Hyperspectral Qualification of Aged Beef Sirloin
By: Ferenc Firtha, Anita Jasper, László Friedrich, József Felföldi
Abstract
Since the ageing process provides high added values that manifests in price, a quick, non-contact measurement method would be useful in industry to check ageing state. The objective of this study was to estimate the ageing time of beef sirloin samples by their reflected NIR spectra and hyperspectral datacube as well.`
Hyperspectral remote sensing for detecting geotechnical problems at Ray mine
By: Jingping He, Isabel Barton
Abstract
In this case study, we applied hyperspectral remote sensing for large-scale mapping and detection of minerals at a non-structure-related ground instability in the highwalls of the Ray mine near Tucson, Arizona. This combines drone- and tripod-mounted sensors, integrating hyperspectral with LiDAR and radar data, and using an iteratively refined spectral library based on site-specific sampling supported by ground truth.
Identification of Wheat Yellow Rust using Spectral and Texture Features of Hyperspectral Images
By: Anting Guo, Wenjiang Huang, Huichun Ye, Yingying Dong, Huiqin Ma, Yu Ren, Chao Ruan
Abstract
Wheat yellow rust is one of the most destructive diseases in wheat production and significantly affects wheat quality and yield. Accurate and non-destructive identification of yellow rust is critical to wheat production management. Hyperspectral imaging technology has proven to be effective in identifying plant diseases. We investigated the feasibility of identifying yellow rust on wheat leaves using spectral features and textural features (TFs) of hyperspectral images.
In-field and non-destructive monitoring of grapes maturity by hyperspectral imaging
By: Alessandro Benelli, Chiara Cevoli, Luigi Ragni, Angelo Fabbri
Abstract
Monitoring the quality attributes of grapes is a practice that allows the state of ripeness to be checked and the optimal harvest time to be identified. A non-destructive method based on hyperspectral imaging (HSI) technology was developed. Analyses were carried out directly in the field on a ‘Sangiovese’ (Vitis vinifera L.) vineyard destined for wine production, by using a Vis/NIR (400–1000 nm) hyperspectral camera.
In-field Vis/NIR hyperspectral imaging to measure soluble solids content of wine grape berries during ripening
By: Alessandro Benelli, Chiara Cevoli, Angelo Fabbri
Abstract
Monitoring the quality attributes of grapes is a practice that allows to check the grapes’ state of ripeness and to decide when it is appropriate to proceed with the harvest. In the present study, a non-destructive method based on hyperspectral imaging (HSI) technology was developed. Analyses were carried out directly in the field using a Vis/NIR (400-1000 nm) hyperspectral camera (HSC) between the rows of `Sangiovese’ (Vitis vinifera L.) vineyard destined for wine production.
Intuitive Image Analyzing on Plant Data High-Throughput Plant Analysis with LemnaTec Image Processing
By: Stefan Paulus, T. Dornbusch, Marcus Jansen
Abstract
For digital plant phenotyping huge amounts of 2D images are acquired. This is known as one part of the phenotyping bottleneck. This bottleneck can be addressed by well-educated plant analysts, huge experience and an adapted analysis software.
Lasers Without Lost Cities: Using Drone Lidar to Capture Architectural Complexity at Kuelap, Amazonas, Peru
By: Parker VanValkenburgh, K. C. Cushman, Luis Jaime Castillo Butters, Carol Rojas Vega, Carson B. Roberts, Charles Kepler, James Kellner
Abstract
We report the results of drone lidar survey at a high-elevation archaeological site in the Chachapoyas region of Peruvian Amazonia. Unlike traditional airborne remote sensing, drone lidar produces very high-density measurements at a wide range of scan angles by operating at low altitudes and slow flight speeds. These measurements can resolve near vertical surfaces and novel dimensions of variability in architectural datasets.
Machine learning-driven hyperspectral imaging for non-destructive origin verification of green coffee beans across continents, countries, and regions
By: Joy Sim, Yash Dixit, Cushla Mcgoverin, Indrawati Oey, Russell Frew, Marlon M. Reis, Biniam Kebede
Abstract
Coffee is a target for geographical origin fraud. More rapid, cost-effective, and sustainable traceability solutions are needed. The potential of hyperspectral imaging-near-infrared (HSI-NIR) and advanced machine learning models for rapid and non-destructive origin classification of coffee was explored for the first time (i) to understand the sensitivity of HSI-NIR for classification across various origin scales (continental, country, regional), and (ii) to identify discriminant wavelength regions. HSI-NIR analysis was conducted on green coffee beans from three continents, eight countries, and 22 regions.
Mapping Barrier Island Soil Moisture using a Radiative Transfer Model of Hyperspectral Imagery from an Unmanned Aerial System
By: Rehman S. Eon, Charles M. Bachmann
Abstract
The advent of remote sensing from unmanned aerial systems (UAS) has opened the door to more affordable and effective methods of imaging and mapping of surface geophysical properties with many important applications in areas such as coastal zone management, ecology, agriculture, and defense. We describe a study to validate and improve soil moisture content retrieval and mapping from hyperspectral imagery collected by a UAS system.
MethaneAIR: A High-Resolution Infrared Imaging Spectrometer for Airborne Measurements of CH4 and CO2
By: Jenna Samra, Jonathan E. Franklin, Bruce Daube, Peter Cheimets, Scott Milligan, Martin H. Ettenberg, Josh Benmergui, Kelly Chance, Apisada Chulakadabba, Eamon Conway, Xiong Liu, Christopher Miller, Amir H. Souri, Kang Sun, Steven Wofsy
Abstract
Measures total-column dry air mole fraction of CH4 and CO2 over a 23.7° swath at high spatial resolution. Precursor to MethaneSAT, which will revolutionize measurements and modeling of CH4 emissions across the globe. Observations facilitate advances in spectroscopy and retrievals needed for precise emissions measurements from MethaneSAT. By itself represents a major advance in the state of the art of airborne remote sensing of CH4 and CO2
