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Rapid technological advancements have tremendously increased the dataacquisition capabilities of remote sensing satellites. However, the datautilization efficiency in satellite missions is very low. This growing dataalso escalates the cost required for data downlink transmission andpost-processing. Selective data transmission based on in-orbit inferences willaddress these issues to a great extent. Therefore, to decrease the cost of thesatellite mission, we propose a novel system design for selective datatransmission, based on in-orbit inferences. As the resolution of images plays acritical role in making precise inferences, we also include in-orbitsuper-resolution (SR) in the system design. We introduce a new imagereconstruction technique and a unique loss function to enable the execution ofthe SR model on low-power devices suitable for satellite environments. Wepresent a residual dense non-local attention network (RDNLA) that providesenhanced super-resolution outputs to improve the SR performance. SR experimentson Kaguya digital ortho maps (DOMs) demonstrate that the proposed SR algorithmoutperforms the residual dense network (RDN) in terms of PSNR andblock-sensitive PSNR by a margin of +0.1 dB and +0.19 dB, respectively. Theproposed SR system consumes 48% less memory and 67% less peak instantaneouspower than the standard SR model, RDN, making it more suitable for execution ona low-powered device platform.

In-Orbit Lunar Satellite Image Super Resolution for Selective Data Transmission