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154 results for “multispectral”
DJI Mavic 3 Multispectral Edition spectral response
<p>Spectral responses for multispectral and RGB sensors on the DJI Mavic 3 Multispectral Edition.</p> <p>Optics Of Photosynthesis Lab, University of Helsinki, 2024.</p> <p>See <strong>README</strong> and Jani Kuurasuo's <a href="http://hdl.handle.net/10138/593074">thesis</a> for more details.</p> <p> </p>
FIGURE 11 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 11. Fossils (bones and teeth) and a mixture of fossils and sediment (sand and gravel) from Sample 3. 1, RGB image under daylight (D65). 2, RGB image under UV light. 3, Blue channel image (grayscale) under UV light. 4, Black and white image after segmentation of the blue channel image.
FIGURE 9. 1 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 9. 1, Reflectance spectra of sediment (sand and gravel) and fossils (bones and teeth) of Sample 3. 2, Fluorescence spectra (radiance in W/sr*cm2) of sediment (sand and gravel) and fossils (bones and teeth) of Sample 3.
FIGURE 10 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 10. Examples of spectral images of Sample 3 through spectral bands (420, 520, 620, 670, and 720 nm) taken under daylight. An RGB computed using the sRGB - standard RGB colour space image is also provided.
FIGURE 4 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 4. Examples of spectral images of Sample 1 through spectral bands (420, 520, 620, 670, and 720 nm) taken under daylight. An RGB image computed using the sRGB - standard RGB colour space is also provided.
FIGURE 8 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 8. RGB images of Sample 2 with ferrous particles, bones-teeth, sand-gravel and a mixture of bonesteeth and sand-gravel. 1, Under daylight (D65). 2, Under UV Light.
FIGURE 3. 1 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 3. 1, Reflectance spectra of sediment (sand and gravel) and fossils (bones and teeth) of Sample 1. 2, Fluorescence spectra (radiance in W/sr·cm2) of sediment (sand and gravel) and fossils (bones and teeth) of Sample 1.
FIGURE 6. 1 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 6. 1, Reflectance spectra of sediment (sand and gravel), fossils (bones and teeth) and ferrous sediment of Sample 2. 2, Fluorescence spectra (radiance in W/sr*cm2) of sediment (sand and gravel), fossils (bones and teeth) and ferrous sediment of Sample 2.
FIGURE 1. 1 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 1. 1, Wet sieving process of palaeontological samples using Freudenthal's technique. 2, Visual recognition and separation of teeth and bones using a binocular microscope and pincers.
FIGURE 7 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 7. Examples of spectral images of Sample 2 through spectral bands (420, 520, 620, 670 and 720 nm) taken under daylight. An RGB computed using the sRGB - standard RGB colour space image is also provided.
FIGURE 5 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 5. Mixture of bones-teeth and sand-gravel from Sample 1. 1, RGB image under daylight (D65). 2, RGB image under UV light. 3, Blue channel image under UV light. 4, Green channel image (grayscale) under UV light. 5, Black and white image after segmentation of the green channel image.
