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154 results for “multispectral”
High Throughput Multispectral Image Processing with applications in Food Science
<p>Raw image samples for the PLoS ONE paper entitled "High Throughput Multispectral Image Processing with applications in Food Science".</p> <p>Segmented images for the PLoS ONE paper entitled "High Throughput Multispectral Image Processing with applications in Food Science".</p>
Fusion of Single and Integral Multispectral Aerial Images
<p>Abstract: <span>An adequate fusion of the most significant salient information from multiple input channels is essential for many aerial imaging tasks. While multispectral recordings reveal features in various spectral ranges, synthetic aperture sensing makes occluded features visible. We present a first and hybrid (model- and learning-based) architecture for fusing the most significant features from conventional aerial images with the ones from integral aerial images that are the result of synthetic aperture sensing for removing occlusion. It combines the environment’s spatial references with features of unoccluded targets that would normally be hidden by dense vegetation. Our method outperforms state-of-the-art two-channel and multi-channel fusion approaches visually and quantitatively in common metrics, such as mutual information, visual information fidelity, and peak signal-to-noise ratio. The proposed model does not require manually tuned parameters, can be extended to an arbitrary number and arbitrary combinations of spectral channels, and is reconfigurable for addressing different use cases. We demonstrate examples for search and rescue, wildfire detection, and wildlife observation.</span> <strong><br></strong></p>
GloSoFarID: Global multispectral dataset for Solar Farm IDentification in satellite imagery
Open the record for dataset details and reuse information.
Hullerbusch beech forest, Germany, captured by UAV-based multispectral imaging
<p>This data set contains data products of a multispectral UAV survey over the Hullerbusch beach forest, located in southern Mecklenburg–Western Pomerania, Germany. As survey drone, the DJI M300 with RTK antenna was employed and the multispectral image data was captured with a Micasense Altum multispectral camera. Data were processed in Agisoft Metashape 1.7, with the orthomosaic converted to reflectance.</p> <p>Data products: <br> Orthomosaic 5band reflectance 3.1 cm (.tif)<br> Orthomosaic RGB preview 10 cm (.tif)<br> DSM 3.1 cm (.tif)<br> RGB Dense Point Cloud 110 mio pts (.laz)<br> Agisoft Processing report (.pdf)<br> Flight shapefile (.kml)<br> <br> Center coordinates: 53.322907N, 13.441929E<br> Coordinate reference system: WGS84/UTM zone 32N<br> Survey area: 88503 m2</p> <p>Agisoft Metashape average camera location error estimates:<br> X: 13.6 cm; Y: 55.9 cm; Z: 7.2 cm; Total error: 58.1 cm<br> </p>
Multispectral imagery of macroalgae in Illas Cíes (NW Spain)
<p>This dataset focuses on an intertidal coastal environment in Illas Cíes, NW Spain. It includes multispectral orthoimages (5 bands), photoquadrat images, and manually segmented shapefiles. The dataset spans 4 months and contains <strong>20 orthoimages</strong> (5 per month, one for each band: Blue, Green, Red, Red edge, and NIR). There are also <strong>164 photoquadrat</strong> images and 8 shapefiles with a total of <strong>949 polygons</strong>. The dataset classifies the intertidal area into <strong>9 categories</strong>: <em>Codium</em> spp., <em>Ulva</em> spp., <em>C. peregrina</em>, <em>B. bifurcata</em>, <em>E. selaginoides</em>, <em>S. polyschides</em>, <em>S. muticum</em>, Inert and water. </p>
Comparison and assessment of different object-based classifications using machine learning algorithms and UAVs multispectral imagery in the framework of precision agriculture
<p>Supplementary material of the paper</p>
Online Fusion of Multi-resolution Multispectral Images with Weakly Supervised Temporal Dynamics
<p>This is the data set for experiments of satellite images of Oroville dam site in paper: Online Fusion of Multi-resolution Multispectral Images with Weakly Supervised Temporal Dynamics. Inside the zip file, there are 3 folders: 'HD-IMG-Database-Landsat-8', 'HD-IMG-Database-Landsat-8-Qest' and 'MODIS_250'.</p>
PI Data: Multispectral and Thermal Surface Imagery and Surface Elevation Mosaics (CAMSPEC-AIR)
