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

Satellites are increasingly adopting on-board Artificial Intelligence (AI) techniques to enhance platforms' autonomy through edge inference. In this context, the utilization of deep learning (DL) techniques for segmentation in HS satellite imagery offers advantages for remote sensing applications, and therefore, we train 16 different models, whose codes are made available through our study, which we consider to be relevant for on-board multi-class segmentation of HS imagery, focusing on classifying oceanic (sea), terrestrial (land), and cloud formations. We employ the HYPSO-1 mission as an illustrative case for sea-land-cloud segmentation, and to demonstrate the utility of the segments, we introduce a novel sea-land-cloud ranking application scenario. Our system prioritizes HS image downlink based on sea, land, and cloud coverage levels from the segmented images. We comparatively evaluate the models for in-orbit deployment, considering performance, parameter count, and inference time. The models include both shallow and deep models, and after we propose four 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, called 1D-Justo-LiuNet, consistently surpasses state-of-the-art models for sea-land-cloud segmentation, such as U-Net and its variations, in terms of performance (0.93 accuracy) and parameter count (4,563). However, the 1D models present longer inference time (15s) in the tested processing architecture, which is clearly suboptimal. Finally, after demonstrating that in-orbit image segmentation should occur post L1b radiance calibration rather than on raw data, we additionally show that reducing spectral channels down to 3 lowers models' parameters and inference time, at the cost of weaker segmentation performance.