  Hyper-Spectral Imaging (HSI) is a crucial technique for analysing remote
sensing data acquired from Earth observation satellites. The rich spatial and
spectral information obtained through HSI allows for better characterisation
and exploration of the Earth's surface over traditional techniques like RGB and
Multi-Spectral imaging on the downlinked image data at ground stations.
Sometimes, these images do not contain meaningful information due to the
presence of clouds or other artefacts, limiting their usefulness. Transmission
of such artefact HSI images leads to wasteful use of already scarce energy and
time costs required for communication. While detecting such artefacts before
transmitting the HSI image is desirable, the computational complexity of these
algorithms and the limited power budget on satellites (especially CubeSats) are
key constraints. This paper presents an unsupervised learning-based
convolutional autoencoder (CAE) model for artefact identification of acquired
HSI images at the satellite and a deployment architecture on AMD's Zynq
Ultrascale FPGAs. The model is trained and tested on widely used HSI image
datasets: Indian Pines, Salinas Valley, the University of Pavia and the Kennedy
Space Center. For deployment, the model is quantised to 8-bit precision,
fine-tuned using the Vitis-AI framework and integrated as a subordinate
accelerator using AMD's Deep-Learning Processing Units (DPU) instance on the
Zynq device. Our tests show that the model can process each spectral band in an
HSI image in 4 ms, 2.6x better than INT8 inference on Nvidia's Jetson platform
& 1.27x better than SOTA artefact detectors. Our model also achieves an
f1-score of 92.8% and FPR of 0% across the dataset, while consuming 21.52 mJ
per HSI image, 3.6x better than INT8 Jetson inference & 7.5x better than SOTA
artefact detectors, making it a viable architecture for deployment in CubeSats.