<p>This data set contains high-resolution image products (orthomosaics) acquired from midsized uncrewed aerial systems that have been processed for value-added quality. The instrument itself, a multispectral imager, the Altum by Micasense, captures six spectral bands (red, green blue, NIR, red edge, and LWIR/thermal1) as radiance, which is converted to reflectance. The code used to develop these images first uses tools from the Micasense Python library2 to apply dark level corrections, row gradient corrections, and radiometric corrections. Next, it uses the processing API from Agisoft Metashape software to align and mosaic the processed imagery, following the processes developed by the USGS' structure from motion workflow documentation.3 Captures at different altitudes (recorded in MSL) produce an orthomosaic, a tif image containing information related to the six spectral bands, and a digital elevation model (DEM), a tif image containing information related to the elevation of the surveyed terraine. Metadata included in every image can be used to extract lat, lon, and reflectance values.</p> <p> </p> <p>Cite as: Tagestad, J., Nelson, K., Goldberger, L., Gonzalez-Hirshfeld, I. PI Data: Multispectral and Thermal Surface Imagery and Surface Elevation Mosaics (CAMSPEC-AIR). (02/04/2023 – 02/08/2023) ARM Data Center [Dataset]. DOI: 10.5439/1969041. <a href="http://arm.gov/capabilities/instruments/camspec-air">https://arm.gov/capabilities/instruments/camspec-air</a> , 2023.</p>
PI Data: Multispectral and Thermal Surface Imagery and Surface Elevation Mosaics (CAMSPEC-AIR)
<p>This data set contains high-resolution image products (orthomosaics) acquired from midsized uncrewed aerial systems that have been processed for value-added quality. The instrument itself, a multispectral imager, the Altum by Micasense, captures six spectral bands (red, green blue, NIR, red edge, and LWIR/thermal1) as radiance, which is converted to reflectance. The code used to develop these images first uses tools from the Micasense Python library2 to apply dark level corrections, row gradient corrections, and radiometric corrections. Next, it uses the processing API from Agisoft Metashape software to align and mosaic the processed imagery, following the processes developed by the USGS' structure from motion workflow documentation.3 Captures at different altitudes (recorded in MSL) produce an orthomosaic, a tif image containing information related to the six spectral bands, and a digital elevation model (DEM), a tif image containing information related to the elevation of the surveyed terraine. Metadata included in every image can be used to extract lat, lon, and reflectance values.</p> <p> </p> <p>Cite as: Tagestad, J., Nelson, K., Goldberger, L., Gonzalez-Hirshfeld, I. PI Data: Multispectral and Thermal Surface Imagery and Surface Elevation Mosaics (CAMSPEC-AIR). (07/09/2022 – 07/18/2022) ARM Data Center [Dataset]. DOI: 10.5439/1962600. https://arm.gov/capabilities/instruments/camspec-air , 2023.</p>
OPTIMA - RGB colour images and multispectral images (including LabelImg annotations)
<p>The images and annotations (LabelImg) in this folder are acquired during the H2020 OPTIMA project. The images were acquired in orchards, vineyards and fields in three European countries (France, Italy, Spain). The images represent three diseases in three crops: apple scab in apple, alternaria in carrot, downy mildew in grape. The txt files contain the bounding box locations for the diseases (bounding box detection for YOLOv5 object detection). </p> <p>The folder contains three subfolders:</p> <ol> <li>apple_applescab <ol> <li>ms: multispectral images acquired on 10-05-2022 in Spain with the Silios multispectral camera</li> <li>rgb: rgb colour images acquired on 27-05-2021 and 10-06-2021 in Spain with the NEON-202B-JT2-X smart-camera</li> </ol> </li> <li>carrot_alternaria <ol> <li>ms: multispectral images acquired on 23-09-2021 and 24-09-2021 in France with the Silios multispectral camera</li> <li>rgb: rgb colour images acquired on 02-09-2021, 24-09-2021, and 04-11-2021 in France with the NEON-201B-JT2-X smart-camera</li> </ol> </li> <li>grape_downymildew <ol> <li>ms: multispectral images acquired on 06-06-2019 in Italy with the IMEC multispectral camera</li> <li>rgb: rgb colour images acquired on 23-07-2021 in Italy with the NEON-201B-JT2-X smart-camera</li> </ol> </li> </ol>
Non-invasive Imaging of Muscle Structure in Duchenne Muscular Dystrophy Using Multispectral Optoacoustic Tomography
ClinicalTrials.gov study NCT03490214. IPD Sharing: NO. Countries: 1. Publications: 1.
Multispectral Fluorescence as a Tool to Separate Healthy and Disease Related Lymphatic Anatomies in Prostate Cancer.
ClinicalTrials.gov study NCT05120973. IPD Sharing: NO. Countries: 1. Publications: 1.
