We describe an Underwater Hyperspectral Imager (UHI) deployed on an instrument-carrying platform consisting of two interconnected mini-ROVs (Remotely Operated Vehicle) for the mapping and monitoring of Arctic macroalgal habitats in Kongsfjorden (Svalbard) during the Polar Night. The mini-ROV-UHI system is easy to transport, assemble and deploy from shore, even under the dark, icy and cold conditions of the Arctic Polar Night. The system can be operated by two persons, keeping the operational costs low. In vivo hyperspectral reflectance of collected specimens of brown, red and green macroalgae was measured with a spectrometer in the lab to provide a spectral library for supervised pigment group classification based on UHI photomosaics. The in situ UHI-photomosaics provided detailed information of the areal coverage of the seafloor substrate (16%), as well as brown (51% habitat cover), red (18%), and green (14%) macroalgae, with spatial resolution in the range of cm and spectral resolution of 2 nm. The collected specimens from the mapped area were also used for species identification and health state evaluation. This innovative UHI sampling method provides significant information about macroalgal distribution and physiology, and due to its flexibility in terms of deployment, it is applicable to a variety of environments.

This article proposes a novel methodology for precise georegistration in underwater hyperspectral imaging (UHI) using a red–green–blue (RGB) camera to build a photogrammetry model that estimates the pose and a 3-D seabed model. The two main scientific contributions are: 1) the development of two methods for geometric calibration of the hyperspectral imager (HSI) with the RGB camera and 2) the development of a methodology for consistent georegistration of the pushbroom hyperspectral imagery on the seabed using the photogrammetry model and the geometrically calibrated HSI. The georegistration uses a calibrated RGB camera, fixed to the HSI, with an overlapping field of view. The RGB images are used to build a photogrammetry model. Through utilizing the geometric HSI parameters from the calibration, the hyperspectral imagery is ray cast onto the 3-D model. This methodology is exemplified for UHI from a remotely operated vehicle on a cold-water coral reef in the Trondheim Fjord, Norway. The precision is unprecedented for georegistration in UHI, demonstrated by the first-ever spatially consistent UHI mosaic to contain multiple transects.

In this paper we present coregistration of hyperspectral imagery with photogrammetry for shallow-water mapping from an Unmanned Aerial Vehicle (UAV). The coregistration is based on a methodology for georeferencing that utilizes outputs from the photogrammetry pipeline through three steps. First, we perform the photogrammetry pipeline. This gives camera poses from Structure-from-Motion (SfM) and a dense point cloud from Multi-View Stereo (MVS). Performing a refraction correction of the dense point cloud yields high-resolution bathymetry. Second, poses from SfM are fused with UAV navigation sensors in a Kalman smoother. Third, the geometric model of the hyperspectral imager is calibrated to align hyperspectral images with photogrammetry. Then, ray tracing is performed to georeference spectral measurements onto the bathymetry from MVS. Using the georeferenced hyperspectral imagery, we present spectral bathymetry estimation. The methods were demonstrated for a coastal site (depth < 6 m) in Norway, using a UAV with a camera and a hyperspectral imager. The georeferencing yielded a horizontal Mean Absolute Error (MAE) of 22 cm between hyperspectral and photogrammetry, equivalent to one hyperspectral pixel. The MVS bathymetry gave a MAE of 14 cm with respect to ground truth acoustic. The spectral bathymetry estimator was calibrated on ground truth acoustics with a MAE of 10 cm. Comparing the bathymetry from MVS with the spectral bathymetry yielded a MAE of 11 cm. The results suggest that coregistration with photogrammetry yields accurate georeferencing of hyperspectral imagery. The results also show that we can map bathymetry accurately using MVS with refraction correction or the spectral estimator.

In situ spectral reflectance initially captured at high spatial resolution with underwater hyperspectral imaging (UHI) is effective for classification and quantification in oceanic biogeochemical studies; however, the measured spectral radiance is rarely used as an absolute quantity due to challenges in calibration of UHI instruments. In this paper, a commercial UHI instrument was calibrated for radiometric flat field response and pixelwise immersion effect to support in situ measurement of absolute spectral radiance. The radiometric and immersion factor calibrations of the UHI instrument were evaluated quantitatively through comparative experiments with a spectroradiometer and a spectrometer. Results show that the immersion factor of the center pixel of the tested UHI instrument was 1.763 in pure water at 600 nm, and the averaged difference in immersion factor between the center and edge pixel of the UHI instrument in the visible light band was only 1∼3% across its half angle field of view of 35° in air. The new calibration coefficients were further used to calculate the spectral radiance of transmitted sunlight through ice algae clusters in sea ice measured by the UHI instrument during an Arctic under-ice bio-optical survey.

This paper presents an innovative, holistic, and comprehensive approach to drone-based imaging spectroscopy based on a small, cost-effective, and lightweight Unmanned Aerial Vehicle (UAV) payload intended for remote sensing applications. The payload comprises a push-broom imaging spectrometer built in-house with readily available Commercial Off-The-Shelf (COTS) components. This approach encompasses the entire process related to drone-based imaging spectroscopy, ranging from payload design, field operation, and data processing to the extraction of scientific data products from the collected data. This work focuses on generating directly georeferenced imaging spectroscopy datacubes using a Do-It-Yourself (DIY) imaging spectrometer, which is based on COTS components and freely available software and methods. The goal is to generate a remote sensing reflectance datacube that is suitable for retrieving chlorophyll-A (Chl-A) distributions as well as other properties of the ocean spectra. Direct georeferencing accuracy is determined by comparing landmarks in the directly georeferenced datacube to their true location. The quality of the remote sensing reflectance datacube is investigated by comparing the Chl-A distribution on various days with in situ measurements and satellite data products.

We propose a semi-analytical (SA) model for relating seafloor reflectance to measured radiance in deep-water hyperspectral imaging in artificially illuminated scenes. Using accurate sensor-seafloor geometry from photogrammetry and the principle of two-viewpoint observation, we estimate the inherent optical properties (IOPs) of the water column. We demonstrate the SA model and estimation of IOPs for hyperspectral imaging of a deep-water coral reef from a remotely operated vehicle. For the calibrated SA model, evaluation of across-viewpoint similarity demonstrates the model’s ability to compensate for water column and light source effects.