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100 results for “hyperspectral images”

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zenodo52/100

Urban material ground truth data for the 2007 HyMap hyperspectral image of Munich

This dataset entails a spectral library file (.sli file with matching .hdr text file) with 12028 labeled spectra derived from the 4m resolution airborne hyperspectral HyMap image of Munich (Germany) that was acquired during the summer of 2007 (June 17 and 25 2007). The labeled image spectra are retrieved from pixels of the HyMap dataset that has been processed to level 2A surface reflectance in 119 bands ranging between the wavelengths of 455 nm and 2496 nm. The preprocessing performed on this image data is explained in Heldens et al. (2008) and Heiden et al. (2012). See the "Related works" section of this data publication. The ground truth (GT) data have been used in previous research (again, see the "Related works" section) and they were likewise used for the remote sensing-based mapping experiments with a generic urban spectral library performed in the frame of the GENLIB research project. The data set contains reflectance spectra of typical urban surface materials and their spectral variations. The spectra included in this dataset were sampled from the above mentioned HyMap image by (1) using the methodology described in Jilge et al. (2017) and (2) through the delineation of manually digitized regions of interest. The image spectra are  labeled based on the method mentioned above and using ancillary reference data, already published urban spectral libraries, terrain knowledge and some field work. The header of the spectral library contains the various labels that were added to the image spectra. These labels cover: <ul> <li>EAGLE Land Cover Component (LCC) from the EAGLE matrix version 3.1. Visit the <a href="https://land.copernicus.eu/en/eagle" target="_blank" rel="noopener">website of the EAGLE framework</a> for more information.</li> <li>Material Groups (MG).</li> <li>Artificial Material Types (AMT).</li> </ul> The value domains of these spectrum attributes are described in the look-up table included as a CSV-file in this data publication. While considerable efforts have been made to safeguard the accuracy of these data, they are published as is, without any warranty or support. Use at your own discretion.

opencc-by-4.0May 2024View details →

zenodo48/100

Hyperspectral Imaging Dataset for Laser Thermal Ablation Monitoring in Vital Organs

Objectives: The objective of the research was to use hyperspectral imaging (HSI) to detect thermal damage induced in vital organs (such as the liver, pancreas, and stomach) during laser thermal therapy. The experimental study was conducted during thermal ablation procedures on live pigs. Ethical Approval: The experiments were performed at the Institute for Image Guided Surgery in Strasbourg, France. This experimental study was approved by the local Ethical Committee on Animal Experimentation (ICOMETH No. 38.2015.01.069) and by the French Ministry of Higher Education and Research (protocol №APAFiS-19543-2019030112087889, approved on March 14, 2019). All animals were treated in accordance with the ARRIVE guidelines, the French legislation on the use and care of animals, and the guidelines of the Council of the European Union (2010/63/EU). Description: During our experimental study, we used a TIVITA hyperspectral camera to acquire hypercubes of size 640x480x100 voxels, indicating 640x480 pixels for 100 bands, and regular RGB images at each acquisition step. These bands were acquired directly from the hyperspectral camera without additional pre-processing. The hypercube was acquired in approximately 6 seconds and synchronized with the absence of breathing motion using a protocol implemented for animal anesthesia. Polyurethane markers were placed around the target area to serve as references for superimposing the hyperspectral images, which were acquired using target areas selected according to the hyperspectral camera manufacturer's guidelines. As part of our investigation, we included hyperspectral cubes from 20 experiments conducted under identical conditions in our study. The hyperspectral cubes were collected in three distinct stages. In the first stage, the cubes were gathered before laparotomy at a temperature of 37°C. In the second stage, we obtained the cubes as the temperature gradually increased from 60°C to 110°C at 10°C intervals. Finally, in the last stage, the cubes were collected after turning off the laser during the post-ablation phase. Thus, we obtained a total of 233 hyperspectral cubes, each consisting of 100 wavelengths, resulting in a dataset of 23,300 two-dimensional images. The temperature changes were recorded, and the “Temperature profile during laser ablation” image illustrates the corresponding profile, highlighting the specific time intervals during which the hyperspectral camera and laser were activated and deactivated. To provide a visual representation of the collected data, we have included several examples of images captured from different organs in the “Examples of ablation areas” figure. The raw dataset, comprising 233 hyperspectral cubes of 100 wavelengths each, was transformed into 699 single-channel images using PCA and t-SNE decompositions. These images were then divided into training and test subsets and prepared in the COCO object detection format. This COCO dataset can be used for training and testing different neural networks. Access to the Study: Further information about this study, including curated source code, dataset details, and trained models, can be accessed through the following repositories: <ul> <li>Source code: <a href="https://github.com/ViacheslavDanilov/hsi_analysis" target="_blank" rel="noopener">https://github.com/ViacheslavDanilov/hsi_analysis</a></li> <li>Dataset: <a href="https://doi.org/10.5281/zenodo.10444212" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10444212</a></li> <li>Models: <a href="https://doi.org/10.5281/zenodo.10444269" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10444269</a></li> </ul>

