Automated Georectification and Mosaicking of UAV-Based Hyperspectral Imagery from Push-Broom Sensors
By: Yoseline Angel, Darren Turner, Stephen Parkes, Yoann Malbeteau, Arko Lucieer, Matthew F. McCabe
Abstract
Hyperspectral systems integrated on unmanned aerial vehicles (UAV) provide unique opportunities to conduct high-resolution multitemporal spectral analysis for diverse applications. However, additional time-consuming rectification efforts in postprocessing are routinely required, since geometric distortions can be introduced due to UAV movements during flight, even if navigation/motion sensors are used to track the position of each scan.
Characterization of FIREFLY, an Imaging Spectrometer Designed for Airborne Measurements of Solar-Induced Fluorescence
By: Bruce Cook, Lawrence Corp, Peter Clemens, Ian Paynter, Jyoteshwar Nagol, Joel McCorkel
Abstract
FIREFLY (Fluorescence Imaging of REd and Far-red Light Yield) is a compact, fine-resolution imaging spectrometer that was designed and assembled by Headwall Photonics (Fitchburg, MA, USA) in collaboration with NASA scientists for airborne measurements of Solar-Induced Fluorescence (SIF).
Characterization of FIREFLY, an Imaging Spectrometer Designed for Remote Sensing of Solar-Induced Fluorescence
By: Ian Paynter, Bruce Cook, Lawrence Corp, Jyoteshwar Nagol, Joel McCorkel
Abstract
Solar induced fluorescence (SIF) is an ecological variable of interest to remote sensing retrievals, as it is directly related to vegetation composition and condition. FIREFLY (fluorescence imaging of red and far-red light yield) is a high performance spectrometer for estimating SIF. FIREFLY was flown in conjunction with NASA Goddard’s lidar, hyperspectral, and thermal (G-LiHT) instrument package in 2017, as a technology demonstration for airborne retrievals of SIF.
Classification of Black Plastics Waste Using Fluorescence Imaging and Machine Learning
By: Florian Gruber, Wulf Grählert, Philipp Wollmann, Stefan Kaskel
Abstract
This work contributes to the recycling of technical black plastic particles, for example from the automotive or electronics industries. These plastics cannot yet be sorted with sufficient purity (up to 99.9%), which often makes economical recycling impossible. As a solution to this problem, imaging fluorescence spectroscopy with additional illumination in the near infrared spectral range in combination with classification by machine learning or deep learning classification algorithms is here investigated.
Community and Commercial Monitoring of Cyanobacterial Blooms in Drinking and Recreational Bodies of Water by Air and on the Surface
By: Carson Roberts Ph.D, Scott M. Gallager, Ross Nakatsuji
Abstract
A presentation by Carson Roberts, PhD, at AGU 2022 includes an overview on hyperspectral imaging and a Headwall and Coastal Ocean Vision study of cyanobacterial at Santuit Pond on Cape Cod in Massachusetts.
Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat
By: Clement Atzberger, Martine Guérif, Frédéric Baret, Willy Werner
Abstract
Hyperspectral data sets contain useful information for characterizing vegetation canopies not previously available from multi-spectral data sources. However, to make full use of the information content one has to find ways for coping with the strong multi-collinearity in the data.
Comparing the effectiveness of hyperspectral imaging and Raman spectroscopy: a case study on Armenian manuscripts
By: Ian J. Maybury, David Howell, Melissa Terras, Heather Viles
Abstract
There is great practical and scholarly interest in the identification of pigments in works of art. This paper compares the effectiveness of the widely used Raman Spectroscopy (RS), with hyperspectral imaging (HSI), a reflectance imaging technique, to evaluate the reliability of HSI for the identification of pigments in historic works of art and to ascertain if there are any benefits from using HSI or a combination of both.
Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review
By: Anton Terentev, Viktor Dolzhenko, Alexander Fedotov, Danila Eremenko
Abstract
The development of hyperspectral remote sensing equipment, in recent years, has provided plant protection professionals with a new mechanism for assessing the phytosanitary state of crops. Semantically rich data coming from hyperspectral sensors are a prerequisite for the timely and rational implementation of plant protection measures. This review presents modern advances in early plant disease detection based on hyperspectral remote sensing.
Detecting Xylella fastidiosa in a machine learning framework using Vcmax and leaf biochemistry quantified with airborne hyperspectral imagery
By: C. Camino, K. Ara˜no, J.A. Berni, H. Dierkes, J.L. Trapero-Casas, G. Le´on-Ropero, M. Montes-Borrego, M. Roman-´Ecija, M.P. Velasco-Amo, B.B. Landa, J.A. Navas-Cortes, P. S.A. Beck
Abstract
The bacterium Xylella fastidiosa (Xf) is a plant pathogen that can block the flow of water and nutrients through the xylem. Xf symptoms may be confounded with generic water stress responses. Hyperspectral imaging can be used to detect Xylella fastidiosa in a machine learning framework using Vcmax and leaf biochemistry quantified with airborne hyperspectral imager.
Detection of symptoms induced by vascular plant pathogens in tree crops using high-resolution satellite data: Modelling and assessment with airborne hyperspectral imagery
By: T. Poblete, J.A. Navas-Cortes, A. Hornero, C. Camino, R. Calderon, R. Hernandez-Clemente, B.B. Landa, P.J. Zarco-Tejada
Abstract
• Detection of symptoms due to vascular pathogens was evaluated with satellite imagery.
• Hyperspectral and thermal images used as benchmark to assess the detection performance.
• Both spectral bandsets were accurate to detect intermediate and advanced stages of infection.
• Multispectral satellite data were unable to detect early stages of infection accurately.
• Combining airborne thermal with satellite data improved accuracy up to 15% & κ ≥ 0.2.