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303 results for “hyperspectral”
Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the Wytham Woods site in the United Kingdom
<p>The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the Wytham Woods HYPERNETS site in the United Kingdom (WWUK). It is a subset of the complete data record which consists of the best quality WWUK measurements which could be used for satellite validation. </p> <p>The provided NetCDF files are the L2A hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in the L2A products is the Hemispherical-directional Reflectance Factor (HDRF) defined as: HDRF = π L / E where L is the directional upwelling radiance (with field of view of 5 degrees) and E is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for a given geometry (combination of viewing zenith and azimuth angle). In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, number of valid scans used, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p> <p>The WWUK site is a deciduous broadleaf forest comprised primarily of Oak, Hazel, Ash, Sycamore and Beech. It is located approximately 5 km North-West of Oxford, UK and has an extensive history of scientific research. The site follows the typical seasonal dynamics of a temperate forest with distinctive periods of leaf-off, green up and senescence across the growing season. The HYPERNETS site itself (51.777206 degrees N, 1.338494 W), is located at a height of 28 m upon a flux tower in the centre of the forest. The HYPSTAR®-XR sensor was installed in October 2021. Data are collected e very 30 minutes between 9am and 6pm local time between viewing zenith angles of 0 and 30 degrees.</p> <p>The HYPSTAR®-XR (eXtended Range) instruments deployed at each land HYPERNETS site consist of a VNIR and a SWIR sensor and autonomously collect data between 380-1700 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm and the SWIR sensor has 220 channels between 1000 and 1700 nm with a FWHM of 10 nm. The hypernets_processor (Goyens et al. 2021; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All of the products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information) which were propagated using the CoMet toolkit (www.comet-toolkit.org). </p> <p>To obtain this dataset, we start from the full WWUK data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, two additional screening procedures are developed to remove outliers and only supply the best quality data suitable for satellite validation. For Wytham wood, sequences are only supplied that match a typical vegetation spectrum. As such, data is only provided between April and October during the leaf-on period. Reflectances are then tested against three parameters to check that they are vegetation spectrum. Firstly, that there is a peak in the green portion of the visible wavebands (560 nm). Secondly, that a red edge is detected. Finally, the Normalized Difference Vegetation Index (NDVI) is calculated. Spectra with an NDVI of less than 0.42 are removed from the final data set.</p> <p>After the vegetation quality flag are applied, a sigma-clipping method is used to remove outliers. First reflectances are extracted in separate 2 hour windows throughout the day (to account for BRDF differences due to different solar position) for 4 different wavelengths (500, 900, 1100 and 1600 nm). Outliers in these reflectances are then identified by iteratively calculating the mean reflectance trend with time (by binning the data per maximum 30 data points), calculating the standard deviation from this trend, and masking any data that is more than 3 standard deviations away from the trend. This process is repeated on the unmasked data until the standard deviation does not vary by more than 5% between two iterations. The masks for the 4 different wavelengths are then combined (keeping only measurements for which none of the 4 wavelengths is an outlier). The reflectances and associated uncertainties for any masked series (i.e. a geometry that is masked either by the sigma-clipping procedure or from the masks of the hypernets_processor) are replaced by NaNs. Any sequence that has more than half of its series masked is removed entirely. </p>
Vegetation and invertebrate communities in 500 plots in the Duplin and Dean Creek watersheds: ground truth data for matching hyperspectral imagery
We measured characteristics of vegetation (Aster tenuifolius, Batis maritima, Borrichia frutescens, Distichlis spicata, Iva frutescens, Juncus roemerianus, Limonium carolinianum, Salicornia biglovii, Salicornia virginica, Spartina alterniflora, Spartina patens, Sporobolus virginicus), soil (salinity, proportion organic and proportion water) and densities of common gastropods and bivalves in 500 plots in the Duplin and Dean Creek watersheds on Sapelo Island on June 20-26, 2006. Plot locations were determined using a high precision hand-held GPS. These data were used to help ground-truth hyperspectral aerial images collected at the same time by Dr. John Schalles.
2006 AISA hyperspectral imagery of the GCE domain for water
Airborne Imaging Spectrometer for Applications (AISA) Eagle hyperspectral imagery were acquired on June 20-21, 2006, by the Center for Advanced Land Management Information Technologies (CALMIT). This included six flight lines flown for the examination of water spectral properties for the Satilla River, Altamaha River, and Sapelo Sound. Imagery was acquired for 97 bands from 435-950 nm at a 1 m spatial resolution. The bandwidths were preselected by investigators with CALMIT to to capture the photoplankton red reflectance feature and carotenoid and chlorophyll driven absorption behaviors. These data were acquired for the following purposes: 1) calculate suites of remote sensing phytoplankton indices, (2) produce algorithms for predicting plant and phytoplankton chlorophyll and accessory pigments and productivity, 3) assess water quality, and (4) perform atmospheric corrections.
