Hyperspectral Imaging, employed in satellites for space remote sensing, like HYPSO-1, faces constraints due to few labeled data sets, affecting the training of AI models demanding these ground-truth annotations. In this work, we introduce The HYPSO-1 Sea-Land-Cloud-Labeled Dataset, an open dataset with 200 diverse hyperspectral images from the HYPSO-1 mission, available in both raw and calibrated forms for scientific research in Earth observation. Moreover, 38 of these images from different countries include ground-truth labels at pixel-level totaling about 25 million spectral signatures labeled for sea/land/cloud categories. To demonstrate the potential of the dataset and its labeled subset, we have additionally optimized a deep learning model (1D Fully Convolutional Network), achieving superior performance to the current state of the art. The complete dataset, ground-truth labels, deep learning model, and software code are openly accessible for download at this website which we provide as a supplementary material for the paper AN OPEN HYPERSPECTRAL DATASET WITH SEA-LAND-CLOUD GROUND-TRUTH FROM THE HYPSO-1 SATELLITE submitted to arXiv on 25th August 2023. The manuscript is available here (also in PDF). We hope our Hyperspectral Dataset benefits the HSI community and the broader field of Earth and ocean observation.
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Read our additional article on Sea-Land-Cloud Segmentation in Satellite Hyperspectral Imagery by Deep Learning: click here for more details (also in PDF).