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74 results for “Hyperspectral Data”
SiEUGreen - Data for 'Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters'
<p>Dataset used in the scientific publication <a href="https://zenodo.org/record/4671852">'Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters'</a>. The data includes chemical contents of tomatoes that was measured, images and scripts used in the paper. The scripts here aim to predict tomato quality parameters, sugar content, acidity, sugar acid ratio and lycopene, of automatically segmented tomato through hyperspectral image reconstruction from single RGB image. The same data can also be found at the <a href="https://github.com/ZJiangsan/TomatoQualityPredictionOnAutomaticallySegmentedTomato">Github repository</a>. The data collection and scientific paper was produced by SiEUGreen partners at Norwegian Institute of Bioeconomy Research (NIBIO).</p>
Raw hyperspectral imaging data of Baltic Sea algae cultures
<p>This file archive contains the raw data from hyperspectral imaging of Baltic sea algae cultures performed on 16th of August, 2018 at the hyperspectral imaging laboratory of the Faculty of Information Technology, University of Jyväskylä, Finland.</p> <p>The dataset contains images of cultures of the following algal species in various dilutions and mixes:</p> <ul> <li> <p>Diatoma tenuis DTTV-1401</p> </li> <li> <p>Melosira arctica MATV-1402</p> </li> <li> <p>Scrippsiella hangoei (aka Apocalathium malmogiense) SHTV-1</p> </li> <li> <p>Kryptopendinium foliaceum KFF-1001</p> </li> <li> <p>Monoraphidinium sp. TV70</p> </li> <li> <p>Chlorella pyrenoidosa TV216</p> </li> </ul> <p>In addition, the dataset includes images of pure water samples, empty petri dishes and millimeter paper useful for transmittance calculations and size measurement.</p> <p>The imaging setup consisted of living samples pipeted on glass Petri dishes, with a halogen light source illuminating the dish from the bottom towards the camera on top.</p> <p>The signal in each image contains slight fluctuation in the spectral dimension due to the AC current light source used.</p>
Classification and identification of pinecones mulching on blueberry cultivation based on crop leaf characteristics and hyperspectral data
<p><span>Supplementary Figure S1: Spectra preprocessing before and after.; Table S1: The evaluation results of the classification model of leaf growth and physiology.; Table S2: The evaluation results of the classification model of VIs.; Table S3: The evaluation results of the classification model of VNIR.; Table S4: The evaluation results of the classification model of SWIR.</span></p>
MUESLI Hyperspectral & LiDar Data Set
<p>The data set contain the hyperspectral images and the corresponding LiDar data from the MUESLI project.</p> <p>The meta data is included in the tif files. For the spectral bands, a copy of the original hdr file is below:</p> <p>fwhm = {4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,4.5,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25,6.25}<br> wavelength = 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<p> </p> <p> </p> <p> </p>
Existing Urban Hyperspectral Reference Data
<p>This dataset supports the publication "Urban Hyperspectral Reference Data Availability and Reuse: State-of-the-Practice Review" DOI: 10.1111/phor.12508. The urban hyperspectral signatures of 9 pre-existing spectral libraries have been converted into .xlsx file formats and organized by material category. This reformatted and compiled dataset is uploaded to this open-source repository to promote FAIR (Findable, Accessible, Interoperable, Reusable) data principles and facilitate data preservation and reuse. </p>
Unmixing Autoencoder for Image Reconstruction from Hyperspectral Data
<p>The NIR handwriting imaging data and the noise simulated data of five <span>hydroxyl compounds: methanol, ethanol, 2-phenylethanol, 1-propanol, and 2-chloroethanol.</span></p>
Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Standard calibration
<p>Accompanying data to publication: <strong>Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging</strong></p> <p>Hyperspectral data with all their processing steps - normalization, ROI, subsets, masking - resulting in RABD indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.</p>
Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 1 - Phycocyanin & Chlorophyll a
<p>Accompanying data to publication: <strong>Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging</strong></p> <p>Hyperspectral data with all their processing steps - normalization, ROI, subsets, masking - resulting in RABD indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.</p>
Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging
<p>Accompanying data to publication: <strong>Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging</strong></p> <p>Excel files include hyperspectral indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.</p> <p>Scripts used for producing plots in the publication and supplementary material are available on Renku; see the Software section.</p> <p>Hyperspectral data are submitted separately due to their size; see the Related works.</p>
Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 2 - Phycocyanin
<p>Accompanying data to publication: <strong>Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging</strong></p> <p>Hyperspectral data with all their processing steps - normalization, ROI, subsets, masking - resulting in RABD indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.</p>
Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 2 - Phycocyanin & Chlorophyll a
