Fusion neural networks for plant classification: learning to combine RGB, hyperspectral, and lidar data.

ABSTRACT: Airborne remote sensing offers unprecedented opportunities to efficiently monitor vegetation, but methods to delineate and classify individual plant species using the collected data are still actively being developed and improved. The Integrating Data science with Trees and Remote Sensing (IDTReeS) plant identification competition openly invited scientists to create and compare individual tree mapping methods. Participants were tasked with training taxon identification algorithms based on two sites, to then transfer their methods to a third unseen site, using field-based plant observations in combination with airborne remote sensing image data products from the National Ecological Observatory Network (NEON). These data were captured by a high resolution digital camera sensitive to red, gre

SUBMITTER: Scholl VM 

PROVIDER: S-EPMC8325917 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature