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Abstract:
This paper describes a large dataset of underwater hyperspectral imagery
that can be used by researchers in the domains of computer vision,
machine learning, remote sensing, and coral reef ecology. We present the
details of underwater data acquisition, processing and curation to
create this large dataset of coral reef imagery annotated for habitat
mapping. A diver-operated hyperspectral imaging system (HyperDiver) was
used to survey 147 transects at 8 coral reef sites around the Caribbean
island of Curacao. The underwater proximal sensing approach produced
fine-scale images of the seafloor, with more than 2.2 billion points of
detailed optical spectra. Of these, more than 10 million data points
have been annotated for habitat descriptors or taxonomic identity with a
total of 47 class labels up to genus- and species-levels. In addition to
HyperDiver survey data, we also include images and annotations from
traditional (color photo) quadrat surveys conducted along 23 of the 147
transects, which enables comparative reef description between two types
of reef survey methods. This dataset promises benefits for efforts in
classification algorithms, hyperspectral image segmentation and
automated habitat mapping. Dataset:
https://doi.org/10.1594/PANGAEA.911300 Dataset License: CC-BY-NC