2006 AISA hyperspectral imagery of the GCE domain for vegetation
Airborne Imaging Spectrometer for Applications (AISA) Eagle hyperspectral imagery were acquired on June 20-21, 2006, by the Center for Advanced Land Management Information Technologies (CALMIT). This included ten flight lines flown for the examination of salt marsh and upland vegetation spectral properties near Blackbeard Creek, the Duplin River, Dean Creek, and the Altamaha River. Imagery was acquired for 63 bands from 400-980 nm at a 1 m spatial resolution. The bandwidths were preselected by investigators with CALMIT to to capture the vegetative red reflectance feature, leaf water content related NIR reflectance, and carotenoid and chlorophyll driven absorption behaviors. These data were acquired for the following purposes: 1) calculate suites of remote sensing vegetation indices, (2) produce algorithms for predicting plant and phytoplankton chlorophyll and accessory pigments, vegetation biomass, 3) assess vegetative health, and (4) perform atmospheric corrections.
Maximum likelihood classification of 2006 AISA hyperspectral imagery of the GCE domain for vegetation
Airborne Imaging Spectrometer for Applications (AISA) Eagle hyperspectral imagery were acquired on June 20-21, 2006, by the Center for Advanced Land Management Information Technologies (CALMIT). This included four flight lines flown for the examination of vegetation for the Duplin River salt marshes. Imagery was acquired for 63 bands from 400-980 nm at a 1 m spatial resolution. Imagery were classified using the maximum likelihood classifier (MLC) and a post-classification decision tree to achieve an overall classification accuracy of 90%. Classification training and validation data were obtained from the 2006 Hyperspectral ground survey. See Hladik (2012) and Hladik, Alber, and Schalles (2013) and Schalles, et. al. (2013) for additional details.
NDVI images derived from the 2006 AISA hyperspectral imagery of the GCE domain for vegetation
Airborne Imaging Spectrometer for Applications (AISA) Eagle hyperspectral imagery were acquired on June 20-21, 2006, by the Center for Advanced Land Management Information Technologies (CALMIT). This included ten flight lines flown for the examination of salt marsh and upland vegetation and water for spectral properties at 1 m spatial resolution. For all vegetation images, the Normalized difference vegetation index (NDVI) was calculated. NDVI uses the ratio of reflectance in the red and NIR wavelengths (NDVI = (NIR799 - RED675)/ (NIR799 + RED675)) to derive an index of plant vigor (Rouse et al., 1974). The subscript values are the wavelength band centers used to calculate NDVI. Values indicate the amount of green vegetation present in the pixel—higher NDVI values indicate more green vegetation. Vallid results fall between -1 and +1.
Hyperspectral X-ray CT data set of mineralised ore sample with Au and Pb deposits
<p><strong>General data description:</strong></p> <p>This is a hyperspectral (energy-resolved) X-ray CT projection data set of a mineralised ore sample with small gold and galena deposits. It was acquired in a laboratory micro-CT scanner with an energy-sensitive HEXITEC detector in the Henry Moseley X-ray Imaging Facility at The University of Manchester.</p> <p>The data included contains all the relevant files required for reconstruction, following a hyperspectral scan of a mineralised ore sample. The sample contains a number of mineral phases, of varying concentration, distributed throughout. Some phases (including gold, and lead-based Galena) produce unique absorption edges, which act as spectral identifiers that can be measured by an energy-sensitive detector.</p> <p><strong>File descriptions:</strong></p> <p>The data set consists of one .txt file and three .mat (MATLAB) data files.</p> <p>Au_rock_scan_geometry.txt gives a breakdown of the full sample and detector geometry used when acquiring the raw projections. The number of horizontal detector pixels accounts for the fact that a set of 5 tiled scans of the sample were collected and later stitched together.</p> <p>Au_rock_sinogram_full.mat contains the full 4D sinogram constructed following flat-field normalisation of the raw projection data. The data matrix contains the total number of energy channels acquired during scanning, as well as the conventional elements of vertical/horizontal detector pixel number and total projection angles.</p> <p>commonX.mat provides a direct conversion between the energy channels, and the energies (in keV) that they correspond to, following a calibration procedure prior to scanning.</p> <p>FF.mat contains the 4D flatfield data acquired when no sample was present. This data was used to normalise the projection datasets, as the sinogram was constructed.</p>
Ground truth and raw hyperspectral files of olive trees for plant stress detection
