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100 results for “hyperspectral images”
Hyperspectral image using Specim IQ camera from Chatzivariti Winery (survey date 2021-07-23) #20
<p>The image contains the hyperspectral data from Chatzivariti Winery captured with Specim IQ hyperspectral camera. The grape variety of the field is Traminer (Gewürztraminer). The image captured at 2021-07-23 (#20) and it was processed using Specim IQ Studio.</p>
Hyperspectral image using Specim IQ camera from Chatzivariti Winery (survey date 2021-08-13) #17
<p>The image contains the hyperspectral data from Chatzivariti Winery captured with Specim IQ hyperspectral camera. The grape variety of the field is Xinomavro. The image captured at 2021-08-13 (#17) and it was processed using Specim IQ Studio.</p>
HeiPorSPECTRAL - the Heidelberg Porcine HyperSPECTRAL Imaging Dataset of 20 Physiological Organs
<p>Hyperspectral Imaging (HSI) is a relatively new medical imaging modality that exploits an area of diagnostic potential formerly untouched. Although exploratory translational and clinical studies exist, no surgical HSI datasets are openly accessible to the general scientific community. To address this bottleneck, this publication releases HeiPorSPECTRAL (<a href="https://www.heiporspectral.org">https://www.heiporspectral.org</a>), the first annotated high-quality standardized HSI dataset. It comprises 5,758 spectral images acquired with the TIVITA Tissue and annotated with 20 physiological porcine organs in a total number of 11 pigs. Each HSI image features a resolution of 480 x 640 pixels acquired over the 500-1000 nm wavelength range. The acquisition protocol has been designed such that the variability of organ spectra as a function of several parameters including the camera pose and the individual can be assessed. A comprehensive technical validation confirmed both the quality of the raw data and the annotations. We envision potential reuse within this dataset, but also its reuse as baseline data for future research questions outside this dataset, such as the detection of pathologies.</p>
Simulation of hyperspectral imaging recordings of the human exposed cortex
<p>This dataset contains the results of the simulation of a hyperspectral imaging recording of the exposed cortex of a human brain. Monte-Carlo (MC) simulations of the propagation of visible and near-infrared light in the exposed cortex of human were carried out, and the results of the MC simulation were processed to produce an hypercube, i.e., a set of images at various wavelength. Here, the light propagation of 41 wavelengths from 500 to 900nm were simulated and the images reconstructed.</p> <p>The simulation was based on the MCX software (<a href="https://github.com/fangq/mcx).%20">https://github.com/fangq/mcx). </a>The parameters of the simulations (at every wavelength) can be found in the ConfigurationFiles.zip file (1 per wavelength, named: cfg_WMC_wavelengthNumber). For the full details of the files structure, see: https://github.com/fangq/mcx.</p> <p>The domain used for the simulations was generated from an RGB image of the exposed cortex acquired during a surgical procedure. The image was segmented with a semi-automated procedure into three classes: gray matter, large blood vessel, and capillaries. Pixels were clustered into ten clusters using the K-means algorithm from the python library OpenCV (v4.8.0). The components of each cluster were manually sorted and attributed to the three classes. Three functional regions were defined as 1cm disk based on electrical brain stimulation findings, which lead to three other classes: activated grey matter, activated large blood vessel and activated capillaries.</p> <p>Once the image segmented into six classes, the brain volume was modelled. The binary segmentation masks were replicated along the z axis on 2cm to avoid any photon loss. Then the blood vasculature was modelled using morphological erosion. The structuring element used for the erosion was set to 0 (in pixels) for z=0 (in pixels) and was increased of 1 pixel while increasing z axis. The binary volumes of the six classes were finally merged together with a final isotropic resolution of 75um.</p> <p>The Hypercubes.mat file contains the results of the reconstructed ideal images at the surface of the brain, as calculated in reference [1], with a resolution of 116 x 116 pixels (0.5285 x 05285 mm). The absorption properties considered for this reconstruction are available in the ConfigurationFiles.zip file (variable: mua_WMC).</p> <p>The variables of the Hypercube.mat file are :</p> <ul> <li>Reflectance: the reflectance matrix (116 pixel x 116 pixels x 41 wavelengths).</li> <li>Wavelength: the wavelength vector (41 wavelengths)</li> <li>Mean_path_length: the image of the mean photon pathlength for each pixel and each wavelength (116 pixel x 116 pixels x 41 wavelengths)</li> </ul> <p>Reference:</p> <p>[1] Yao, R., Intes, X. and Fang, Q., “Direct approach to compute Jacobians for diffuse optical tomography using perturbation Monte Carlo-based photon ‘replay,’” Biomed. Opt. Express 9(10), 4588 (2018).</p> <p> </p>
Rapid species discrimination of similar insects using hyperspectral imaging and lightweight edge artificial intelligence
Open the record for dataset details and reuse information.
