Across Date Species Detection Using Airborne Imaging Spectroscopy
Anthony Laybros; Daniel Schläpfer; Jean‐Baptiste Féret; Laurent Descroix; Caroline Bedeau; Marie-José Lefèvre; Grégoire Vincent · 2019 · Remote Sensing
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Imaging spectroscopy is a promising tool for airborne tree species recognition in hyper-diverse tropical canopies. However, its widespread application is limited by the signal sensitivity to acquisition parameters, which may require new training data in every new area of application. This study explores how various pre-processing steps may improve species discrimination and species recognition under different operational settings. In the first experiment, a classifier was trained and applied on imaging spectroscopy data acquired on a single date, while in a second experiment, the classifier wa
Abstract by Anthony Laybros; Daniel Schläpfer; Jean‐Baptiste Féret; Laurent Descroix; Caroline Bedeau; Marie-José Lefèvre; Grégoire Vincent, Remote Sensing (2019) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.3390/rs11070789