An Open Hyperspectral Dataset with Sea-Land-Cloud Ground-Truth from the HYPSO-1 Satellite
Jon A. Justo, Joseph Garrett, Dennis D. Langer, Marie B. Henriksen,, Radu T. Ionescu, and Tor A. Johansen

TL;DR
This paper introduces a comprehensive open hyperspectral dataset from the HYPSO-1 satellite, including pixel-level labels for sea, land, and cloud categories, and demonstrates its utility with a deep learning model outperforming existing methods.
Contribution
The creation of a large, labeled hyperspectral dataset with ground-truth for sea, land, and cloud, and the demonstration of its effectiveness with a new deep learning approach.
Findings
Deep learning model achieved state-of-the-art performance.
Dataset includes 200 hyperspectral images with 25 million labeled spectral signatures.
Open access to dataset, labels, and code for scientific research.
Abstract
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,…
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Taxonomy
TopicsRemote-Sensing Image Classification · Infrared Target Detection Methodologies · Geochemistry and Geologic Mapping
