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Satellites are increasingly adopting on-board AI for enhanced autonomythrough in-orbit inference. In this context, the use of deep learning (DL)techniques for segmentation in hyperspectral (HS) satellite imagery offersadvantages for remote sensing applications, and therefore, we train 16different models, whose codes are made available through our study, which weconsider to be relevant for on-board multi-class segmentation of HS imagery,focusing on classifying oceanic (sea), terrestrial (land), and cloudformations. We employ the HYPSO-1 mission as an illustrative case forsea-land-cloud segmentation, and to demonstrate the utility of the segments, weintroduce a novel sea-land-cloud ranking application scenario. We consider howto prioritize HS image downlink based on sea, land, and cloud coverage levelsfrom the segmented images. We comparatively evaluate the models for futurein-orbit deployment, considering performance, parameter count, and inferencetime. The models include both shallow and deep models, and after we proposefour new DL models, we demonstrate that segmenting single spectral signatures(1D) outperforms 3D data processing comprising both spectral (1D) and spatial(2D) contexts. We conclude that our lightweight DL model, called1D-Justo-LiuNet, consistently surpasses state-of-the-art models forsea-land-cloud segmentation, such as U-Net and its variations, in terms ofperformance (0.93 accuracy) and parameter count (4,563). However, the 1D modelspresent longer inference time (15s) in the tested processing architecture,which seems to be a suboptimal architecture for this purpose. Finally, afterdemonstrating that in-orbit segmentation should occur post L1b radiancecalibration rather than on raw data, we also show that reducing spectralchannels down to 3 lowers models' parameter counts and inference time, at thecost of weaker segmentation performance.

Sea-Land-Cloud Segmentation in Satellite Hyperspectral Imagery by Deep Learning