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100 results for “hyperspectral images”
Data from: Detection of tephra layers in Antarctic sediment cores with hyperspectral imaging
Tephrochronology uses recognizable volcanic ash layers (from airborne pyroclastic deposits, or tephras) in geological strata to set unique time references for paleoenvironmental events across wide geographic areas. This involves the detection of tephra layers which sometimes are not evident to the naked eye, including the so-called cryptotephras. Tests that are expensive, time-consuming, and/or destructive are often required. Destructive testing for tephra layers of cores from difficult regions, such as Antarctica, which are useful sources of other kinds of information beyond tephras, is always undesirable. Here we propose hyperspectral imaging of cores, Self-Organizing Map (SOM) clustering of the preprocessed spectral signatures, and spatial analysis of the classified images as a convenient, fast, non-destructive method for tephra detection. We test the method in five sediment cores from three Antarctic lakes, and show its potential for detection of tephras and cryptotephras.
Hyperspectral imaging dataset
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Hyperspectral imaging dataset of potato plants exposed to water-deficit condition
<p><strong>An experiment:</strong></p> <ul> <li>Greenhouse experiment under controlled environmental conditions.</li> <li>Conducted at the Agricultural Institute of Slovenia (Ljubljana, Slovenia). </li> <li>From April to August 2021.</li> <li>A night/day temperature of 21 °C/15 °C; relative humidity of 60%, and photoperiod of 14h.</li> <li>28 cultivars of KIS Krka and 18 of KIS Savinja grown from tubers in 5-litre pots.</li> <li>5 weeks after planting, half plants of both cultivars were randomly assigned to either water-deficient or well-watered groups. </li> <li>The water-deficient group was exposed to a limited water irrigation regime, i.e., up to 50% of substrate saturation field capacity. </li> <li>The soil moisture was surveilled using tensiometers (14.04.04 Jett Fill tensiometers, Eijkelkamp, Giesbeek Netherlands).</li> <li>Throughout the duration of the experiment, the matric potential of the soil was maintained within the range -0,01 MPa to -0,025 MPa for well-watered plants, and -0,05 MPa to -0,07 MPa for water-deficient plants. </li> </ul> <p> </p> <p><strong>Hyperspectral imaging: </strong></p> <ul> <li>Every week after the deficit was introduced.</li> <li>Total of 5 imaging sessions were performed.</li> <li>The imaging sessions took place in a dark room, where cameras were positioned at a 3 m distance from the potato plants, together with calibrated halogen lamps.</li> <li>Hyperspectral images were acquired in the VNIR (visible to near infrared) and SWIR (short-wave infrared) spectral regions. </li> <li>Hyspex (Norsk Elektro Optikk, Oslo Norway) push-broom cameras VNIR-1600 (400–988 nm, 160 bands, bandwidth 3.6 nm) and SWIR-384 (950–2500 nm, 288 bands, bandwidth 5.4 nm) were used.</li> </ul> <p> </p> <p><strong>Files:</strong></p> <ul> <li> <p><strong>File structure:</strong></p> </li> </ul> <p> 📂 imagings<br> ├── 📁 imaging-1<br> │ ├── 📄 0_1_0__KK-K-04_KS-K-05_KK-S-03__imaging-1__1-22_20000_us_2x_HSNR02_ 2022-05-11T104633_corr_rad_f32.hdr<br> │ ├── 📄 0_1_0__KK-K-04_KS-K-05_KK-S-03__imaging-1__1-22_20000_us_2x_HSNR02_2022-05-11T104633_corr_rad_f32.img<br> │ └── 📄 ...<br> ├── 📁 imaging-2<br> │ └── 📄 ...<br> ├── 📁 imaging-3<br> │ └── 📄 ...<br> ├── 📁 imaging-4<br> │ └── 📄 ...<br> └── 📁 imaging-5<br> └── 📄 ...</p> <p> </p> <ul> <li> <p><strong>Description of a name:</strong></p> </li> </ul> <p>A_B_C__L1_L2_L3__imaging-X__ID.img -> image file</p> <p>A_B_C__L1_L2_L3__imaging-X__ID.hdr -> header file belonging to an image file</p> <p> </p> <p>A - index of original raw hyperspectral image</p> <p>B - index of an object on the image (of a particular potato plant)</p> <p>C - index of slice extracted from the image</p> <p>L - labels of plants on the image</p> <p>X - index of the imaging session</p> <p>ID - string identifier</p> <p> </p> <ul> <li> <p><strong>Description of labels (L):</strong></p> </li> </ul> <p>V-T-N (e.g. KK-K-04)</p> <p> </p> <p>V - variety (KK - KIS Krka or KS - KIS Savinja)</p> <p>T - treatment (K - control or S, drought)</p> <p>N - index of a particular plant</p> <p> </p> <ul> <li> <p><strong>Image properties:</strong></p> </li> </ul> <p>Width of the image: 64</p> <p>Height of the image: 64</p> <p>Number of spectral bands: 448</p> <p>Spectral range: 410nm - 2510nm</p> <p>Image values are expressed in reflectance</p> <p> </p> <p><strong>Additional links:</strong></p> <p>Code where the dataset was used for the entire analysis could be found here:</p> <p>https://github.com/Manuscripts-code/Potato-plants-drought--plants-2024</p> <p> </p>
Hyperspectral Oblique Plane Microscopy -- microparticles 3D & laser beam profile & hyperspectral image of UV adhesive
<p>Processed spectra data for microparticle classification and raw hyperspectral images from mixture of microparticle</p>
Fig. 1 a –d Marine benthic organisms used for bio-optical measurements. a Boneccia viridis, b Isodictya pacmata, c Hymedesmia paupertas, d in Development of hyperspectral imaging as a bio-optical taxonomic tool for pigmented marine organisms
Fig. 1 a –d Marine benthic organisms used for bio-optical measurements. a Boneccia viridis, b Isodictya pacmata, c Hymedesmia paupertas, d Hymedesmia sp.
