algorithms will be introduced.

This presentation is part of Minisymposium “MS49 - Image Restoration, Enhancement and Related Algorithms (4 parts)”
organized by: Weihong Guo (Case Western Reserve University) , Ke Chen (University of Liverpool) , Xue-Cheng Tai (Hong Kong Baptist University) , Guohui Song (Clarkson University) .

Authors:

Xiaoxiang Zhu (Technical University of Munich (TUM) & German Aerospace Center (DLR))

Claas Grohnfeldt (Technical University of Munich (TUM))

Keywords:

image enhancement, image fusion, sparse representation