<p>This dataset contains raw hyperspectral images from Cubert S-185 collected on 13 May 2021 from an olive field in Halkidiki, Northern Greece. Included is also a matrix containing the id of each recorded olive tree (the samples) that also appears in the hyperspectral images. QGIS (ver.3.28.0) software plugin 'zonal statistics multiband' was used to compute zonal statistics for each of the 138 spectral bands available for each sample. Accompanying each sample is also the ground truthing data recorded, which addresses the present stress of 3 stressors (<i>Verticillium dahliae, Pleospora herbarum </i>and 'other stressors').</p>
Band Ratio Mosaics from Airborne Hyperspectral Data at Aramo, Spain
<p> </p> <table> <tbody> <tr> <td> <h2>Metadata information</h2> </td> <td> </td> </tr> <tr> <td><strong>Full Title</strong></td> <td>Band Ratio Mosaics from Airborne Hyperspectral Data at Aramo, Spain</td> </tr> <tr> <td><strong>Abstract</strong></td> <td> <p>This dataset comprises results from the S34I Project, derived from processing airborne hyperspectral data acquired at the Aramo pilot site in Spain. Spectral Mapping Services (SMAPS Oy) conducted the airborne data acquisition in May 2024 using the Specim AisaFENIX sensor (covering VNIR-SWIR spectral ranges) over 17 flight lines. SMAPS performed geometric correction, radiometric calibration to reflectance, and atmospheric correction of the data. Subsequent processing steps included spectral smoothing with a Savitzky-Golay filter, cloud masking, bad pixel corrections, and hull correction (continuum removal).</p> <p>Manual processing and interpretation of hyperspectral data is a challenging, time-consuming, and subjective task, necessitating automated or semi-automated approaches. Therefore, we present a semi-automated workflow for large-scale interpretation of hyperspectral data, based on a combination of state-of-the-art methodologies. This dataset results from the calculation of a series of band ratios applied to the images and their subsequent mosaicking into a TIFF file. The mosaics are delivered as georeferenced TIFF files that cover approximately 97 km² with a spatial resolution of 1.2 m per pixel. The NoData value is set to -9999, representing areas of cloud removal or missing flight lines. The projected coordinate system is UTM Zone 30 Northern Hemisphere WGS 1984, EPSG 4326.</p> <p>Hyperspectral band ratios involve applying mathematical operations (such as division, subtraction, addition, or multiplication) among the reflectance values of different spectral bands. This technique enhances subtle variations in how materials absorb and reflect light across the electromagnetic spectrum. These variations are caused by electronic transitions, vibrations of chemical bonds (including -OH, Si-O, Al-O, and others), and lattice vibrations within the material's crystal structure.</p> <p>By creating these mathematical combinations, specific absorption features are emphasized, generating unique spectral fingerprints for different materials. However, these fingerprints alone cannot definitively identify a mineral, as different minerals may share similar absorption features due to common chemical bonds or crystal structures. Spectral geologists use band ratios as a tool to highlight potential areas of interest, but they must integrate this information with other geological knowledge and analyses to accurately interpret the mineralogy of an area. </p> <p>This dataset includes nine spectral band ratios. The mathematical formulas used to calculate each ratio are provided below:</p> <p> </p> <p>BR1 target Carbonate / Chlorite / Epidote</p> <p>BR1 = ((C7 + C9) / (C8))</p> <p>C7= Mean of bands between 2246.6 and 2257.55 nm</p> <p>C8= Mean of bands between 2339 and 2345 nm</p> <p>C9= Mean of bands between 2400 and 2410 nm</p> <p> </p> <p>BR2 target Chlorite</p> <p>BR2 = ((Cl1 + Cl2) / (Cl2))</p> <p>Cl1 = Mean of bands between 2191.93 and 2197.4 nm</p> <p>Cl2 = Mean of bands between 2246.63 and 2257.55 nm</p> <p> </p> <p>BR3 target Clay</p> <p>BR3 = ((C1 + C2) / (C2))</p> <p>C1 = Mean of bands between 1590.32 and 1612.56 nm</p> <p>C2 = Mean of bands between 2191.93 and 2208.35 nm</p> <p> </p> <p>BR4 target Dolomite</p> <p>BR4 = ((C6 + C8) / (C7))</p> <p>C6= Mean of bands between 2186 and 2191 nm</p> <p>C7= Mean of bands between 2246.6 and 2257.55 nm</p> <p>C8= Mean of bands between 2339 and 2345 nm</p> <p> </p> <p>BR5 target Fe2</p> <p>BR5 = ((Fe2n + Fe2d) / (Fe2d))</p> <p>Fe2n = Mean of bands between 721.85 and 742.48 nm</p> <p> </p> <p>BR6 target Fe3</p> <p>BR6 = ((Fe3n - Fe3d) / (Fe3n + Fe3d))</p> <p>Fe3n = Mean of bands between 776.87 to 811.26 nm</p> <p>Fe3d = = Mean of 3 bands around 610 nm</p> <p> </p> <p>BR7 target = Kaolinite / clays</p> <p>BR7 = ((K1 + K2) / (K3 + K4))</p> <p>K1 = Mean of bands between 2082.27 and 2104.23 nm</p> <p>K2 = Mean of bands between 2104.23 and 2115.2 nm</p> <p>K3 = Mean of bands between 2159.07 and 2164.55 nm</p> <p>K4 = Mean of bands between 2202.88 and 2208.35 nm</p> <p> </p> <p>BR8 target Kaolinite2 / clays</p> <p>BR8 = ((K1_2 + K2_2) / (K2_2))</p> <p>K1_2 = Mean of bands between 2197.4 and 2219.29 nm</p> <p>K2_2 = Mean of bands between 2159.07 and 2170.03 nm</p> <p> </p> <p>BR9 target