Multi-sensor dataset for testing merge of Hyperspectral, HD and 3D cloud information for image recognition
<p>This data contain multisensor image dataset constructed for benchmarking purposes. It contains multiple images of the constructed scenes -- on which objects made of different materials are placed to test image recognition scenarios. The scene is recorded from various angles by imagining sensors, i.e. HSI camera, HD camera on mobile chassis and MS Kinect to provide complete information.<br> </p> <p><strong>Equipment</strong><br> The imaging was performed with use of three devices for three different approaches to data. Those three devices' imaging characteristics are widely different when it comes to angle and resolution which required them to be separately positioned to acquire the matching images. Therefore while HD camera was being transferred on the moving platform (chassis), both Kinect and SOC710 were placed on a stationary position which was moved between the frames by hand.</p> <p><em>Hyperspectral data</em></p> <p><br> Hyperspectral data acquisition was performed with Surface Optics SOC710 camera. This camera records spectra at VNIR range $377-1046$ nm; the output image has dimensions $696 \times 520$ with 128 bands and $12$ bit dynamic range.</p> <p>The camera is equipped with sensor line translation unit and can be used from static stand as a conventional camera (i.e. it does not require mechanical translation of the observed sample or rotary stand, as in traditional ‘push broom’ hyperspectral cameras). The lighting was provided with four ambient lamps and adjusted for each scenario separately, so that most of the dynamic range of the camera was used and image saturation is avoided. Captured hyperspectral images were subject to a standard calibration procedure, including: the removal of a dark frame, spectral and radiometric calibration as well as reflectance normalization using the calibration panel. </p> <p><em>3D point clouds</em></p> <p><br> The Kinect sensor incorporates several advanced sensing hardware. The depth sensor consists of the IR projector combined with the IR camera, which is a monochrome complementary metaloxide semiconductor (CMOS) sensor. The IR projector is an IR laser that passes through a diffraction grating and turns into a set of IR dots. The relative geometry between the IR projector and the IR camera as well as the projected IR dot pattern are known. If we can match a dot observed in an image with a dot in the projector pattern, we can reconstruct it in 3D using triangulation. Because the dot pattern is relatively random, the matching between the IR image and the projector pattern can be done in a straightforward way by comparing small neighborhoods using, for example, normalized cross correlation. The depth value is encoded with gray values; the darker a pixel, the closer the point is to the camera in space. The black pixels indicate that no depth values are available for those pixels. This might happen if the points are too far (and the depth values cannot be computed accurately), are too close (there is a blind region due to limited fields of view for the projector and the camera), are in the cast shadow of the projector (there are no IR dots), or reflect poor IR lights </p> <p><em>HD Images</em></p> <p><br> The HD images were acquired using 5 Megapixel HD camera mounted on a mobile chassis made by Dawn Robotics, that allowed the camera to be moved freely on the scene. Both camera and mobile chassis was controlled by a Raspberry PI unit which was also responsible to position the camera in accord to the data being collected by other sources. <br> </p> <p><strong>Data</strong></p> <p>The dataset consists of three scenes consisting of various objects -- minerals, fruit, wood plastic and metal -- placed on a stand. The objects, depending on the view are partially covered and seen from different perspective. Each scene is captured from 8 different angles.</p> <p>Scene 1 (denoted <em>SceneEagle</em>) uses mostly inorganic materials, such as wood, metal, plastic and glass all placed on the vertical stand.<br> Scene 2 (<em>SceneFruit</em>) uses fruits normal and artificial, that are similar on HD photography and 3D cloud of point, but differs in hyperspectral image.<br> Scene 3 (<em>SceneFruit2</em>) uses the fruits but also includes printed full colour images of same fruits that are 2-dimensional.</p> <p> </p> <p>The data are formatted as follows:<br> - The HIS images are available in both \text{*.hdr} and \text{*.cube} formats. The separate files with calibrating panel is provided for each frame.<br> - Kinect clouds are provided in \text{*.obj} format, typical for Kinect output files.<br> - Matched Hyperspectral clouds are also provided as \text{*.obj} files<br> - HD photo files are provided in \text{*.jpg} files.<br> </p> <p><br> <strong>Acknowledgements</strong></p> <p>This work has been supported by the National Science Centre, based on decision no. DEC2012/07/N/ST6/03656.</p> <p> </p>
