Underwater Hyperspectral Imaging for Measuring Seafloor Reflectance

Hongjie Zhang, Gideon Billings, Jackson Shields, Stefan Williams

Abstract: A known challenge for computer vision methods applied to the underwater domain is that nonlinear attenuation of light in underwater environments distorts the color signal in captured imagery, resulting in inconsistent color and contrast at varying distances to an imaged target. While surface reflectance can provide a useful cue for classifying imagery of the seafloor by object or substrate types, color inconsistency makes robust classification challenging. We introduce a method that leverages hyperspectral imagery with an underwater light formation model and structure from motion to estimate the intrinsic optical properties of the underwater environment and correct seafloor reflectance estimates from radiance measurements. We show that our method enables consistent surface reflectance estimates under both artificial and ambient lighting conditions and is readily integrated on small underwater vehicle platforms, such as a BlueROV.