Advanced visible infrared imaging spectrometer-new generation (AVIRIS-NG) airborne Hyperspectral data has 5nm spectral resolution which allows us to identify characteristic spectral signatures of the different altered and weathered mineral assemblage. In this study Airborne AVIRIS-NG hyperspectral data were used to identify the different altered, weathered and clay group of minerals in the Jahajpur, Bhilwara, India. In the study area, different hydrothermal minerals such as Montmorillonite, Smectite and Talc were identified. Apart from this, Goethite/Limonite mineral spectral signatures were identified using the AVIRIS-NG data in the VNIR (visible and near infrared) region of the electromagnetic spectrum. Minerals thus identified were verified by the conventional geological analysis viz. petrography and XRD of the field samples collected from the study area. The results of the conventional geological methods and spectroscopy were in good confirmation with the results found through the analysis of the AVIRIS-NG data. Identified minerals show a good indication of the advance argillic alteration in the study area which stand in confirmation with the geology of the area. Spectral analysis of the AVIRIS-NG data reveals that the reflectance spectra of the airborne AVIRIS-NG Hyperspectral data found promising for mineral identification and mapping.
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http://dx.doi.org/10.1016/j.heliyon.2020.e03487 | DOI Listing |
Sci Rep
December 2024
Department of Food Science and Technology, Zhejiang University of Technology, Hangzhou, 310014, Zhejiang, People's Republic of China.
Visible and Near-infrared hyperspectral imaging (VNIR-HSI) combined with machine learning has shown its effectiveness in various detection applications. Specifically, the quality of cigar tobacco leaves undergoes subtle changes due to environmental differences during the air-curing phase. This study aims to evaluate the feasibility of deep learning methods in overcoming data limitations to develop a VNIR-HSI prediction model for the quality of cigar tobacco leaves at different air-curing levels.
View Article and Find Full Text PDFJ Food Sci
December 2024
College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China.
As consumers increasingly prioritize food safety and nutritional value, the dairy industry faces a pressing need for rapid and accurate methods to detect essential nutritional components in milk, such as fat, protein, and lactose. Hyperspectral imaging (HSI) technology, known for its non-destructive, fast, and precise nature, shows great promise in food quality assessment. However, the high dimensionality of HSI data poses challenges for effective band selection and model optimization.
View Article and Find Full Text PDFPlant Physiol Biochem
December 2024
State Key Laboratory for Crop Stress Resistance and High-Efficiency Production, College of Agronomy, Northwest A&F University, Yangling, 712100, China; Key Laboratory of Wheat Biology and Genetic Improvement on Northwestern China, Ministry of Agriculture and Rural Affairs, Xianyang, 712100, China. Electronic address:
Photosynthesis drives crop growth and production, and strongly affects grain yields; therefore, it is an ideal trait for wheat drought resistance breeding. However, studies of the negative effects of drought stress on wheat photosynthesis rates have lacked accurate evaluation methods, as well as high-throughput techniques. We investigated photosynthetic capacity under drought stress in wheat varieties with varying degrees of drought stress resistance using hyperspectral and chlorophyll fluorescence (ChlF) imaging data.
View Article and Find Full Text PDFACS Sens
December 2024
Department of Physics, Dongguk university, Seoul 04620, Republic of Korea.
This study investigates the reconstruction of hyperspectral signatures from RGB data to enhance surgical imaging, utilizing the publicly available HeiPorSPECTRAL dataset from porcine surgery and an in-house neurosurgery dataset. Various architectures based on convolutional neural networks (CNNs) and transformer models are evaluated using comprehensive metrics. Transformer models exhibit superior performance in terms of RMSE, SAM, PSNR and SSIM by effectively integrating spatial information to predict accurate spectral profiles, encompassing both visible and extended spectral ranges.
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