Removing fluid lensing effects from spatial images
This addresses the challenge of detailed imaging for coral reefs and seagrass meadows, which is critical for climate and biodiversity monitoring, though it appears incremental as a proof of concept.
The paper tackled the problem of fluid lensing distortions in remote sensing images of shallow water ecosystems, developing a proof-of-concept machine learning model that removes most effects to produce clearer, more stable images.
Shallow water and coastal aquatic ecosystems such as coral reefs and seagrass meadows play a critical role in regulating and understanding Earth's changing climate and biodiversity. They also play an important role in protecting towns and cities from erosion and storm surges. Yet technology used for remote sensing (drones, UAVs, satellites) cannot produce detailed images of these ecosystems. Fluid lensing effects, the distortions caused by surface waves and light on underwater objects, are what makes the remote sensing of these ecosystems a very challenging task. Using machine learning, a proof of concept model was developed that is able to remove most of these effects and produce a clearer more stable image.