A spatial-scanning pushbroom hyperspectral imaging (HSI) system incorporating a video camera (VC) which is not only used for direct video imaging but also for the selection of the region of interest within the VC’s full field-of-view is presented. Using a VC for these two applications brings many benefits to a pushbroom HSI system, such as a minimized data acquisition time and smaller data storage requirement. A detailed description of the system followed by the methods and formulas used for calibration and electronic hardware interfacing were discussed and analyzed using United States Air Force resolution chart, chicken breast tissue, and fluorescent targets as test samples. The proposed concepts and developed system can find potential biomedical imaging applications and can be extended to endoscopic imaging applications as well.
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http://dx.doi.org/10.1117/1.JBO.20.4.046010 | DOI Listing |
J Imaging
December 2024
Process Analysis and Technology PA & T, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, Germany.
Ultraviolet (UV) hyperspectral imaging shows significant promise for the classification and quality assessment of raw cotton, a key material in the textile industry. This study evaluates the efficacy of UV hyperspectral imaging (225-408 nm) using two different light sources: xenon arc (XBO) and deuterium lamps, in comparison to NIR hyperspectral imaging. The aim is to determine which light source provides better differentiation between cotton types in UV hyperspectral imaging, as each interacts differently with the materials, potentially affecting imaging quality and classification accuracy.
View Article and Find Full Text PDFJ Biomed Opt
September 2024
Universidad Politécnica de Madrid, Research Center on Software Technologies and Multimedia Systems, Madrid, Spain.
Significance: Hyperspectral imaging sensors have rapidly advanced, aiding in tumor diagnostics for brain tumors. Linescan cameras effectively distinguish between pathological and healthy tissue, whereas snapshot cameras offer a potential alternative to reduce acquisition time.
Aim: Our research compares linescan and snapshot hyperspectral cameras for brain tissues and chromophore identification.
Data Brief
October 2024
Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND, United States.
WeedCube dataset consists of hyperspectral images of three crops (canola, soybean, and sugarbeet) and four invasive weeds species (kochia, common waterhemp, redroot pigweed, and common ragweed). Plants were grown in two separate greenhouses and plant canopies were captured from a top-down camera angle. A push-broom hyperspectral sensor in the visible near infrared region of 400-1000 nm was used for data collection.
View Article and Find Full Text PDFSensors (Basel)
July 2024
Center of Process Analysis and Technology (PA&T), School of Life Sciences, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, Germany.
In the manufacturing process of electrical devices, ensuring the cleanliness of technical surfaces, such as direct bonded copper substrates, is crucial. An in-line monitoring system for quality checking must provide sufficiently resolved lateral data in a short time. UV hyperspectral imaging is a promising in-line method for rapid, contactless, and large-scale detection of contamination; thus, UV hyperspectral imaging (225-400 nm) was utilized to characterize the cleanliness of direct bonded copper in a non-destructive way.
View Article and Find Full Text PDFData Brief
August 2024
Department of Earth and Space Sciences, Indian Institute of Space Science and Technology, Valiamala, Thiruvananthapuram, India.
Technology infusion in agriculture has been progressing steadily, touching upon various spheres of agriculture such as crop identification, soil classification, yield prediction, disease detection, and weed-crop discrimination. On-demand crop type detection, often realized as crop mapping, is a primary requirement in agriculture. Alongside the topographic LiDAR and thermal imaging, hyperspectral remote sensing is a versatile technique for mapping and predicting various parameters of interest in agriculture.
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