opencc-by-4.0Dec 2023View details →

zenodo48/100

Thermal Bridges on Building Rooftops - Hyperspectral (RGB + Thermal + Height) drone images of Karlsruhe, Germany, with thermal bridge annotations

Overview: The dataset of Thermal Bridges on Building Rooftops (TBBR dataset) consists of annotated combined RGB and thermal drone images with a height map. All images were converted to a uniform format of 3000x4000 pixels, aligned, and cropped to 2680x3370 to remove empty borders. See the "Usage" section below for details about the stored formats made available here. The raw images for our dataset were recorded with a normal (RGB) and a FLIR-XT2 (thermal) camera on a DJI M600 drone. They show six large building blocks of around 20 buildings per block recorded in the city centre of the German city Karlsruhe east of the market square. Because of a high overlap rate of the images, the same buildings are on average recorded from different angles in different images about 20 times. All images were recorded during a drone flight on March 19, 2019 from 7 a.m. to 8 a.m. At this time, temperatures were between 3.78 ° C and 4.97 ° C, humidity between 80% and 98%. There was no rain on the day of the flight, but there was 2.3mm/m² 48 hours beforehand. For recording the thermographic images an emissivity of 1.0 was set. The global radiation during this period was between 38.59 W / m² and 120.86 W / m². No direct sunlight can be seen visually on any of the recordings. The dataset contains 926 images with a total of 6,927 annotations of thermal bridges on rooftops, split into train and test subsets with 723 (5,614) and 203 (1,313) images (annotations), respectively. The annotations only include thermal bridges that are visually identifiable with the human eye. Because of the aforementioned image overlap, each thermal bridge is annotated multiple times from different angles. For the annotation of the thermal images the image processing program VGG Image Annotator from the Visual Geometry Group, version 2.0.10, was used. The thermal bridge annotations are outlined with polygon shapes. These polygon lines were placed as close as possible but outside the area of significant temperature increase. If a detected thermal bridge was partially covered by another building component located in the foreground, the thermal bridge was also marked across the covering in case of minor coverings. Adjacent thermal bridges, which affect different rooftop components, were annotated separately. For example, a window with poor insulation of the window reveal located in the area of a poorly insulated roof is annotated individually. There is no overlap between annotated areas. While each image contains annotations, they also include thermal bridges present that are not annotated. Usage: Each compressed archive file represents one of the six flight paths. For the related publication the final path (Flug1_105Media) was used as a hold-out test sample. The archives contain Numpy files (one per image) of shape (2680, 3370, 5), where the final dimension is the colour channel in the format [B, G, R, Thermal, Height]. Archives were compressed using <a href="https://facebook.github.io/zstd/">ZStandard</a> compression. They can be decompressed in a terminal by running e.g. <pre><code class="language-bash">tar -I zstd -xvf Flug1_105Media.tar.zst</code></pre> these will be decompressed into the file structure: <pre><code>images/ └── Flug1_105Media/ └── DJI_0004_R.npy └── DJI_0006_R.npy └── ...</code></pre> Corresponding annotations are provided in the COCO JSON format. There is one file for training (Flug1_100Media - Flug1_104Media blocks) and one for test (Flug1_105Media block). They contain a single class (thermal bridge) and expect the folder structure shown below. Note: The annotation files contain relative paths to numpy files, in case of problems please convert to absolute paths (i.e. insert the containing directory before each file path in the JSON annotation files). We provide the <a href="https://github.com/Helmholtz-AI-Energy/TBBRDet">TBBRDet software</a> which includes a dataloader and dataset inspection tools which make use of the <a href="https://github.com/facebookresearch/detectron2">Detectron2</a> and <a href="https://github.com/open-mmlab/mmdetection">MMDetection</a> libraries. We recommend the following folder structure for use: <pre><code>├── train/ │ ├── Flug1_100-104Media_coco.json │ └── images/ │ ├── Flug1_100Media/ │ │ ├── DJI_XXXX_R.npy │ │ └── ... │ ├── ... │ └── Flug1_104Media/ │ ├── DJI_XXXX_R.npy │ └── ... └── test/ ├── Flug1_105Media_coco.json └── images/ └── Flug1_105Media/ ├── DJI_XXXX_R.npy └── ...