  Rapid technological advancements have tremendously increased the data
acquisition capabilities of remote sensing satellites. However, the data
utilization efficiency in satellite missions is very low. This growing data
also escalates the cost required for data downlink transmission and
post-processing. Selective data transmission based on in-orbit inferences will
address these issues to a great extent. Therefore, to decrease the cost of the
satellite mission, we propose a novel system design for selective data
transmission, based on in-orbit inferences. As the resolution of images plays a
critical role in making precise inferences, we also include in-orbit
super-resolution (SR) in the system design. We introduce a new image
reconstruction technique and a unique loss function to enable the execution of
the SR model on low-power devices suitable for satellite environments. We
present a residual dense non-local attention network (RDNLA) that provides
enhanced super-resolution outputs to improve the SR performance. SR experiments
on Kaguya digital ortho maps (DOMs) demonstrate that the proposed SR algorithm
outperforms the residual dense network (RDN) in terms of PSNR and
block-sensitive PSNR by a margin of +0.1 dB and +0.19 dB, respectively. The
proposed SR system consumes 48% less memory and 67% less peak instantaneous
power than the standard SR model, RDN, making it more suitable for execution on
a low-powered device platform.