Multispectral Optoacoustic Tomography (MSOT) for the Evaluation of Disease Activity in Inflammatory Bowel Diseases (IBD)
ClinicalTrials.gov study NCT02622139. IPD Sharing: NO. Countries: 1. Publications: 2.
Non Invasive Characterization of Pediatric Inflammatory Bowel Diseases Using Multispectral Optoacoustic Tomography
ClinicalTrials.gov study NCT04650867. IPD Sharing: NO. Countries: 1. Publications: 10.
Dataset of aerial photographs acquired with UAV using a multispectral (Green, Red and Near-infrared) camera for cherry tomato (Solanum lycopersicum var. cerasiforme) monitoring
Open the record for dataset details and reuse information.
Multispectral Radiometry percent reflectance:The influence of natural enemies on plant community composition and productivity
The purpose of this experiment is to determine the influences of natural enemies, including plant pathogenic fungi and insect pests, influence plant community composition, productivity, and diversity over time. The experiment is being conducted in an old field that is burned every other year. Within the old field, there are 8 blocks, and within each block there are 6 treatments: foliar fungicide, soil drench fungicide, foliar insecticide, mammal exclosure, the combination of all enemy suppression tactics (pesticides and mammal exclosure), and a nontreated control. The pesticides are applied repeatedly throughout the growing season. Within the plots, community productivity, species composition, percent cover, and pest damage are being quantified over time.
Data from: Human retinal pigment epithelium: in vivo cell morphometry, multispectral autofluorescence, and relationship to cone mosaic
Purpose: To characterize in vivo morphometry and multispectral autofluorescence of the retinal pigment epithelial (RPE) cell mosaic and its relationship to cone cell topography across the macula. Methods: RPE cell morphometrics were computed in regularly spaced regions of interest (ROIs) from contiguous short-wavelength autofluorescence (SWAF) and photoreceptor reflectance images collected across the macula in one eye of 10 normal participants (23–65 years) by using adaptive optics scanning light ophthalmoscopy (AOSLO). Infrared autofluorescence (IRAF) images of the RPE were collected with AOSLO in seven normal participants (22–65 years), with participant overlap, and compared to SWAF quantitatively and qualitatively. Results: RPE cell statistics could be analyzed in 84% of SWAF ROIs. RPE cell density consistently decreased with eccentricity from the fovea (participant mean ± SD: 6026 ± 1590 cells/mm2 at fovea; 4552 ± 1370 cells/mm2 and 3757 ± 1290 cells/mm2 at 3.5 mm temporally and nasally, respectively). Mean cone-to-RPE cell ratio decreased rapidly from 16.6 at the foveal center to <5 by 1 mm. IRAF revealed cells in six of seven participants, in agreement with SWAF RPE cell size and location. Differences in cell fluorescent structure, contrast, and visibility beneath vasculature were observed between modalities. Conclusions: Improvements in AOSLO autofluorescence imaging permit efficient visualization of RPE cells with safe light exposures, allowing individual characterization of RPE cell morphometry that is variable between participants. The normative dataset and analysis of RPE cell IRAF and SWAF herein are essential for understanding microscopic characteristics of cell fluorescence and may assist in interpreting disease progression in RPE cells.
Laser cladding multispectral coaxial monitoring
<p>This is the first dataset containing data for some single laser cladding tracks when they were observed with NIR and S/MWIR cameras and different IR filters.</p>
Multispectral drone images of cocoa agroforestry in Cote d'Ivoire - Raw images
<p>This repository hosts the raw multispectral imagery dataset from drone surveys of cocoa production plots in the Central-West Region of Côte d'Ivoire.</p><p>The dataset encompasses high-resolution multispectral images from 10 cocoa production plots. These images were captured using the DJI Phantom 4 Multispectral (P4M) drone, fitted with an array of sensors capable of capturing data in RGB, red, green, blue, red edge, and near-infrared bands. </p><p>The study area comprises diverse cocoa agroforestry systems, recorded to reflect the varying structural complexities and stages of growth within the plots.</p><p>All imagery was acquired during UAV flights on May 4 and 5, 2022, between the hours of 10 AM and 2 PM (UTC). These flights were carefully scheduled to ensure optimal lighting conditions and minimal shadow interference, conducive to high-quality data capture. The region's tropical climate, marked by consistent temperatures and regular rainfall, adds to the relevance of this dataset for agricultural and ecological studies.</p><p> </p>
Glacial sediment-rich meltwater plume investigation using a high-resolution multispectral sensor embedded in an Unmanned Aerial Vehicle
<p>Methodology video</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.