</code></pre> Metadata: The experimental metadata was structured with the Spatio Temporal Asset Catalog (STAC) specification family. This specification provides a standardized way for describing geospatial assets. It defines related JSON object types of Item, Catalog, and Catalog, extending on Collection as the basis. One STAC Collection JSON object provides information about the recorded images and the environmental conditions during recordings. It also contains information about the overall bounding box of the entire area in which images were recorded. This object links to related STAC Item JSON objects containing information about the recorded city blocks and the cameras. The objects for the city blocks contain the GeoJSON geometry of the respective block and the corresponding bounding box. The objects containing the camera information are based on an existing STAC extension for camera related metadata. Metadata of the archived NumPy files for each image was structured using the Data Package schema from the Frictionless Standards. This standard describes a collection of data files. Therefore, metadata about all containerized NumPy files of the six flight paths (Flug1_100Media - Flug1_104Media blocks and Flug1_105Media block) is provided within a JSON-based file. Note that camera1 corresponds to the RGB camera and camera2 the thermal. FAIR Digital Objects: All files are represented in a standardized way as FAIR Digital Objects (FAIR DOs) to enable machine actionable decisions on the data in spirit of the FAIR principles. Persistent Identifier (PID): Persistent Identifiers (PIDs) are resolvable with the <a href="https://hdl.handle.net/">Handle.Net Registry (HNR)</a>. <table> <thead> <tr> <th scope="col">File</th> <th scope="col">Persistent Identifier (PID)</th> </tr> </thead> <tbody> <tr> <td>Flug1_100-104Media_coco.json</td> <td>21.11152/6ea60288-d895-414e-80c0-26c9fdd662b2</td> </tr> <tr> <td>Flug1_105Media_coco.json</td> <td>21.11152/58d43ddc-5e29-4980-8675-ae579b50a1e2</td> </tr> <tr> <td>Flug1_100.tar.zst</td> <td>21.11152/6858a0b5-cc60-40e9-afef-8c2dd8b35e8e</td> </tr> <tr> <td>Flug1_101.tar.zst</td> <td>21.11152/e670f510-7e00-4d3a-9b90-3bac7a7c069e</td> </tr> <tr> <td>Flug1_102.tar.zst</td> <td>21.11152/3ab9f444-05f6-445e-a691-62fae4021bea</td> </tr> <tr> <td>Flug1_103.tar.zst</td> <td>21.11152/365fd8cf-8e86-41b8-9d0e-b816fdd01d29</td> </tr> <tr> <td>Flug1_104.tar.zst</td> <td>21.11152/041a6111-644a-4617-afb3-3c421a88e8e3</td> </tr> <tr> <td>Flug1_105.tar.zst</td> <td>21.11152/f48bf4e7-3879-4216-8f64-45a060b8f658</td> </tr> <tr> <td>Flug1_100-105_frictionless_standards.json</td> <td>21.11152/7b58b3b5-75eb-4417-ac4d-abe025e159f6</td> </tr> <tr> <td>Flug1_collection_stac_spec.json</td> <td>21.11152/ba370aa3-6422-428c-9ff7-c2ef429df603</td> </tr> <tr> <td>Flug1_100_stac_spec.json</td> <td>21.11152/09cb76fc-b8cb-4116-a22a-68c5bdfa77b0</td> </tr> <tr> <td>Flug1_101_stac_spec.json</td> <td>21.11152/24a55398-b96b-43dd-b0fb-cd8ce302c7ce</td> </tr> <tr> <td>Flug1_102_stac_spec.json</td> <td>21.11152/721234ac-4b5a-4d02-9944-82a08ef2db35</td> </tr> <tr> <td>Flug1_103_stac_spec.json</td> <td>21.11152/ebaeb5bc-0514-47c9-bcd2-98f0253843d8</td> </tr> <tr> <td>Flug1_104_stac_spec.json</td> <td>21.11152/9854677c-77c5-4a0b-916b-57dd9ec20198</td> </tr> <tr> <td>Flug1_105_stac_spec.json</td> <td>21.11152/cfd0fc0e-f5ea-464e-a57f-28e882924860</td> </tr> <tr> <td>Flug1_camera1_stac-spec.json</td> <td>21.11152/976fcf28-f924-4a21-b53d-5d054ad8198d</td> </tr> <tr> <td>Flug1_camera2_stac-spec.json</td> <td>21.11152/37833c54-1d36-42e4-858d-831447122863</td> </tr> </tbody> </table>

