Fluid lensing and machine learning for centimeter-resolution airborne assessment of coral reefs in American Samoa

摘要

A novel NASA remote sensing technique, airborne fluid lensing, has enabled cm-resolution multispectral 3D remote sensing of aquatic systems, without adverse refractive distortions from ocean waves. In 2013, a drone-based airborne fluid lensing campaign conducted over the coral reef of Ofu Island, American Samoa, revealed complex 3D morphological, ecological, and bathymetric diversity at the cm-scale over a regional area. In this paper, we develop and validate supervised machine learning algorithm products tailored for accurate automated segmentation of coral reefs using airborne fluid lensing multispectral 3D imagery. Results show that airborne fluid lensing can significantly improve the accuracy of coral habitat mapping using remote sensing.