Hyperspectral datacube and raw spatio-spectral images of Butterfly
<p>This dataset contains spectral data of four butterfly species: Hypolimnas Misippus (HM), Danaus Chrysippus (DC), Amauris Ochlea (AO), and Acraea Egina (AE). The data collection has been obtained with the help of International Institute of Tropical Agriculture (IITA) in Benin.</p> <p> It is structured as follows :</p> <ul> <li> `classification_test/` : Contains raw spatio-spectral images related to the testing of classification models. <ul> <li>`{species}/` : Contains the raw images for each butterfly `{species}`<br> </li> </ul> </li> <li>`datacube/` : Contains hyperspectral datacubes for each species, used to estimate Gaussian distribution parameters. <ul> <li>`{species}/cube {#No}` : Contains the specific datacube for the butterfly `{species}`</li> <li>`{filename}.dat` : Data file containing the primary spectral information of the butterfly `{species}`.</li> <li>`{filename}.dat.hdr` : Header file containing metadata for the corresponding `{filename}.dat` file, including information about dimensions, wavelengths, and other important parameters.</li> <li>`{filename}_mask.npy` : A NumPy array file that likely contains a mask to segment butterfy regions in the hyperspectral datacube.</li> </ul> </li> </ul>
Cross-Scene Hyperspectral Remote Sensing Wetland image data
<p>Two representative study areas in China, i.e., Yancheng and Huanghekou (i.e, Yellow River Estuary) wetlands, are selected.<br> For Yancheng wetland, there are two HSIs acquired by the Advanced Hyperspectral Imager (AHSI) aboard on China's Ziyuan1-02D (ZY1-02D) and GaoFen-5 (GF-5) satellites, respectively. For Huanghekou wetland, there are also two HSIs acquired by the AHSI aboard on China's ZY1-02D satellite in June 28, 2020 and September 29, 2021, respectively.</p>
Hyperspectral Imaging in Thoracic Surgery
ClinicalTrials.gov study NCT04784884. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Hyperspectral Imaging to Assess and Predict Diabetic Foot Ulcers
ClinicalTrials.gov study NCT00617916. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Hyperspectral Imaging Pre and Post Endovascular Intervention
ClinicalTrials.gov study NCT00768495. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Intraoperative Hyperspectral Imaging in Gastrointestinal Anastomoses
ClinicalTrials.gov study NCT03667950. IPD Sharing: NO. Countries: 1. Publications: 1.
Hyperspectral images of King, Magnificent, and hybrid King of Holland's Bird-of-Paradise (Cicinnurus regius, C. magnificus, and C. magnificus x C. regius)
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Data from: Biomimicry of iridescent, patterned insect cuticles: comparison of biological and synthetic, cholesteric microcells using hyperspectral imaging
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Data from: Detection of tephra layers in Antarctic sediment cores with hyperspectral imaging
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HyTexiLa: High Resolution Visible and Near Infrared Hyperspectral Texture Images
<p>We present a dataset of close range hyperspectral images of materials that span the visible and near infrared spectrums: HyTexiLa (Hyperspectral Texture images acquired in Laboratory). The data is intended to provide high spectral and spatial resolution reflectance images of 112 materials to study spatial and spectral textures. In this paper we discuss the calibration of the data and the method for addressing the distortions during image acquisition. We provide a spectral analysis based on non-negative matrix factorization to quantify the spectral complexity of the samples and extend local binary pattern operators to the hyperspectral texture analysis. The results demonstrate that although the spectral complexity of each of the textures is generally low, increasing the number of bands permits better texture classification, with the opponent band local binary pattern feature giving the best performance.</p>
Data underlying the paper titled "Enhancing hyperspectral imaging through macro and multi-modal capabilities"
<p>The data is in .mat format and contains two variables:<br>data(x, y, lambda) is the variable that contains the hyperspectral dataset, after being converted from temporal to spectral hypercube.<br>WL(lambda) is the variable that contains the wavelength calibration.</p>
The Clinical Study Aims to Assess the Quality of Donor Livers Using Hyperspectral Imaging.
ClinicalTrials.gov study NCT06608667. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Prediction of cooking time for soaked and unsoaked dry beans (Phaseolus vulgaris L.) using hyperspectral imaging technology
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Integration of hyperspectral imaging and transcriptomics from individual cells with HyperSeq
GEO Series GSE254034. Homo sapiens. 204 samples. Type: Expression profiling by high throughput sequencing.
Measurement report: VOCs hyperspectral imaging: A new insight to evaluate the health risk from industrial emissions
<p>We carried out hyperspectral imaging of aldehyde VOCs for a chemical facility, a petrochemical facility and an industrial park containing various types of enterprises in the Yangtze River Delta. The human health risk of these VOCs.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.