opencc-by-4.0Aug 2022View details →

zenodo48/100

Urban material ground truth data for the 2015 APEX hyperspectral image of Brussels

This dataset entails a spectral library file (.sli file with matching .hdr text file) with 1350 georeferenced and labeled spectra derived from the 2m resolution airborne hyperspectral APEX image of Brussels (Belgium) that was acquired during the summer of 2015. The labeled spectra included in this dataset describe level 2A surface reflectance profiles ranging between 450 and 2431 nm. The original APEX image files can be downloaded via the <a href="https://belair.vito.be/en/belair-data" target="_blank" rel="noopener">Belair website</a>, and the preprocessing performed on this image data is explained in Sterckx et al. (2016) and Vreys et al. (2016). See the "Related works" section of this data publication. The main purpose of this dataset is to provide Ground Truth (GT) data for remote sensing-based mapping experiments with a generic urban spectral library, performed in the frame of the GENLIB research project. The content of this dataset hence focuses on the optical reflectance/absorption behaviour of urban surface materials and their variations. The spectra included in this dataset were manually sampled from the above mentioned APEX image and labeled using ancillary reference data (very high-resolution aerial imagery, Google Street View, LiDAR ...), already published urban spectral libraries, terrain knowledge and some field work. The header of the spectral library contains the various labels that were added to these spectra. These labels cover: <ul> <li>EAGLE Land Cover Component (LCC) from the EAGLE matrix version 3.1. Visit the <a href="https://land.copernicus.eu/en/eagle" target="_blank" rel="noopener">website of the EAGLE framework</a> for more information.</li> <li>Material Groups (MG).</li> <li>Artificial Material Types (AMT).</li> <li>Artificial Material Coating or Fabrication (AMCF).</li> <li>Artificial Material Forms (AMF).</li> <li>Latitude (degrees, WGS84).</li> <li>Longitude (degrees, WGS84).</li> </ul> The value domains of these spectrum attributes are described in the look-up table included as a CSV-file in this data publication. While considerable efforts have been made to safeguard the accuracy of these data, they are published as is, without any warranty or support. Use at your own discretion.

opencc-by-4.0May 2024View details →

zenodo48/100

Hyperspectral (RGB + Thermal) drone images of Karlsruhe, Germany - Raw images for the Thermal Bridges on Building Rooftops (TBBR) dataset