Hyperspectral images of the beehive panel from Slovenia
<p>Four hyperspectral imaging cameras were used for scanning cultural heritage objects provided by the Slovenian Ethnographic Museum. The objects were beehive panels from the museum collection. HIS cameras used were: FX10, FX17, SWIR, MWIR, all produced by SPECIM. Hyperspectral image files include the standard output generated when acquiring hyperspectral images with the Lumo software tool (SPECIM), including white and dark references. These files can be open in diverse other tools, including Matlab. </p>
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>
Surface plasmons-phonons for mid-infrared hyperspectral imaging
<p>Dataset for the hyperspectral imaging of spike proteins of the severe acute respiratory syndrome coronavirus (SARS-CoV) using the synergistic plasmon-phonon hyperspectral bioimaging system.</p>
Hyperspectral and Polarization images of an experimental oil painting
<p>This repository contains 4 hyperspectral images in the VNIR range (400 - 1000 nm) and 4 hyperspectral images in the SWIR range (1000 - 2500 nm) of an experimental oil painting.</p> <p>Each scene (P0,P1,P2,P3) is associated with the capturing with a linear polarization filter placed in front of the hyperspectral cameras. The rotation angles of the polarizers are found in the spreadsheet 'theta.xlsx'</p> <p>Please cite as:</p> <p>Grillini, Federico, et al. "Relationship between reflectance and degree of polarization in the VNIR-SWIR: A case study on art paintings with polarimetric reflectance imaging spectroscopy." <em>PloS one</em> 19.5 (2024): e0303018.</p>
Hyperspectral images of patches at different stages of degraded alpine meadows
<p>This data contains hyperspectral images of degraded alpine meadow patches in four stages, which are:active patches (Stage 0), inactive patches (Stage 1), recovering patches (Stage 2), and healthy alpine meadow (Stage 3).</p>
Sedimentary structure discrimination with hyperspectral imaging in sediment cores
<p>The LDB17_P11Ax (IGSN: TOAE0000000243); Datation Age 1040 +/- 30 to 2017 CE by core correlation, 14C, lamina counting) core from the Bourget Lake (France) was analyzed in 2018 by hyperspectral imaging. We studied the potential of hyperspectral sensor to image a sediment cores and created machine learning models. The hyperspectral images were acquired in order to develop quantitative (estimating particle size and loss on ignition) and qualitative (detection of instantaneous events or lamina) methods.<br> All these methods allow to reconstruct the past environment and climate at high resolution (pixel size: 50-250 microns) and without destroying the sample for archiving for future analysis.<br> These images have been valorized in publications for the detection of instantaneous events with hyperspectral and combined with XRF data, for the combination of the two images into a composite image.<br> image (.hdr, .dat, .jpg)</p>
Image dataset: Applicability of hyperspectral imaging during salinity stress in rice for tracking Na+ and K+ levels in planta
<p>The ratio of Na<sup>+</sup> and K<sup>+</sup> is an important determinant of the magnitude of Na<sup>+</sup> toxicity and osmotic stress in plant cells. Traditional analytical approaches involve destructive tissue sampling and chemical analysis, where real-time observation of spatio-temporal experiments across genetic or breeding populations is unrealistic. Such an approach can also be very inaccurate and prone to erroneous biological interpretation. Analysis by Hyperspectral Imaging (HSI) is an emerging non-destructive alternative for tracking plant nutrient status in a time-course with higher accuracy and reduced cost for chemical analysis. In this study, the feasibility and predictive power of HSI-based approach for spatio-temporal tracking of Na<sup>+</sup> and K<sup>+</sup> levels in tissue samples was explored using a panel recombinant inbred line (RIL) of rice (Oryza sativa L.; salt-sensitive IR29 x salt-tolerant Pokkali) with differential activities of the Na<sup>+</sup> exclusion mechanism conferred by the SalTol QTL. In this panel of RILs the spectrum of salinity tolerance was represented by FL499 (super-sensitive), FL454 (sensitive), FL478 (tolerant), and FL510 (super-tolerant). Whole-plant image processing pipeline was optimized to generate HSI spectra during salinity stress at EC = 9 dS m<sup>-1</sup>. Spectral data was used to create models for Na<sup>+</sup> and K<sup>+</sup> prediction by partial least