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74 results for “Hyperspectral Data”
Hyperspectral data of sediment samples collected from Vigo fieldwork Sept. 2023
<h2>Abstract</h2> <p>Raw hyperspectral data of 9 sediment samples in HDF5 file format. 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 Ecotone UHI in February 2024. The data was scanned for both dry and wet sediments.</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> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Hyperspectral data of sediment samples collected from Vigo fieldwork Sept. 2023</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Raw 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 with Ecotone UHI in February 2024. The data was scanned for both dry and wet sediments.</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, 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><a href="https://doi.org/10.5281/zenodo.13381973">https://doi.org/10.5281/zenodo.13381973</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>16.02.2024</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>Fromat</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>2 mm (with UHI about 2 m 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>
Data from: Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data
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Data from: Biomimicry of iridescent, patterned insect cuticles: comparison of biological and synthetic, cholesteric microcells using hyperspectral imaging
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Data from: Detection of tephra layers in Antarctic sediment cores with hyperspectral imaging
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Data underlying the paper titled "Enhancing hyperspectral imaging through macro and multi-modal capabilities"
<p>The data is in .mat format and contains two variables:<br>data(x, y, lambda) is the variable that contains the hyperspectral dataset, after being converted from temporal to spectral hypercube.<br>WL(lambda) is the variable that contains the wavelength calibration.</p>
Data from: Prediction of cooking time for soaked and unsoaked dry beans (Phaseolus vulgaris L.) using hyperspectral imaging technology
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Data from: The potential of hyperspectral patterns of winter wheat to detect changes in soil microbial community composition
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Raw data: A thermal infrared hyperspectral camera based on a birefringent interferometer
<p>In this repository we publish the raw data for the paper "A thermal infrared hyperspectral camera based on a birefringent interferometer"</p> <p>Data types:</p> <ul> <li> <p><strong>QCLs</strong><br><strong>t5</strong>: motor positions used to sample the interferogram of QCL at 5.263 µm [mm]<br><strong>Int5</strong>: corresponding intensity values of the interferogram of QCL at 5.263 µm as absolute value of the trace of the lock-in detector connected to the MCT [arbitrary counts]<br><strong>t7</strong>: motor positions used to sample the interferogram of QCL at 7.675 µm [mm]<br><strong>Int7</strong>: corresponding intensity values of the interferogram of QCL at 7.675 µm as absolute value of the trace of the lock-in detector connected to the MCT [arbitrary counts]<br><strong>t9</strong>: motor positions used to sample the interferogram of QCL at 9.073 µm [mm]<br><strong>Int9</strong>: corresponding intensity values of the interferogram of QCL at 9.073 µm as absolute value of the trace of the lock-in detector connected to the MCT [arbitrary counts]<br><strong>t9_short</strong>: motor positions used to sample the interferogram of QCL at 9.073 µm (shorter scan with smaller step) [mm]<br><strong>Int9_short</strong>: corresponding intensity values of the interferogram of QCL at 9.073 µm as absolute value of the trace of the lock-in detector connected to the MCT (shorter scan with smaller step) [arbitrary counts]</p> </li> <li> <p><strong>DFG_orig</strong><br><strong>tDFG</strong>: motor positions used to sample the DFG signal [mm]<br><strong>IntDFG</strong>: corresponding intensity values of the DFG signal as absolute value of the trace of the lock-in detector connected to the MCT [arbitrary counts]</p> </li> <li> <p><strong>DFG</strong><br><strong>tDFG</strong>: motor positions used to sample the DFG signal, delay corrected, cropped, and centered [mm]<br><strong>IntDFG</strong>: corresponding intensity values of the DFG signal as absolute value of the trace of the lock-in detector connected to the MCT, low-frequency corrected, inverted and normalized to the mean [arbitrary counts]</p> </li> <li> <p><strong>Kanthal_orig<br>tKanthal</strong>: motor positions used to sample the Kanthal signal [mm]<br><strong>IntKanthal</strong>: corresponding intensity values of the Kanthal signal as absolute value of the trace of the lock-in detector connected to the MCT [arbitrary counts]</p> </li> <li> <p><strong>Kanthal<br>tKanthal</strong>: motor positions used to sample the Kanthal signal [mm]<br><strong>IntKanthal</strong>: corresponding intensity values of the Kanthal signal as absolute value of the trace of the lock-in detector connected to the MCT, linearly detrended, inverted and normalized to the mean [arbitrary counts]</p> </li> <li> <p><strong>Quartz</strong><br><strong>t</strong>: motor positions used to sample the quartz emission [mm]<br><strong>HyperMatrix</strong>: temporal hypercube of the quartz emission containing one bolometer image for every motor position [arbitrary counts]</p> </li> <li> <p><strong>Filters</strong><br><strong>t_Filters</strong>: motor positions used to sample the filters transmitted light [mm]<br><strong>HyperMatrix_Filters</strong>: temporal hypercube of the filters transmitted light containing one bolometer image for every motor position [arbitrary counts]</p> </li> <li> <p><strong>Hotplate</strong><br><strong>t_Hotplate</strong>: motor positions used to sample the hotplate emission [mm]<br><strong>HyperMatrix_Hotplate</strong>: temporal hypercube of the hotplate emission containing one bolometer image for every motor position [arbitrary counts]</p> </li> </ul>
Data from: Underwater hyperspectral classification of deep sea corals exposed to 2-methylnaphthalene
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Processing and Distribution of Hyperspectral Radiometer Data from Ships of Opportunity for Monitoring Phytoplankton in the Ocean
Ocean-colour sensors mounted on satellites can view the entire ocean over a period of a few days. However, to calibrate these sensors and validate the data, satellite observations must be compared with accurate and reliable in situ measurements, collected at the ocean surface. There are many regions of the ocean where these in situ measurements are rarely collected. Ocean-colour sensors can be mounted on research vessels and ships of opportunity. In this project, an ocean-colour sensor (Seabird HyperSAS Solar Tracker) was mounted on a yacht that visits remote regions of the planet where few observations have been collected. This project aims to process this data to a level for use by the scientific community for scientific applications, such as satellite validation.