Overview: This repository contains the raw images for the dataset of <a href="https://doi.org/10.5281/zenodo.4767771">Thermal Bridges on Building Rooftops (TBBR) dataset</a>. This dataset contains 5696 drone images (2848 RGB and 2848 thermal) of building rooftops, recorded with a normal (RGB) and a FLIR-XT2 (thermal) camera on a DJI M600 drone. They show six large building blocks of around 20 buildings per block recorded in the city centre of the German city Karlsruhe east of the market square. Because of a high overlap rate of the images, the same buildings are on average recorded from different angles in different images about 20 times. All images were recorded during a drone flight on March 19, 2019 from 7 a.m. to 8 a.m. At this time, temperatures were between 3.78 ° C and 4.97 ° C, humidity between 80% and 98%. There was no rain on the day of the flight, but there was 2.3mm/m² 48 hours beforehand. For recording the thermographic images an emissivity of 1.0 was set. The global radiation during this period was between 38.59 W / m² and 120.86 W / m². No direct sunlight can be seen visually on any of the recordings. Usage: Each zip archive file represents one of the six drone flight paths. The archives contain JPG files of size 4000x3000 pixels (RGB) and 640x512 (Thermal), separated into individual directories for RGB and Thermal: <pre><code>├── Flug_100/ │ ├── RGB/ │ │ ├── DJI_0004.jpg │ │ ├── DJI_0006.jpg │ │ └── ... │ └── Thermal/ │ ├── DJI_0003_R.JPG │ ├── DJI_0005_R.JPG │ └── ... ├── Flug_101/ │ ├── RGB/ │ │ ├── DJI_0001.jpg │ │ ├── DJI_0003.jpg │ │ └── ... │ └── Thermal/ │ ├── DJI_0000_R.JPG │ ├── DJI_0002_R.JPG │ └── ... └── ...</code></pre> File Numbering/Naming Scheme: The pairs of RGB + Thermal images follow the simple numbering scheme of: RGB = Thermal + 1. For example, DJI_0003_R.jpg and DJI_0004.JPG are the matching Thermal and RGB images, respectively, that can be merged to form a single hyperspectral drone image. To perform the merging, we recommend using the merge_image_layers.py script provided by the associated <a href="https://github.com/Helmholtz-AI-Energy/TBBRDet">TBBRDet software</a> (see the scripts/alignment/ directory). For convenience, we have provided a CSV listing all annotated images in the <a href="https://doi.org/10.5281/zenodo.4767771">Thermal Bridges on Building Rooftops (TBBR) dataset</a>. The CSV format is as follows: <pre><code>Flight,RGB,Thermal Flug_100,DJI_0048.jpg,DJI_0047_R.JPG Flug_100,DJI_0050.jpg,DJI_0049_R.JPG ...</code></pre>

opencc-by-4.0Nov 2022View details →

zenodo48/100

Hyperspectral Imaging dataset for use in Heritage Science

The following data sets were collected to support the potential uses of opensource data in the context of digital humanities and heritage sciences.   This proposed experiment is conducted by the UCL Institute for Sustainable Heritage in collaboration with the Centre for Digital Humanities. Imaging methods including Photography, Multispectral Imaging, Hyperspectral Imaging and Xray Fluorescence Mapping have been collected along with the complete readout metadata of the instrumentation.   We hope that you find the data helpful, and we welcome you to use the data in any way you wish, for all and any analysis development purposes. For us to build upon this research, we ask that in return you would be willing to share in some regard your experiences in using open-source data, using our data, successes and issues.   If you would be willing to engage with us in this endeavor, please feel free to contact us so that we may be able to follow up with you.   Other Data sets available <a href="https://zenodo.org/record/7319696#.Y3NuOXbP2Uk">Here</a>   E: <a href="mailto:molly.fort.21@ucl.ac.uk">molly.fort.21@ucl.ac.uk</a>  Object Paradata;   <ul> <li>Postcard – c. Early 1900's  </li> <li>Language – Eng. </li> <li>Materials – colour print on card, metallic leafing. </li> <li>Front transcription -  </li> <li> ‘Greetings’ </li> <li> ‘May your Birthday bring you Peace & perfect Happiness, Golden hopes & Love of Friends, And every Happiness this world can send.’ </li> <li>Object Dimensions – 138mm X 88mm </li> </ul> The postcard is an item of ephemera donated to the UCLDH Digitisation Suite by Prof Melissa Terras, for teaching and training purposes in 2015. This folder contains: Hyperspectral Image data collected using a <a href="https://www.clydehsi.com/hyperspectral-cameras">ClydeHSI VNIR-HR+ Hyperspectral Imaging System</a>. Images captured : 477x484 pixel, 304 spectral band images, 4*4 pixel binning <ul> <li>*.hdr - Header file read out from the ClydeHSI systems instructions for reading the subsequent .raw spectral database. </li> <li>*.raw - Hyperspectral image data cube information. Combination with hdr file creates a ENVI file format, this can be read into a variety of image analysis software packages. </li> <li>postcardhsi.ini - Metadata collected and read out from ClydeHSI system.</li> <li>Dark/White.corr - Correction files taken from camera for processing and minimalising system noise and illumination variences.</li> <li>*_refl.* - Pre - Corrected hyperspectral image data, using provided ClydeHSI software.</li> <li>Truecolour RGB reference image </li> </ul> Each raw and header file set makes-up a single data set in ENVI file format.  ENVI reading support exists in Python, R, Matlab, and other common image analysis packages.

