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 Curaçao. 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.
Highlights
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
While in situ benthic field survey methods have long been the gold standard among coral reef ecologists and widely deployed for many monitoring programs around the world, they are limited in spatial scale due to logistical constraints [2,3]
Summary
Assessing coral reef habitats has historically been difficult because they are highly heterogeneous and structurally complex systems. There is a pressing need for a rapid and scalable method of assessing coral reefs It is within this context that close-range [6], or underwater hyperspectral imaging [7] has been developed and deployed for surveying the habitat structure of coral reefs. We formulated a novel protocol to annotate hyperspectral coral reef imagery to reduce the amount of manual identification, easing the data annotation effort This dataset contains an independent set of annotations to develop and validate image segmentation efforts to extract semantic descriptions of habitat maps. Coral reef ecologists would benefit from the availability of this dataset, as it can be used to develop tools for scalable habitat description This type of data is of interest to research communities seeking real-world datasets to improve machine learning workflows for automated analyses such as data fusion, classification and segmentation [17]. Another avenue of interest may be the use of incremental learning, where data is consumed gradually by classifier models, since this dataset contains hyperspectral images distributed across water depths (between 3 m and 10 m) and geographical location [18]
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