<p>Accompanying data to publication: <strong>Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging</strong></p> <p>Hyperspectral data with all their processing steps - normalization, ROI, subsets, masking - resulting in RABD indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.</p>
A dataset including 2-year eddy covariance and hyperspectral data at Yunxiao mangrove flux tower
<p>A dataset including 2-year eddy covariance and hyperspectral data at Yunxiao mangrove flux tower. The data is used for producing key findings in a manuscript under review.</p>
Data from: Hyperspectral imaging reveals differential carotenoid and chlorophyll temporal dynamics and spatial patterns in Scots pine under water stress
<p>Data and codes associated with the manuscript '<span>Hyperspectral imaging reveals differential carotenoid and chlorophyll temporal dynamics and spatial patterns in Scots pine under water stress</span>'. </p>
Selected clear-sky FloX hyperspectral data
<p>Zip - Folders containing calibrated reflectance, reflected radiance and incoming radiance retrieved from measurements with FloX field spectrometers over Alfalfa (2018 in Grosseto, Italy) and grass (2018 in Julich, Germany) during clear sky conditions. The incoming radiance is measured with optics of hemispherical (~180°) field of view and the reflected radiance with conical (~25°) field of view.</p> <p>The data is organized in sub-folders inside the zip-repositories which are named according to the date of recording, containing CSV-files which hold the hyperspecteral information. One file is associated to the integrated full-range (FULL) spectrometer between 340nm and 1000nm, and the fluorescence-range (FLUO) spectrometer between 650nm and 810nm, containing incoming radiance, reflected radiance or reflectance, respectively. The first collumn contains the associated central wavelengths, the first row the time of recording, each subsequent field of the table contains the calibrated radiance (in W m<sup>-2</sup> sr<sup>-1</sup> nm<sup>-1</sup>) or reflectance (unitless) values.</p>
Data Set: Hyperspectral image unmixing with LiDAR data-aided spatial regularization
<p>Data set and matlab codes used for the experimental section of "Hyperspectral Image Unmixing With LiDAR Data-Aided Spatial Regularization"</p> <p>T. Uezato, M. Fauvel and N. Dobigeon, "Hyperspectral Image Unmixing With LiDAR Data-Aided Spatial Regularization," in <em>IEEE Transactions on Geoscience and Remote Sensing</em>, vol. 56, no. 7, pp. 4098-4108, July 2018.<br> doi: 10.1109/TGRS.2018.2823419<br> URL: <a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8347066&isnumber=8393475">http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8347066&isnumber=8393475</a><br> </p>
Data, research code, and metadata related to the Soils of the Upper Part of Itatiaia National Park (INP) for Pedology, Hyperspectral Soil Mapping, and Spectral studies
<p>Data, research code (in R language), and metadata related to the Soils of the Upper Part of Itatiaia National Park (INP) for Hyperspectral Soil Mapping</p> <p>Up to this point, this data, and code were used in the Doctoral thesis of Elias Mendes Costa and Yuri Andrei Gelsleichter, and the following publications.</p> <p>------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Dados, código de pesquisa (em linguagem R) e metadados relacionados aos Solos e ao Mapeamento da Parte Superior do Parque Nacional de Itatiaia (PNI) para o Mapeamento Hiperespectral de Solos</p> <p>Até este momento, esses dados, e códigos foram utilizados nas teses de doutorado de Elias Mendes Costa e de Yuri Andrei Gelsleichter e nas seguintes publicações.</p> <p> </p> <p>Gelsleichter, Y. A., Costa, E. M., dos Anjos, L. H. C., & Marcondes, R. A. T. (2023). Enhancing soil mapping with hyperspectral subsurface images generated from soil lab vis-SWIR spectra tested in southern Brazil. Geoderma Regional, e00641.<br> doi: https://doi.org/10.1016/j.geodrs.2023.e00641<br> link: https://www.sciencedirect.com/science/article/pii/S2352009423000378</p> <p>Costa, E. M., Pinheiro, H. S. K., Anjos, L. H. C. dos, Marcondes, R. A. T., & Gelsleichter, Y. A. (2020). Mapping soil properties in a poorly-accessible area. Revista Brasileira de Ciência Do Solo, 44.<br> doi: https://doi.org/10.36783/18069657rbcs20190107<br> link: https://www.rbcsjournal.org/article/mapping-soil-properties-in-a-poorly-accessible-area/<br> <br> Costa, E. M., dos Anjos, L. H. C., Pinheiro, H. S. K., Gelsleichter, Y. A., & Marcondes, R. A. T. (2020). Spatial Bayesian belief networks: A participatory approach for mapping environmental vulnerability at the Itatiaia National Park, Brazil. Environmental Earth Sciences, 79, 1–13.<br> doi: https://doi.org/10.1007/s12665-020-09099-9<br> link: https://link.springer.com/article/10.1007/s12665-020-09099-9#citeas</p> <p>The code is arranged to deliver the outputs in their respective folders.</p>
SNP genotype and hyperspectral reflectance data from: Ensembles of genomic and hyperspectral imaging-based prediction enable selection for reduced deoxynivalenol content in wheat grains
Open the record for dataset details and reuse information.
Data From: Hyperspectral leaf reflectance of grasses varies with evolutionary lineage more than with site
Open the record for dataset details and reuse information.
Data from: Hyperspectral imaging predicts differences in carbon and nitrogen status among representative biocrust functional groups of the Colorado Plateau
Open the record for dataset details and reuse information.
Data from: Hyperspectral imaging has a limited ability to remotely sense the onset of beech bark disease
Open the record for dataset details and reuse information.
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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.