  Hyperspectral imaging (HSI) is a key technology for earth observation,
surveillance, medical imaging and diagnostics, astronomy and space exploration.
The conventional technology for HSI in remote sensing applications is based on
the push-broom scanning approach in which the camera records the spectral image
of a stripe of the scene at a time, while the image is generated by the
aggregation of measurements through time. In real-world airborne and spaceborne
HSI instruments, some empty stripes would appear at certain locations, because
platforms do not always maintain a constant programmed attitude, or have access
to accurate digital elevation maps (DEM), and the travelling track is not
necessarily aligned with the hyperspectral cameras at all times. This makes the
enhancement of the acquired HS images from incomplete or corrupted observations
an essential task. We introduce a novel HSI inpainting algorithm here, called
Hyperspectral Equivariant Imaging (Hyper-EI). Hyper-EI is a self-supervised
learning-based method which does not require training on extensive datasets or
access to a pre-trained model. Experimental results show that the proposed
method achieves state-of-the-art inpainting performance compared to the
existing methods.