opencc-by-4.0Nov 2022View details →

edi48/100

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.

openCustomJan 2020View details →

zenodo44/100

Lake Cadagno sediment core hyperspectral imaging and pigment data tables

Data Tables related to the manuscript "Hyperspectral imaging sediment core scanning tracks high-resolution Holocene variations in (an)oxygenic phototrophic communities at Lake Cadagno, Swiss Alps" in submission.

opencc-by-4.0Jan 2023View details →

zenodo40/100

Hyperspectral imager acquisitions from SMART Soils Test Bed, 2022-04-18

Hyperpsectral imager data retrieved over the Lawrence Berkeley National Lab SMART Soils Test Bed on 2022-04-18 at three times (11:20, 12:34, 13:44). Radiance data (mW cm−2 μm−1 sr−1) subset to bands of interest over the Headwall Hyperspec Imager's 680-800nm range (680-682, 757-652, 769-772, and 778-780 nm) to retrieve RED, NIR, NDVI and SIF while minimizing file size. Data associated with Ruehr et al. 2023, 'Quantifying seasonal and diurnal cycles of solar-induced fluorescence with a novel hyperspectral imager,' submitted to Geophysical Research Letters in November 2023. Code for processing these data and descriptions of the files are available at https://github.com/sruehr/SIFretrieval.

opencc-by-4.0Nov 2023View details →

zenodo40/100

Dataset and software for processing of hyperspectral images of different CDW materials

<h2>Overview</h2> The provided scripts are designed to process hyperspectral images of construction and demolition waste (CDW) materials, extract relevant features, and train a machine-learning model for material classification. The scripts perform the following tasks: <ol> <li>Feature Extraction: Extract spectral features from hyperspectral data.</li> <li>Background Removal and Subset Extraction: Remove backgrounds from images and extract subsets for analysis.</li> <li>Data Visualization: Generate plots to visualize the extracted features and reflectance curves.</li> <li>Machine Learning Model Training: Using the extracted features, train and evaluate a multilayer perceptron (MLP) classifier.</li> </ol> <h2>Prerequisites</h2> Before running the scripts, ensure that you have the following: <ul> <li>Python 3.x installed on your system.</li> <li>Required Python packages: <ul> <li><code>numpy</code></li> <li><code>matplotlib</code></li> <li><code>scipy</code></li> <li><code>pandas</code></li> <li><code>scikit-learn</code></li> <li><code>seaborn</code></li> <li><code>rembg</code> (for background removal)</li> <li><code>Pillow</code> (PIL)</li> </ul> </li> <li>Hyperspectral data files in <code>.mat</code> format containing calibrated hyperspectral cubes and wavelength information.</li> <li>A directory structure to organize input and output files as described in each script.</li> </ul> <h2>Scripts Description</h2> <h3>1. <code>hyperspectral_features_v2.py</code></h3> <h4>Purpose</h4> This script processes individual hyperspectral image files to extract spectral features from a central subset of the image. It generates RGB images from the hyperspectral data, plots the mean reflectance spectra, and outputs a LaTeX-formatted table containing the extracted features. <h4>Functionality</h4> <ul> <li>Loading Data: Reads <code>.mat</code> files containing hyperspectral data from a specified input directory.</li> <li>Feature Calculation: <ul> <li>Calculates mean reflectance within a central window of the image.</li> <li>Extracts spectral features such as peak wavelength and area under the reflectance curve.</li> <li>Records reflectance values at selected wavelengths, including standard RGB channels and additional wavelengths.</li> </ul> </li> <li>RGB Image Generation: Creates RGB images using specific wavelengths corresponding to the red, green, and blue channels.</li> <li>Spectra Plotting: Plots the mean reflectance spectra for each sample.</li> <li>LaTeX Table Generation: Produces a LaTeX-formatted table of the extracted features for inclusion in a report or paper.</li> </ul> <h4>Usage Instructions</h4> <ol> <li> Prepare Input Data: <ul> <li>Place your <code>.mat</code> files containing the hyperspectral data in the appropriate input directory (e.g., <code>input/mortar</code>).</li> </ul> </li> <li> Run the Script: <ul> <li>Modify the <code>materials</code> list at the end of the script to include the materials you want to process (e.g., <code>materials = ['mortar']</code>).</li> <li>Execute the script: <div> <div>bash</div> <div> <div> <div> </div> </div> </div> </div> </li> </ul> </li> </ol>

