From Do-It-Yourself Design to Discovery: A Comprehensive Approach to Hyperspectral Imaging from Drones

This paper presents an innovative, holistic, and comprehensive approach to drone-based imaging spectroscopy based on a small, cost-effective, and lightweight Unmanned Aerial Vehicle (UAV) payload intended for remote sensing applications. The payload comprises a push-broom imaging spectrometer built...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2024-09, Vol.16 (17), p.3202
Main Authors: Hasler, Oliver, Løvås, Håvard S., Oudijk, Adriënne E., Bryne, Torleiv H., Johansen, Tor Arne
Format: Article
Language:English
Subjects:
Cameras
Data collection
Data processing
Design
Drone aircraft
Drones
Geographic information systems
Hyperspectral imaging
Imaging spectrometers
imaging spectroscopy
In situ measurement
Payloads
Reflectance
Remote sensing
Satellites
Sensors
Software
Spectroscopy
Spectrum analysis
UAV
Unmanned aerial vehicles
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Summary:This paper presents an innovative, holistic, and comprehensive approach to drone-based imaging spectroscopy based on a small, cost-effective, and lightweight Unmanned Aerial Vehicle (UAV) payload intended for remote sensing applications. The payload comprises a push-broom imaging spectrometer built in-house with readily available Commercial Off-The-Shelf (COTS) components. This approach encompasses the entire process related to drone-based imaging spectroscopy, ranging from payload design, field operation, and data processing to the extraction of scientific data products from the collected data. This work focuses on generating directly georeferenced imaging spectroscopy datacubes using a Do-It-Yourself (DIY) imaging spectrometer, which is based on COTS components and freely available software and methods. The goal is to generate a remote sensing reflectance datacube that is suitable for retrieving chlorophyll-A (Chl-A) distributions as well as other properties of the ocean spectra. Direct georeferencing accuracy is determined by comparing landmarks in the directly georeferenced datacube to their true location. The quality of the remote sensing reflectance datacube is investigated by comparing the Chl-A distribution on various days with in situ measurements and satellite data products.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs16173202