  The development of deep learning-based models for the compression of
hyperspectral images (HSIs) has recently attracted great attention in remote
sensing due to the sharp growing of hyperspectral data archives. Most of the
existing models achieve either spectral or spatial compression, and do not
jointly consider the spatio-spectral redundancies present in HSIs. To address
this problem, in this paper we focus our attention on the High Fidelity
Compression (HiFiC) model (which is proven to be highly effective for spatial
compression problems) and adapt it to perform spatio-spectral compression of
HSIs. In detail, we introduce two new models: i) HiFiC using Squeeze and
Excitation (SE) blocks (denoted as HiFiC$_{SE}$); and ii) HiFiC with 3D
convolutions (denoted as HiFiC$_{3D}$) in the framework of compression of HSIs.
We analyze the effectiveness of HiFiC$_{SE}$ and HiFiC$_{3D}$ in compressing
the spatio-spectral redundancies with channel attention and inter-dependency
analysis. Experimental results show the efficacy of the proposed models in
performing spatio-spectral compression, while reconstructing images at reduced
bitrates with higher reconstruction quality. The code of the proposed models is
publicly available at https://git.tu-berlin.de/rsim/HSI-SSC .