NDVI (Normalized Difference Vegetation Index)</p> <p>BR9 = ((NIR - Red) / (NIR + Red))</p> <p>NIR= Mean of bands between 776.87 and 811.26 nm</p> <p>Red = Mean of bands between 666.87 to 680.6 nm</p> </td> </tr> <tr> <td>Keywords</td> <td>Earth Observation, Remote Sensing, Hyperspestral Imaging, Automated Processing, Hyperspectral Data Processing, Mineral Exploration, Critical Raw Materials</td> </tr> <tr> <td>Pilot area</td> <td>Aramo</td> </tr> <tr> <td>Language</td> <td> <p>English</p> </td> </tr> <tr> <td>URL Zenodo</td> <td>https://zenodo.org/uploads/14193286</td> </tr> <tr> <td><strong>Temporal reference</strong></td> <td> </td> </tr> <tr> <td>Acquisition date (dd.mm.yyyy)</td> <td>01.05.2024</td> </tr> <tr> <td>Upload date (dd.mm.yyyy)</td> <td>20.11.2024</td> </tr> <tr> <td><strong>Quality and validity</strong></td> <td> </td> </tr> <tr> <td>Fromat</td> <td>GeoTiff</td> </tr> <tr> <td>Spatial resolution</td> <td>1.2m </td> </tr> <tr> <td>Positional accuracy</td> <td>0.5m </td> </tr> <tr> <td>Coordinate system</td> <td>EPGS 4326</td> </tr> <tr> <td><strong>Access and use constrains</strong></td> <td> </td> </tr> <tr> <td>Use limitation</td> <td>None</td> </tr> <tr> <td>Access constraint</td> <td>None</td> </tr> <tr> <td>Public/Private</td> <td>Public</td> </tr> <tr> <td><strong>Responsible organisation</strong></td> <td> </td> </tr> <tr> <td>Responsible Party</td> <td>Beak Consultants GmbH</td> </tr> <tr> <td>Responsible Contact</td> <td>Roberto De La Rosa</td> </tr> <tr> <td><strong>Metadata on metadata</strong></td> <td> </td> </tr> <tr> <td>Contact</td> <td>Roberto.delarosa@beak.de</td> </tr> <tr> <td>Metadata language</td> <td>English</td> </tr> </tbody> </table>
Hyperspectral imagery Research Products - Toulouse urban area 2015 (French ANR HYEP project)
<p>The HYEP project (ANR 14-CE22-0016-01) main goal was to propose a panel of methods and processes designed for hyperspectral imaging, which specificity makes a weighty auxiliary for the monitoring of the elements of the urban area. The main results of the project can be found at</p> <ul> <li><a href="http://doi.org/10.1080/01431161.2017.1410247">G. Roussel, C. Weber, X. Briottet and X. Ceamanos, "Comparison of two atmospheric correction methods for the classification of spaceborne urban hyperspectral data depending on the spatial resolution", International Journal of Remote Sensing, vol. 39(5), pp. 1593-1614, 2018.</a></li> <li><a href="http://doi.org/10.1109/ECMSM.2017.7945884">F. Z. Benhalouche, M. S. Karoui, Y. Deville, I. Boukerch, A. Ouamri, ``Multi-sharpening hyperspectral remote sensing data by multiplicative joint-criterion linear-quadratic nonnegative matrix factorization'', Proceedings of the 2017 IEEE International Workshop on Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM 2017), May 24-26, 2017, Donostia - San Sebastian</a></li> <li><a href="https://hal-amu.archives-ouvertes.fr/hal-01903469">Gintautas Mozgeris, Vytaut ̇e Juodkien ̇e, Donatas Jonikaviˇcius, Lina Straigyt ̇e, S ́ebastien Gadal, and Walid Ouerghemmi. Ultra-Light Aircraft-Based Hyperspectral and Colour-Infrared Imaging to Identify Deciduous Tree Species in an Urban Environment. Remote Sensing, 10(10), October 2018.</a></li> <li><a href="https://hal.archives-ouvertes.fr/hal-02281003">Christiane Weber, Thomas Houet, S ́ebastien Gadal, Rahim Aguejdad, Grzegorz Skupinski, Yannick Deville, Jocelyn Chanussot, Mauro Dalla Mura, Xavier Briottet, Cl ́ement Mallet, and Arnaud Le Bris. HYEP HYperspectral imagery for Environmental urban Planning : principaux résultats. In 7ème colloque scientifique du groupe SFPT-GH, Toulouse, France, July 2019. ONERA - SFTP.</a></li> <li><a href="https://hal-amu.archives-ouvertes.fr/hal-01852844">Christiane Weber, Rahim Aguejdad, Xavier Briottet, Josselin Aval, Sophie Fabre, Jean Demuynck, Emmanuel Zenou, Yannick Deville, Moussa Sofiane Karoui, Fatima Zohra, Sébastien Gadal, Walid Ouerghemmi, Clément Mallet, Arnaud Le Bris, and Nesrine CHEHATA. Hyperspectral Imagery for Environmental Urban Planning. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2018, pages 1628–1631, Valencia, Spain, July 2018a. IEEE.</a></li> <li><a href="https://hal.archives-ouvertes.fr/hal-01854904">Christiane Weber, Rahim Aguejdad, X Briottet, J Avala, S. Fabre, J Demuynck, E Zenou, Y. Deville, M. Karoui, F Z Benhalouche, S Gadal, W Ourghemmi, C. Mallet, A. Le Bris, and N. Chehata. HYPERSPECTRAL IMAGERY FOR ENVIRONMENTAL URBAN PLANNING. In IGARSS 2018, Valencia, Spain, 2018b. </a></li> <li><a href="http://doi.org/10.5194/isprs-archives-XLII-1-W1-167-201">W. Ouerghemmi, A. Le Bris, Nesrine CHEHATA, and Clément Mallet. A two-step decision fusion strategy: application to hyperspectral and multispectral images for urban classification. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, volume XLII-1/W1, pages 167–174, Hanover, Germany, May 2017. Copernicus GmbH (Copernicus Publications).