  Image fusion technology is widely used to fuse the complementary information
between multi-source remote sensing images. Inspired by the frontier of deep
learning, this paper first proposes a heterogeneous-integrated framework based
on a novel deep residual cycle GAN. The proposed network consists of a forward
fusion part and a backward degeneration feedback part. The forward part
generates the desired fusion result from the various observations; the backward
degeneration feedback part considers the imaging degradation process and
regenerates the observations inversely from the fusion result. The proposed
network can effectively fuse not only the homogeneous but also the
heterogeneous information. In addition, for the first time, a
heterogeneous-integrated fusion framework is proposed to simultaneously merge
the complementary heterogeneous spatial, spectral and temporal information of
multi-source heterogeneous observations. The proposed heterogeneous-integrated
framework also provides a uniform mode that can complete various fusion tasks,
including heterogeneous spatio-spectral fusion, spatio-temporal fusion, and
heterogeneous spatio-spectral-temporal fusion. Experiments are conducted for
two challenging scenarios of land cover changes and thick cloud coverage.
Images from many remote sensing satellites, including MODIS, Landsat-8,
Sentinel-1, and Sentinel-2, are utilized in the experiments. Both qualitative
and quantitative evaluations confirm the effectiveness of the proposed method.