Gummern - Mining Waste Deposits Multispectral UAV Imagery
<h2>Abstract</h2> <p>Mining Waste Deposits Multispectral UAV Imagery from DJI Mavic 3M</p> <p>This depositry contains data generated within the European S34 project.</p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Mining Waste Deposits Multispectral UAV Imagery</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Mining Waste Deposits Multispectral UAV Imagery from DJI Mavic 3M</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>Multispectral, Mining Waste Deposits, UAV, Drone</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Gummern</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>UAV</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>06.09.2023</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>06.09.2023</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Other</p> </td> </tr> <tr> <td> <p>Fromat</p> </td> <td> <p>Other</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>0.05m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>0.25m</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 4326</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Enoc Sanz Ablanedo (esana@unileon.es)</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>UNILEON</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Enoc Sanz Ablanedo (esana@unileon.es)</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
PoLambRimetry dataset: Multispectral Mueller matrices of lamb brain
<h3>Using this dataset</h3> <p>This dataset is available for re-distribution and re-use (see license) under the following citation:</p> <p>Mieites Alonso, V., Anichini, G., Qi, J., O'Neill, K., Conde, O. M., & Elson, D. (2024). <strong>PoLambRimetry dataset: Multispectral Mueller matrices of lamb brain [Data set]</strong>. Zenodo. <a href="https://doi.org/10.5281/zenodo.11127947" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11127947</a></p> <h3>Dataset description</h3> <p>This dataset contains Multispectral Mueller matrices of lamb brain. A total of six fresh brain specimens were imaged, mainly in lateral and medial views, but also in sections. The wavelengths used are 450, 500, 550, 590, 650, and 680 nm. The dataset was generated using Python and contains a total of twenty matrices, following this folder structure:</p> <ul> <li>Specimen (1 to 6) <ul> <li>View: right or left hemispheres; cerebellum hemispheres; medial or lateral view; sections; frontal basal ganglia (bg) sections; pineal region; brain stem sections. <ul> <li>Data in hdf5 (data.h5) format filled with Python objects <ul> <li><em>M</em>: Mueller matrix. Dimensions: (6, 4, 4, 604, 642) corresponding to (wavelength, matrix row, matrix column, pixels row, pixels column). Python's numpy.array followed filled with numpy.float64.</li> <li><em>ref_img</em>: Reference grayscale image (at 450 nm). Dimensions: (604, 642) corresponding to (pixels row, pixels column). Python's numpy.array followed filled with numpy.float64.</li> <li><em>roi</em>: Region of interest. Its values are 1 inside the sample and 0 in the background. Dimensions: (604, 642) corresponding to (pixels row, pixels column). Python's numpy.array followed filled with numpy.int32.</li> <li><em>labels</em>: Labelled areas. There are 14 different white matter and gray matter structures. Dimensions: (604, 642) corresponding to (pixels row, pixels column).</li> <li><em>wvls</em>: Wavelengths in descending order. Dimensions: (6) corresponding to (wavelengths).</li> </ul> </li> <li>Figure representing the data inside data.h5 (data.png).</li> </ul> </li> </ul> </li> <li>labels.csv: CSV file containing the legend for the labels. WM and GM stand for white matter and gray matter, respectivelly.</li> </ul> <p>This dataset was used in a manuscript currently under peer review (May 2024). Upon publication, the embargo will be removed and we will update the information to provide a more detailed presentation of the dataset. </p>
A high-throughput multispectral imaging system for museum specimens
<p>We present an economical imaging system with integrated hardware and software to capture multispectral images of Lepidoptera with high efficiency. This method facilitates the comparison of colors and shapes among species at fine and broad taxonomic scales and may be adapted for other insect orders with greater three-dimensionality. Our system can image both the dorsal and ventral sides of pinned specimens. Together with our processing pipeline, the descriptive data can be used to systematically investigate multispectral colors and shapes based on full-wing reconstruction and a universally applicable ground plan that objectively quantifies wing patterns for species with different wing shapes (including tails) and venation systems. Basic morphological measurements, such as body length, thorax width, and antenna size are automatically generated. This system can increase exponentially the amount and quality of trait data extracted from museum specimens.</p>
Dataset of the paper "Numerical Study of the Optical Response of ITO-In2O3 Core-Shell Nanocrystals for Multispectral Electromagnetic Shielding"
<p>This dataset provides the raw data of the paper "Numerical Study of the Optical Response of ITO-In2O3 Core-Shell Nanocrystals for Multispectral Electromagnetic Shielding"</p>
A multispectral UAV Imagery dataset of wheat, soybean, and barley crops in East Kazakhstan