</a></li> <li><a href="https://hal.inria.fr/hal-02384455">Christiane Weber, Sébastien GADAL, Xavier Briottet, and Clément Mallet. Apport de l’imagerie hyperspectrale pour la planification urbaine. In Karine Emsellem, Diego Moreno, Christine Voiron-Canicio, and Didier Josselin, editors, SAGEO 2016 - Spatial Analysis and Geomatics, Actes de la conférence SAGEO’2016 - Spatial Analysis and GEOmatics, pages 454–462, Nice, France, December 2016. </a></li> <li><a href="https://hal-amu.archives-ouvertes.fr/hal-01359643">Gintautas Mozgeris, S ́ebastien Gadal, Donatas Jonikaviˇcius, Lina Straigyte, Walid Ouerghemmi, and Vytaut ̇e Juodkiene. Hyperspectral and color-infrared imaging from ultra-light aircraft: Potential to recognize tree species in urban environments. In University of California Los Angeles, editor, 8th Workshop in Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, pages 542–546, Los Angeles, United States, August 2016.</a></li> <li><a href="https://hal.inria.fr/hal-02384458">Alexandre Hervieu, Arnaud Le Bris, and Cl ́ement Mallet. Fusion of hyperspectral and VHR multispectral image classifications in urban α–areas. In ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, volume III-3, pages 457–464, Prague, Czech Republic, July 2016.</a></li> <li><a href="https://hal.archives-ouvertes.fr/hal-01888126">Christiane Weber, Thomas Houet, Sebastien GADAL, Rahim Aguejdad, Grzegorz Skupinski, Aziz Serradj, Yannick Deville, Jocelyn Chanussot, Mauro Dalla Mura, Xavier Briottet, Clément Mallet, and Arnaud Le Bris. ANR HYEP ANR 14-CE22-0016-01Hyperspectral imagery for Environmental urban Planning HyepProgramme Mobilité et systèmes urbains 2014. Research report, CNRS UMR TETIS, ESPACE, LETG ; ONERA ; GIPSA-lab ; IRAP ; IGN, October 2018c. </a></li> <li><a href="https://doi.org/10.1080/01431161.2019.1579937">Josselin Aval, Sophie Fabre, Emmanuel Zenou, David Sheeren, Mathieu Fauvel & Xavier Briottet (2019) Object-based fusion for urban tree species classification from hyperspectral, panchromatic and nDSM data, International Journal of Remote Sensing, 40:14, 5339-5365, DOI: 10.1080/01431161.2019.1579937 </a></li> <li><a href="https://doi.org/10.3390/rs11111269">Charlotte Brabant, Emilien Alvarez-Vanhard, Achour Laribi, Gwenaël Morin, Kim Thanh Nguyen et al. Comparison of Hyperspectral Techniques for Urban Tree Diversity Classification Remote Sensing, MDPI, 2019, 11 (11), pp.1269. ⟨10.3390/rs11111269⟩ hal-02191084v1 </a></li> <li><a href="https://hal.archives-ouvertes.fr/halshs-02191363v1">C. Brabant, Emilien Alvarez-Vanhard, Gwenaël Morin, Thanh Ngoc Nguyen, Achour Laribi et al. Evaluation of dimensional reduction methods on urban vegetation classification performance using hyperspectral data IGARSS 2018, Jul 2018, Valencia, Spain halshs-02191363v1</a></li> <li><a href="https://hal.archives-ouvertes.fr/halshs-02191097v1">Charlotte Brabant, Emilien Alvarez-Vanhard, Thomas Houet. Improving the classification of urban tree diversity from Very High Spatial Resolution hyperspectral images: comparison of multiples techniques Joint Urban Remote Sensing Event (JURSE 2019), May 2019, Vannes, France halshs-02191097v1</a></li> </ul> <p>This Dataset contains five research outputs of this project that were produced on the basis of Hyperspectral data obtained during an acquisition campaign led on Toulouse (France) urban area on July 2015 using Hyspex instrument which provides 408 spectral bands spread over 0.4 – 2.5 μ. Flight altitude lead to 2 m spatial resolution images.</p> <ul> <li><strong>Fields_samples.7z: </strong> ESRI Shape Format. Supervised SVN classification results for 600 urban trees according to a 3 level nomenclature: leaf type (5 classes), family (12 & 19 classes) and species (14 & 27 classes). The number of classes differ for the two latter as they depend on the minimum number of individuals considered (4 and 10 individuals per class respectively). Trees positions have been acquired using differential GPS and are given with centimetric to decimetric precision. A randomly selected subset of these trees has been used to train machine SVM and Random Forest classification algorithms. Those algorithms were applied to hyperspectral images using a number of classes for family (12 & 19 classes) and species (14 & 27 classes) levels defined according to the minimum number of individuals considered during training/validation process (4 and 10 individuals per class, respectively). Global classification precision for several training subsets is given by Brabant et al, 2019 (<a href="https://www.mdpi.com/470202">https://www.mdpi.com/470202</a>) in terms of averaged overall accuracy (AOA) and averaged kappa index of agreement (AKIA).</li> <li><strong>HySPex-2m.7z: </strong>full hyperspectral VNIR-SWIR ENVI standard image obtained from the coregistration of both VNIR and SWIR ones through a signal aggregation process that allowed to obtain a synthetic VNIR 1.6 m spatial resolution image, with pixels exactly corresponding to natif SWIR image ones. First, a spatially resampled 1.6 m VNIR image was built, where output pixel values were calculated as the average of the VNIR 0.8 m pixel values that spatially contribute to it. Then, ground control points (GCP) were selected over both images and SWIR one was tied to the VNIR 1.6 m image using a bilinear resampling method using ENVI tool. This lead to a 1.6 m spatial resolution full VNIR-SWIR image.</li> <li><strong>HYPXIM-4m.7z, HYPXIM-8m.7z, Sentinel2-10m.7z</strong>: hyperspectral ENVI standard simulated images. Spatial and spectral configurations generated correspond to ESA SENTINEL-2 instrument that was lunched on 2015, and HYPXIM sensor which was under study at that time. </li> </ul>
