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303 results for “hyperspectral”
SNP genotype and hyperspectral reflectance data from: Ensembles of genomic and hyperspectral imaging-based prediction enable selection for reduced deoxynivalenol content in wheat grains
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Hyperspectral reflectance-based partial least squares regression models for predicting cotton leaf physiological traits
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Data From: Hyperspectral leaf reflectance of grasses varies with evolutionary lineage more than with site
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Data from: Hyperspectral imaging predicts differences in carbon and nitrogen status among representative biocrust functional groups of the Colorado Plateau
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Data from: Hyperspectral imaging has a limited ability to remotely sense the onset of beech bark disease
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Data from: Reflections of stress: Ozone damage in broadleaf saplings can be identified from hyperspectral leaf reflectance
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Active and low-cost hyperspectral imaging for spectral analysis in low lighting environment
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Data from: Black-grass monitoring using hyperspectral image data is limited by between-site variability
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Data from: A hyperspectral image can predict tropical tree growth rates in single-species stands
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Geotechnical and hyperspectral dataset for gold tailings
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A synthetic database of hyperspectral ocean optical properties
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RGB and VIS/NIR Hyperspectral Imaging Data for 90 Rice Seed Varieties
<p>The dataset contains 90 rice seed species and 96 kernels per species resulting in 8,640 rice seed kernels in total. The dataset was collected in 2017 using the following two imaging systems:</p> <ol> <li>Visible - Near Infrared (VIS/NIR) Hyperspectral Imaging Device System (~385nm - ~1000nm) consisting of a Specim V10E Imaging Spectrograph and Hamamatsu ORCA-05G CCD camera.</li> <li>RGB - Fujifilm X-M1 with a 35mm/F2.0, ISO 400.</li> </ol> <p>For each species, 96 kernels have been captured in two imaging bundles with 48 kernels in each bundle. For each imaging bundle, the 48 kernels were carefully positioned on a sheet of white paper and arranged in an <code>8x6</code> matrix. This rice seed matrix was then positioned on a translational stage and imaged using the HSI and RGB cameras described above.</p> <p>The following three files result from a single acquisition:</p> <ul> <li><code>.hdr</code>: The HSI ENVI header file (More information on the ENVI format can be found at the <a href="https://www.harrisgeospatial.com/docs/ENVIHeaderFiles.html">Harris Geospatial Solutions</a> documentation.</li> <li><code>.raw</code>: The HSI datacube data.</li> <li><code>.jpg</code>: The RGB image.</li> </ul> <p>The filename convention used is the (short) species name followed by a dash, followed by the bundle number (i.e. 1 or 2), followed by the filename suffix. For instance, the data for the <code>BC15</code> rice seed variety are contained in the following 6 files:</p> <ul> <li><code>BC15-01.hdr</code></li> <li><code>BC15-01.raw</code></li> <li><code>BC15-01.jpg</code></li> <li><code>BC15-02.hdr</code></li> <li><code>BC15-02.raw</code></li> <li><code>BC15-02.jpg</code></li> </ul> <p>The data were captured in 9 batches across multiple days. All the data from the same batch are contained in a dedicated folder. For instance the folder <code>Data-VIS-20170111-2-room-light-off</code>indicates that the data are in the VIS/NIR range, captured on the 11th of January 2017 and this was the second batch for that day with the room lights off. Two halogen bulbs were used for illumination and these were accurately positioned to provide balanced lighting across the scene. To ensure stability, the halogen bulbs were switched on and allowed to reach constant operating temperature before the data were acquired in a dark room to minimise any other sources of illumination variance.</p> <p>For the purposes of calibration each HSI image contains in the scene a 100% reflective spectralon tile which is a highly reflective Lambertian scatter. For the dark reference, each folder contains an HSI image with the lens-cap covering the camera. The dark reference can be founds in each folder under the filename <code>black.hdr</code>/<code>black.raw</code>.</p> <p>A full index of the data for each species is provided in the <code>index.csv</code> file. The file contains the following columns:</p> <ul> <li>Species Full Name: The full species name (as used in filenames).</li> <li>Species Short Name: A shorthand of the species name.</li> <li>Bundle Number: Imaging Bundle Number (each bundle contains 48 kernels) every species has 2 bundles.</li> <li>Folder: The name of the folder containing the data (as described above where each folder contains a batch of images captured in a single imaging session).</li> <li>File Name: The stem of the filename. Note: that there are 3 suffixes for each stem (<code>.hdr</code>, <code>.raw</code>, <code>.jpg</code>)</li> </ul> <p>The HSI system was used to capture 256 wavelengths in this experiment and the exact wavelengths corresponding to the data provided are included in the file <code>wavelengths.csv</code>.</p> <p>Both camera systems were fixed on a rigid frame for the duration of the experiments. To permit possible registration between the two cameras, a chessboard pattern has been imaged and the acquired files are also contained in the folder <code>chessboard</code>.</p> <p><strong>Note:</strong> The bundle <code>01</code> for the species <code>NDC1</code> was originally acquired during the batch <code>Data-VIS-20170111-2-room-light-off</code>. However, the file was corrupted and hence, the acquisition was repeated during the batch <code>Data-VIS-20170203-1-room-light-off</code>. As a result, the <code>NDC1-01</code> files are in the <code>Data-VIS-20170203-1-room-light-off</code> folder.</p>