squares regression (PLSR). Three datasets, i.e., mean image pixel spectra, smoothened version of mean image pixel spectra, and wavelength bands, with wide differences in intensity between control and salinity facilitated the prediction models with high R<sup>2</sup>. The smoothened and filtered datasets showed significant improvements over the mean image pixel dataset. However, model prediction was not fully consistent with the empirical data. While the outcome of modeling-based prediction showed a great potential for improving the throughput capacity for salinity stress phenotyping, additional technical refinements including tissue-specific measurements is necessary to maximize the accuracy of prediction models.</p>
HSICityV2: Urban Scene Understanding via Hyperspectral Images
<p>Light in all spectrum travel in the physical world. The trichromatism (RGB) human vision captures and understands it. Machine vision makes an analogy which use RGB camera for semantic segmentation and scene understanding. We argue that such machine vision suffers from metamerism, that different objects may appear in same RGB color while actually distinctive in spectrum. While learning based solutions, especially deep learning, have been heavily explored, they do not solve the fundamental physical limitation. In this paper, we propose to use Hyperspectral images (HSIs), which capture hundreds of consecutive narrow bands from the real visible world and therefore metamerism no longer exists. In short, we aim to 'see beyond human vision'. In practice, we introduce a novel large scale high quality HSI dataset for semantic segmentation in cityscapes. Namely, Hyperspectral City dataset. The dataset contains 1330 HSIs which are captured in typical urban driving scenes. Each HSI has 1889×1422 spatial resolution and 128 spectral channels ranged from 450nm to 950nm. The dataset provides semantic annotation at pixel level which is done manually by professional annotators. We believe this dataset enables a new direction for scene understanding.</p>
HyperspectralBlueberries: a dataset of hyperspectral reflectance images of normal and defective blueberries
<p>The <strong>HyperspectralBluberries</strong> dataset consists of hyperspectral datacubes, which were acquired by an in-house assembled benchtop line scanning system, from 420 blueberries of two categories, including 210 sound fruit and 210 samples with various defects. The fruit samples were hand-picked from a commercial orchard. Each scanning event, which was done for an array of 42 samples, yields two files in image formats .bil (band-interleaved-by-line) and .hdr (header), which store the hyperspectral raw data and associated metadata, respectively, and are both necessary for loading hyperspectral data for processing. In addition to sample scanning, a white reference was also scanned, which can be used for standardizing spectral responses. As a result, there are 22 files in the dataset, totaling about 25 GB in file size. The sample file names are descriptive, indicating the blueberry category and number information. The dataset was used for developing machine learning models for differentiating between normal and defective blueberries, achieving an overall accuracy of 96.6%. Software programs for the modeling work are publicly available at: <a href="https://github.com/vicdxxx/Blueberry-Defect-Detection-by-Hyperspectral-Imaging">https://github.com/vicdxxx/Blueberry-Defect-Detection-by-Hyperspectral-Imaging.</a></p> <p>Details about the dataset curation and modeling experiments are described in the journal article: <a href="https://www.sciencedirect.com/science/article/pii/S2772375524000789">Deng, B., Lu, Y., Stafne, E. (2024). </a><a href="https://www.sciencedirect.com/science/article/pii/S2772375524000789">Fusing Spectral and Spatial Features of Hyperspectral Reflectance Imagery for Differentiating between Normal and Defective Blueberries. Smart Agricultural Technology</a>. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.atech.2024.100473" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.atech.2024.100473</a>. If you use the dataset in published research, please consider citing the dataset or the <a href="https://doi.org/10.1016/j.ecoinf.2024.102546">journal article</a>. Hopefully, you find the dataset useful. </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>
Datasets used for Automatic Acquisition of Non-Saturated Hyperspectral Images
<p>data-sets acquired for studying correlation between automatic exposure times and hyper-spectral images, with the aim of devising procedures for automatic acquisition of non-saturated hyper-spectral images</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>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.