FlexiGroBots - Blueberry UAV Hyperspectral Dataset
<p>Acquisition dates: 07.07.2021; 16.07.2021; 12.09.2021<br> Location: Maišiagala, Vilnius District Municipality, Lithuania<br> Spatial resolution: 0.023 m/pixel<br> Number of spectral bands: 204<br> Spectral range: 426-958 nm (visible-near infrared spectrum)<br> Spectral resolution: 2.8 nm<br> Flight altitude: 70 m<br> <br> The dataset consists of blueberry hyperspectral imaging data acquired with a UAV and a BaySpec OCI-F Hyperspectral Imager on several dates. In total, six flights on three different dates were performed. The data from each UAV flight are given as a separate dataset. Each dataset consists of raw and processed hyperspectral imaging data. The raw data include calibration images of white reference and dark background, raw hyperspectral images, and information on the UAV flight path. Calibration data are stored in the folders "...-White", "...-White_FS2", "...-Dark", and "...-Dark_FS2". Raw images are located in subfolders RawImages and RawImages_FS2 of the main data folder, which ends with "..._BI08". The BaySpec Cube Creator 2100 software was used to process raw images into hyperspectral data cubes, which are provided in the format of band sequential image files (BSQ). BSQ files are located in the Cube folders of each dataset together with HDR files containing metadata for each cube. The values of hyperspectral data cubes are in digital numbers, which can be recalculated to reflectance using the reflectance scaling factor. It is specified in the HDR files for each cube individually.</p> <p>Datasheet of the dataset: <a href="https://drive.google.com/file/d/1QV5he5bGazAlN8A5Mpyl1xMc7lQcvy9W">https://drive.google.com/file/d/1QV5he5bGazAlN8A5Mpyl1xMc7lQcvy9W</a><br> <br> Download links:<br> <a href="https://blueberry-dataset.s3.eu-west-1.amazonaws.com/2021-07-07.zip">https://blueberry-dataset.s3.eu-west-1.amazonaws.com/2021-07-07.zip (60.48 GB)</a><br> <a href="https://blueberry-dataset.s3.eu-west-1.amazonaws.com/2021-07-16.zip">https://blueberry-dataset.s3.eu-west-1.amazonaws.com/2021-07-16.zip (177.22 GB)</a><br> <a href="https://blueberry-dataset.s3.eu-west-1.amazonaws.com/2021-09-15.zip">https://blueberry-dataset.s3.eu-west-1.amazonaws.com/2021-09-15.zip (81.12 GB)</a></p>
Applying Sensor Fusion to Augment Hyperspectral Data with Depth Information
<p>The research data for the paper "Applying Sensor Fusion to Augment Hyperspectral Data with Depth Information"<br> <br> Data in the archive "hyperdepth.tar.gz" includes:</p> <p><br> <strong>calibration_images/</strong><br> includes preprocessed images for calibrating both cameras</p> <p><strong>pointclouds/</strong><br> Includes individual hyperspectral point clouds for each view (front, rightmost, right, leftmost, left with postfixes correspondingly: edesta, oikea, oikea2, vasen, vasen2)<br> <br> <strong>raw_images/</strong><br> Two directories "day5" and "day6" which include the raw hyperspectral images and kinect images<br> <br> Some extra images are included which were not used in the research paper.</p> <p> </p> <p><strong>2022-03-11_112336_stereocalibration.json</strong> includes calibration results (mainly the intrinsic camera matrix and extrinsic parameters) for the setup.</p>
Hyperspectral data of Vigo sediment samples
<h2>Abstract</h2> <p>Reflectance and Radiance converted hyperspectral data of 9 sediment samples. The samples were collected by UPORTO and IGME from Vigo Campaign fieldwork in Sept 2023 but they were scanned at Ecotone lab in Trondheim by UHI in February 2024. The data was scanned for both dry and wet sediments.</p> <p>This depository 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>Hyperspectral data of Vigo sediment samples</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Reflectance and Radiance converted hyperspectral data of 9 sediment samples. The samples were collected by UPORTO and IGME from Vigo Campaign fieldwork in Sept 2023 but they were scanned at Ecotone lab in Trondheim by UHI in February 2024. The data was scanned for both dry and wet sediments.</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>Reflectance estimated hyperspectral data, Radiance converted hyperspectral data, sediments, sand, mineral resource</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Ria de Vigo</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>Report, photo</p> <p>Raw data: <a href="https://doi.org/10.5281/zenodo.13462199">https://doi.org/10.5281/zenodo.13462199</a></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>Mineral resources</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>3.04.2024</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>3.04.2024</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>Format</p> </td> <td> <p>HDF5</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>1.5m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>/</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 3035</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>Ecotone AS (info@ecotone.com)</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>Ecotone AS</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Ecotone AS (info@ecotone.com)</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