Fig. 3 in An integrated analysis of hyperspectral and morphological data of cicada ovipositors revealed unexplored links to specific oviposition hosts
Fig. 3 Morphological characters of ovipositors used for measurements. a, b First valvulae in lateral view; c base of first valvulae in dorsal view; d apex of first valvulae in dorsal view. Section A: base of ovipositor; Section B: body part of ovipositor; Section C: front claw of ovipositor
Fig. 1 in An integrated analysis of hyperspectral and morphological data of cicada ovipositors revealed unexplored links to specific oviposition hosts
Fig. 1 Oviposition of cicadas Subpsaltria yangi in live twig of Ziziphus jujuba Mill. var. spinosa (a–c) and Karenia caelatata in dead twig of Quercus aliena var. acuteserrata (d–f). a A female of S. yangi; d a female of K. caelatata; b, e egg nests (En) on twigs; c, f sets of eggs. Scale bars: c 1.0 mm; e, f 5.0 mm; a, b, d are not to scale
SPATIALLY ADAPTIVE SEMI-SUPERVISED LEARNING WITH GAUSSIAN PROCESSES FOR HYPERSPECTRAL DATA ANALYSIS
SPATIALLY ADAPTIVE SEMI-SUPERVISED LEARNING WITH GAUSSIAN PROCESSES FOR HYPERSPECTRAL DATA ANALYSIS GOO JUN * AND JOYDEEP GHOSH* Abstract. A semi-supervised learning algorithm for the classification of hyperspectral data, Gaussian process expectation maximization (GP-EM), is proposed. Model parameters for each land cover class is first estimated by a supervised algorithm using Gaussian process regressions to find spatially adaptive parameters, and the estimated parameters are then used to initialize a spatially adaptive mixture-of-Gaussians model. The mixture model is updated by expectationmaximization iterations using the unlabeled data, and the spatially adaptive parameters for unlabeled instances are obtained by Gaussian process regressions with soft assignments. Two sets of hyperspectral data taken from the Botswana area by the NASA EO-1 satellite are used for experiments. Empirical evaluations show that the proposed framework performs significantly better than baseline algorithms that do not use spatial information, and the results are also better than any previously reported results by other algorithms on the same data.
hyperspectral albedo data collected at Ny Alesund
<p>The produced dataset (in csv format) contains snow spectral albedo as measured the field activity carried out in Ny-Alesund (Svalbard Islands, Norway), where an unmanned apparatus was installed to provide continuous observations during the 2014 spring/summer period at the CNR Climate Change Tower, with the aim to deeply investigate changes in radiative characteristics during snow melting process.</p> <p>The survey covered the period from 25 May to 9 July 2014, when weather is highly variable and the Sun is constantly above the horizon. The continuous monitoring of the spectral albedo of the snowed surface was obtained coupling a tilting system with an ASD FieldSpec 3 portable spectroradiometer. The system was installed during the boreal spring nearly 8 m above the ground. The foreoptic applied to the fiber was a remote cosine receiver (RCR) with an hemispheric field of view that was fixed on the tilting device. The collected radiant energy was detected by the three spectroradiometers included in the Fieldspec with a wavelength range comprised between 350 and 2500 nm with a maximum spectral resolution of 1 nm. The spectral albedo measurements were acquired using the standard RCR mounted on the tilting system. Measurement cycles were performed <strong>every 15 min</strong>. Dark and optimization operations were executed every hours to estimate gain and spectral offset for each radiometer. Each rotation cycle was composed by a sequence of six downwelling irradiance spectra performed before and after three upwelling measurements, by completing the whole sequence in less than 3 min. All the stored spectra result from the average of ten fast acquisitions. Standard deviation associated to each triplet of spectra, provided information on sky stability, hence, indicating the possibility to perform accurate determination of the spectral albedo. For more details on experimental setup, acquisition and analysis procedures please refers to</p> <p><strong><em>Salzano R., Lanconelli C., Salvatori R., Esposito G., Vitale V. (2016), Continuous monitoring of spectral albedo of snowed surfaces in Ny-Alesund, Rend. Fis. Acc. Lincei, 27 (Suppl 1):S137–S146 doi: 10.1007/s12210-016-0513-y</em></strong></p> <p>Data set include 1728 spectra of snow albedo with a resolution of 1 nm from 350 nm to 2500 nm. They are stored in a unique file that contains columns of collected measurements in the following order:</p> <p> </p> <p><strong>row 1</strong> wavelenght channels (w_350, w_351, .........w_2500); Time</p> <p><strong>from row 2</strong></p> <p><strong>column 1</strong> Spectra number (albedo, alb, alb.1.....alb.1726)</p> <p><strong>column 2</strong> snow spectral albedo at 350 nm</p> <p><strong>......</strong></p> <p><strong>column 2501 </strong>snow spectral albedo at 2500 nm</p> <p><strong>column 2502</strong> Date time (AAAA-MM-DD HH:MM) in UTC</p> <p> </p> <p>We have holes in the data, mainly due to instrument failures.</p>
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