A Dataset for Evaluating Blood Detection in Hyperspectral Images
<p>The sensitivity of hyperspectral imaging (imaging spectroscopy) to haemoglobin derivatives makes it a promising tool for detection and classification of blood. However, due to complexity and high dimensionality of hyperspectral images, the development of hyperspectral blood detection algorithms is challenging. To facilitate their development, we present a new hyperspectral blood detection dataset. This dataset consists of 14 hyperspectral images (ENVI format) of a mock-up scene containing blood and visually similar substances (e.g. artificial blood or tomato concentrate). Images were taken over a period of three weeks and differ in terms of background composition and lighting intensity. To facilitate the use of data, the dataset includes an annotation of classes: pixels where blood and similar substances are visible have been marked by the authors. The main intention behind the dataset is to serve as testing data for Machine Learning methods for hyperspectral target detection and classification.</p>
Data from: Biomimicry of iridescent, patterned insect cuticles: comparison of biological and synthetic, cholesteric microcells using hyperspectral imaging
<p>Biological systems inspire the design of multifunctional materials and devices. However, current syynthetic replicas rarelyy capture the range of structural complexityy observed in natural materials. Prior to the definition of a biomimetic design, a dual investigation with a common set of criteria for comparing the biological material and the replica is required. Here, we deal with this issue by addressing the non-trivial case of insect cuticles tessellated with polygonal microcells with iridescent colors due to the twisted cholesteric organization of chitin fibers. By using hyperspectral imaging within a common methodology, we compare, at several length scales, the textural, structural and spectral properties of the microcells found in the two-band cuticle of the scarab beetle <i>Chrysina gloriosa</i> with those of the polygonal texture formed in flat films of cholesteric liquid crystal oligomers. The hyperspectral imaging technique offers a unique opportunity to reveal the common features and differences in the spectral-spatial signatures of biological and synthetic samples at a 6-nm spectral resolution over 400 nm-1000 nm and a spatial resolution of 150 nm. The biomimetic design of chiral tessellations is relevant to the field of non-specular properties such as deflection and lensing in geometric phase planar optics.</p>
Superpixel and low-rank double-sparse regression hyperspectral unmixing
<p>These are data and code for the paper "Superpixel and low-rank double-sparse regression hyperspectral unmixing"</p>
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.
Data from: Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data
Forests are a major component of the global carbon cycle, and accurate estimation of forest carbon stocks and fluxes is important in the context of anthropogenic global change. Airborne laser scanning (ALS) data sets are increasingly recognized as outstanding data sources for high-fidelity mapping of carbon stocks at regional scales. We develop a tree-centric approach to carbon mapping, based on identifying individual tree crowns (ITCs) and species from airborne remote sensing data, from which individual tree carbon stocks are calculated. We identify ITCs from the laser scanning point cloud using a region-growing algorithm and identifying species from airborne hyperspectral data by machine learning. For each detected tree, we predict stem diameter from its height and crown-width estimate. From that point on, we use well-established approaches developed for field-based inventories: above-ground biomasses of trees are estimated using published allometries and summed within plots to estimate carbon density. We show this approach is highly reliable: tests in the Italian Alps demonstrated a close relationship between field- and ALS-based estimates of carbon stocks (r2 = 0·98). Small trees are invisible from the air, and a correction factor is required to accommodate this effect. An advantage of the tree-centric approach over existing area-based methods is that it can produce maps at any scale and is fundamentally based on field-based inventory methods, making it intuitive and transparent. Airborne laser scanning, hyperspectral sensing and computational power are all advancing rapidly, making it increasingly feasible to use ITC approaches for effective mapping of forest carbon density also inside wider carbon mapping programs like REDD++.
Hyperspectral and LiDAR data of the Botanical Garden of Rio de Janeiro
<p>In this repository you can find a hyperspectral image, a LiDAR point cloud and a shapefile of polygons of individual tree crowns from the the Botanical Garden of Rio de Janeiro. For more details refer to <a href="https://doi.org/10.1016/j.ufug.2024.128362">Ferreira et al. (2024)</a>.</p>
Hyperspectral imaging dataset
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Hyperspectral Multipoint High-Speed Confocal Microscopy Supporting Information Movies
<p><strong>S1 Movie. Neuronal growth in a zebrafish embryo</strong>. Sensory neurons (green) are labeled with GFP and motor neurons (magenta) are labelled with mCherry. The video was captured with filter configuration 2 (see section "System Dichroics and Emission Filters") frames were taken every minute.</p> <p> </p><p><strong>S2 Movie. Video of mitosis in a live <em>Xenopus laevis</em> embryo.</strong> The cellular membranes (magenta) is labelled with mTagBFP::CAAX; chromatin (blue) is labelled with miRFP670::H2B; and mitotic spindles (green) are labelled with mCherry:α-tubulin. One frame of 75 ms exposure time was captured every 5 seconds. The video was captured with filter configuration 2 (see section "System Dichroics and Emission Filters").</p> <p></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.