Hyperspectral environmental illumination maps for outdoor and indoor scenes
<p>This repository contains a dataset of hyperspectral illumination maps collected from 6 outdoor and 4 indoor scenes.</p> <p> </p> <p>If you use this dataset in your research, please cite:</p> <p> </p> <p>Takuma Morimoto, João M. M. Linhares, Sérgio M. C. Nascimento, and Hannah E. Smithson, “How many surfaces can you distinguish by color? Real environmental lighting increases discriminability of surface colors,” Optics Express (in press).</p> <p> </p> <p>Technical details about data acquisition are described in:</p> <p> </p> <p>Takuma Morimoto, Sho Kishigami, João M.M. Linhares, Sérgio M.C. Nascimento, and Hannah E. Smithson, “Hyperspectral environmental illumination maps: characterizing directional spectral variation in natural environments,” Optics Express, 27, 22, 32277 - 32293. (2019). <a href="https://doi.org/10.1364/OE.27.032277">https://doi.org/10.1364/OE.27.032277</a></p> <p> </p> <p>Each file includes the following formats:</p> <p> </p> <ol> <li><strong>png</strong>: RGB image for visualization.<br><br></li> <li><strong>mat</strong>: Hyperspectral image with wavelengths from 400 nm to 700 nm in 10 nm steps. Each pixel value represents spectral radiance in W m−2 sr−1 nm−1. The image and wavelength range are stored in the variables ‘radiance’ and ‘wls’, respectively.</li> </ol> <p>The images have an average spatial resolution of 1019 (height) × 2035 (width) across 10 scenes.</p> <p>File names follow the format X_sceneY, where X is the scene type (outdoor or indoor) and Y is the scene number.</p> <p> </p> <p>For Python users, the mat file can be loaded using e.g. scipy.io.loadmat. More information: <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html">https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html</a></p> <p> </p> <p>Note: To use for hyperspectral renderings (e.g., Mitsuba), convert the hyperspectral image to the OpenEXR format.</p>
Underwater hyperspectral data of shallow water seafloor at Vigo-Rias Biaxas sea zones
<h2>Abstract</h2> <p>Raw hyperspectral data of shallow water seafloor at three different Vigo-Rias Biaxas sea zones from Vigo fieldwork Sept. 2023. The hyperspectral data was recorded by Ecotone UHI (underwater hyperspectral imaging) installed on customized BlueROV 2. The UHI was run about 0.3-2m above the seafloor.</p> <p>This depositry contains data generated within the European S34 project. The data are in raw form and have not been further processed. Processed data are published in other depositories.</p> <h2>Metadata information</h2> <p> </p> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Underwater hyperspectral data of shallow water seafloor at Vigo-Rias Biaxas sea zones</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Raw hyperspectral data of shallow water seafloor at three different Vigo-Rias Biaxas sea zones from Vigo fieldwork Sept. 2023. The hyperspectral data was recorded by Ecotone UHI (underwater hyperspectral imaging) installed on customized BlueROV 2. The UHI was run about 0.3-2m above the seafloor.</p> <p>This depositry contains data generated within the European S34 project. The data are in raw form and have not been further processed. Processed data are published in other depositories.</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>Raw hyperspectral data, underwater hyperspectral data, UHI, seafloor, seabed, mineral, sea region, Vigo, Vigo-Rias Biaxas</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Ria de Vigo</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>Bio-geographical regions</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>27.09.2023</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Format</p> </td> <td> <p>HDF5</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>2mm (with UHI about 2m away)</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>3035</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>no</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>no</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>Ecotone As</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>Ecotone As</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Ecotone As, info@ecotone.com</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table> <p> </p>
Multiple DJI drone flights with hyperspectral camera over forests in Lithuania #3
