Using Underwater Hyperspectral Imaging to Increase Feature Detection and Improve Under-Ice Navigation

Extensive sea ice in Arctic marine regions has hindered indepth exploration and research. To overcome this frontier, underwater robots are increasingly used to explore beneath the ice cover. While this enables research over a broader area, it increases the risk of losing valuable vehicles under the ice, placing greater emphasis on the navigational capabilities of the robots. While inertial and acoustical navigation methods share limitations for operating under the ice, visual navigation can utilize optical features for position estimation at a very fine resolution. However, the underside of ice tends to lack visible features. To overcome this, we propose integrating a novel Underwater Hyperspectral Imager. This technology captures detailed spectral information, revealing features and patterns invisible to traditional imaging methods. To test this, a Remotely Operated Vehicle was equipped with an upward-pointing hyperspectral imager. Three Arctic expeditions were conducted, capturing under-ice data including an Arctic ice algae bloom. To determine whether the hyperspectral imager provides improved navigation capabilities, we created five spectral transformations utilizing the spectral quantity of the hyperspectral domain by either increasing the number of layers fed into the detection algorithm (5-Band, 10-Band and 20-band) or by transforming the spectra using Principal Component Analysis (PCA) and the Pearson correlation. We then compared these to four transformations which resembled the behaviour of an RGB/grayscale equivalent. We found that the hyperspectral methods showed an improvement in precision. These improvements in precision highlight the potential of hyperspectral imagina for robust navigation in challenging under-ice environments.