<p>This study introduces a dataset of crop imagery captured during the 2022 growing season in the Eastern Kazakhstan region. The images were acquired using a multispectral camera mounted on an unmanned aerial vehicle (DJI Phantom 4). The agricultural land, encompassing 27 hectares and cultivated with wheat, barley, and soybean, was subjected to five aerial multispectral photography sessions throughout the growing season. This facilitated thorough monitoring of the most important phenological stages of crop development in the experimental design, which consisted of 27 plots, each covering one hectare. The collected imagery underwent enhancement and expansion, integrating a sixth band that embodies the normalized difference vegetation index (NDVI) values, in conjunction with the original five multispectral bands (Red, Green, Blue, Infrared, and Near Infrared). This amplification enables a more effective evaluation of vegetation health and growth, rendering the enriched dataset a valuable resource for the progression and validation of crop monitoring and yield prediction models, as well as for the exploration of precision agriculture methodologies.</p>
A multispectral UAV Imagery dataset of wheat, soybean, and barley crops in East Kazakhstan
<p>This study introduces a dataset of crop imagery captured during the 2022 growing season in the Eastern Kazakhstan region. The images were acquired using a multispectral camera mounted on an unmanned aerial vehicle (DJI Phantom 4). The agricultural land, encompassing 27 hectares and cultivated with wheat, barley, and soybean, was subjected to five aerial multispectral photography sessions throughout the growing season. This facilitated thorough monitoring of the most important phenological stages of crop development in the experimental design, which consisted of 27 plots, each covering one hectare. The collected imagery underwent enhancement and expansion, integrating a sixth band that embodies the normalized difference vegetation index (NDVI) values, in conjunction with the original five multispectral bands (Blue, Green, Red, Red Edge and Near Infrared Red). This amplification enables a more effective evaluation of vegetation health and growth, rendering the enriched dataset a valuable resource for the progression and validation of crop monitoring and yield prediction models, as well as for the exploration of precision agriculture methodologies.</p>
A multispectral UAV Imagery dataset of wheat, soybean, and barley crops in East Kazakhstan
<p>This study introduces a dataset of crop imagery captured during the 2022 growing season in the Eastern Kazakhstan region. The images were acquired using a multispectral camera mounted on an unmanned aerial vehicle (DJI Phantom 4). The agricultural land, encompassing 27 hectares and cultivated with wheat, barley, and soybean, was subjected to five aerial multispectral photography sessions throughout the growing season. This facilitated thorough monitoring of the most important phenological stages of crop development in the experimental design, which consisted of 27 plots, each covering one hectare. The collected imagery underwent enhancement and expansion, integrating a sixth band that embodies the normalized difference vegetation index (NDVI) values, in conjunction with the original five multispectral bands (Blue, Green, Red, Red Edge and Near Infrared Red). This amplification enables a more effective evaluation of vegetation health and growth, rendering the enriched dataset a valuable resource for the progression and validation of crop monitoring and yield prediction models, as well as for the exploration of precision agriculture methodologies.</p>
A multispectral UAV Imagery dataset of wheat, soybean, and barley crops in East Kazakhstan
<p>This study introduces a dataset of crop imagery captured during the 2022 growing season in the Eastern Kazakhstan region. The images were acquired using a multispectral camera mounted on an unmanned aerial vehicle (DJI Phantom 4). The agricultural land, encompassing 27 hectares and cultivated with wheat, barley, and soybean, was subjected to five aerial multispectral photography sessions throughout the growing season. This facilitated thorough monitoring of the most important phenological stages of crop development in the experimental design, which consisted of 27 plots, each covering one hectare. The collected imagery underwent enhancement and expansion, integrating a sixth band that embodies the normalized difference vegetation index (NDVI) values, in conjunction with the original five multispectral bands (Blue, Green, Red, Red Edge and Near Infrared Red). This amplification enables a more effective evaluation of vegetation health and growth, rendering the enriched dataset a valuable resource for the progression and validation of crop monitoring and yield prediction models, as well as for the exploration of precision agriculture methodologies.</p>
Plant functional traits and corresponding satellite multispectral data for two sites in the Czech Republic
<p>This data set contains measurements of plant functional traits and corresponding satellite multispectral data for two sites in the Czech Republic – Lanžhot and Štítná throughout multiple campaigns. Plant functional traits were collected from representative trees selected at both study sites. At the Lanžhot site, a total of 16 trees of the following species were sampled: Quercus robur (4 trees), Quercus cerris L. (2 trees), Carpinus betulus (4 trees), Fraxinus angustifolia (4 trees), Tilia cordata (2 trees). At Štítná, a total of 10 Fagus sylvatica trees were sampled. Field campaigns were timed to cover different phenological stages of deciduous tree leaf development, from fresh leaf emergence, through full leaf development at the peak of the growing season, to autumn leaf senescence. In total, six field campaigns at Lanžhot during 2019 and 2020 and five field campaigns at Štítná during 2020 and 2021 were conducted.</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)
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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.