<p>Hyperspectral data in all of the datasets consist of processed hyperspectral data cubes to radiance values.<br>all data cubes with _radiance.dat and ._radiance.hdr files are for ENVI format data file. While _rect.dat and _rect.hdr files are ENVI files with georectification applied.</p> <p>Hyperspectral data also contain calculated RGB and NDVI png and tiff images. Png images are generated from _radiance.dat files and tiff images are generated from _rect.dat files that have georectification.</p> <p>All hyperspectral data was collected using the Specim hyperspectral pushbroom camera.</p> <p> </p> <p>### Hyper 3</p> <p>https://art21-icaerus.s3.eu-central-1.amazonaws.com/Hyper_3.zip</p> <p>Dataset consists of 7 flight lines filmed over an infected forest.</p> <p>Each flight folder denoted by fl# contains processed hyperspectral data cubes and "processed" folder with RGB and NDVI images. </p> <p>Dataset zip size: 33.6 GB</p>
Multiple DJI drone flights with hyperspectral camera over forests in Lithuania #2
<p>Hyperspectral data in all of the datasets consist of processed hyperspectral data cubes to radiance values.<br>all data cubes with _radiance.dat and ._radiance.hdr files are for ENVI format data file. While _rect.dat and _rect.hdr files are ENVI files with georectification applied.</p> <p>Hyperspectral data also contain calculated RGB and NDVI png and tiff images. Png images are generated from _radiance.dat files and tiff images are generated from _rect.dat files that have georectification.</p> <p>All hyperspectral data was collected using the Specim hyperspectral pushbroom camera.</p> <p> </p> <p>### Hyper 2</p> <p><a href="https://art21-icaerus.s3.eu-central-1.amazonaws.com/Hyper_1.zip/Hyper_2.zip">https://art21-icaerus.s3.eu-central-1.amazonaws.com/Hyper_2.zip</a></p> <p>Dataset consists of 7 flight lines filmed over an infected forest.</p> <p>Each flight folder denoted by fl# contains processed hyperspectral data cubes and "processed" folder with RGB and NDVI images. </p> <p>Dataset zip size: 23.4 GB</p>
Multiple DJI drone flights with hyperspectral camera over forests in Lithuania #1
<p>Hyperspectral data in all of the datasets consist of processed hyperspectral data cubes to radiance values.<br>all data cubes with _radiance.dat and ._radiance.hdr files are for ENVI format data file. While _rect.dat and _rect.hdr files are ENVI files with georectification applied.</p> <p>Hyperspectral data also contain calculated RGB and NDVI png and tiff images. Png images are generated from _radiance.dat files and tiff images are generated from _rect.dat files that have georectification.</p> <p>All hyperspectral data was collected using the Specim hyperspectral pushbroom camera.</p> <p> </p> <p><a title="Hyperspectral imaging dataset over Lithuanian forests " href="https://art21-icaerus.s3.eu-central-1.amazonaws.com/Hyper_1.zip">https://art21-icaerus.s3.eu-central-1.amazonaws.com/Hyper_1.zip</a></p> <p>Dataset consists of 6 flight lines filmed over a stated healthy forest.</p> <p>Each flight folder denoted by fl# contains processed hyperspectral data cubes and "processed" folder with RGB and NDVI images. </p> <p>Dataset zip size: 25.9 GB</p>
A Synthetic Hyperspectral Dataset for Development and Validation of Phytoplankton Size Class Retrieval Models
<p><strong>A Synthetic Hyperspectral Dataset for Development and Validation of Phytoplankton Size Class Retrieval Models.</strong></p> <p>Please refer to the following scientific paper for a description of the dataset.</p> <blockquote> <p>Holtrop, T.; Van Der Woerd, H.J. (accepted) HYDROPT: An Open-Source Framework for Fast Inverse Modelling of Multi- and Hyperspectral Observations from Oceans, Coastal and Inland Waters. <em>Remote Sens. </em><strong>2021</strong>, 13, 0.</p> </blockquote>
Supplementary data for "Are elevated moist layers a blind spot for hyperspectral infrared sounders? - A model study"
<p>This is the base data for the retrieval of water vapor, temperature and surface temperature based on forward simulated IASI measurements in the spectral bands between 1190-1400 and 645-800 cm-1, as well as 5 channels in the atmospheric window between 901.5 and 1115.75 cm-1.</p> <p>The data includes 1599 atmospheric states over tropical ocean regions, which is a subset of the ECMWF IFS diverse profile database with focus on a broad sampling of humidity states, published by Eresmaa et al. (2014). The full dataset is also available as part of the ARTS (Atmospheric Radiative Transfer Simulator) XML database (https://radiativetransfer.org/tools/). The data also includes the forward modelled spectra in units of brightness temperatures and the associated spectral frequency grid. ARTS is used as the forward model (https://radiativetransfer.org).</p> <p>This dataset is supplementary to the article "Are elevated moist layers a blind spot for hyperspectral infrared sounders? - A model study" that has been submitted to Atmospheric Measurement Techniques (AMT).</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.