opencc-by-4.0Sep 2024View details →

zenodo40/100

OHS data provided by Competition in Hyperspectral Remote Sensing Image Intelligent Processing Application

Images from Chinese Orbita Hyperspectral Satellites (OHS) provided by the Competition in Hyperspectral Remote Sensing Image Intelligent Processing Application are shared. All the images have been radiometric calibrated and atmospheric corrected by the author. Paper: J. He, J. Li, Q. Yuan, H. Shen, and L. Zhang, "Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution," IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2021. More information about the author can be found at https://jianghe96.github.io/ If this dataset is helpful please cite as: <pre>@article{he2021spectral, title={Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution}, author={He, Jiang and Li, Jie and Yuan, Qiangqiang and Shen, Huanfeng and Zhang, Liangpei}, journal={IEEE Transactions on Neural Networks and Learning Systems}, year={2021}, }</pre>

opencc-by-4.0Nov 2021View details →

zenodo40/100

Epipremnum aureum VIS-SWIR Hyperspectral Image from HYPERIA

The spectral image show two Epipremnum aureum leaves measured in the visible and short wave infrared (400-1700 nm) using the HERAVISSWIR device developed during the HYPERIA project. The leaf on the right is healty while the leaf on the left has evident stress signatures. The image was recorded from a 1m distance using a combination of a LED and halogen lamps to cover the full spectral range.

opencc-by-4.0Jul 2024View details →

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Hyperspectral Image of a 6 Metre Genealogical Roll as a Video

This video scrolls down the length of the 6 metre genealogical roll, whilst also passing through the wavelength range of the hyperspectral image. The image is made from multiple hyperspectral scans of a medieval genealogical roll tracing the lineage of England’s Plantagenent rulers back to Adam and Eve (<a href="https://ucldigitalpress.co.uk/Book/Article/2/9/18/">https://ucldigitalpress.co.uk/Book/Article/2/9/18/</a>). More info on the imaging pipeline is available here <a href="https://zenodo.org/record/1312942#.W7yAVGhKhPa">https://zenodo.org/record/1312942#.W7yAVGhKhPa</a>. Images courtesy of Special Collections, UCL Library Services.

opencc-by-4.0Jul 2018View details →

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Processed airborne hyperspectral images and mosaics of a Paranapanema River region in Capivara reservoir, Brazil

This database is a image set of a strongest glint-affected region of inland water Capivara reservoir, Brazil. We carried out a flight survey in September 2016 on the confluence region of the Tibagi and Paranapanema Rivers. We use the hyperspectral camera manufactured by Rikola, model FPI2014, wich collect 25 spectral bands at following intervals and full widths at half maximum (FWHM), both expressed in nanometers (nm): 505.37, (9.51), 515.31 (14.05), 528.55 (14.82), 539.87 (14.03), 546.99 (14.31), 554.99 (13.26), 560.22 (12.11), 570.44 (14.31), 579.58 (13.26), 592.57 (16.57), 605.73 (14.98), 620.22 (16.26), 625.92 (15.47), 655.06 (12.55), 665.72 (15.59), 670.03 (15.74), 681.33 (15.89), 695.04 (14.96), 700.46 (15.44), 707.96 (15.32), 715.19 (15.37), 725.37 (14.72), 737.29 (14.98), 749.87 (15.08) and 780.1 (14.72).  The external orientation parameters (EOP) are acquired by dual frequency GPS and adjusted with tie points computed by bundle adjustment. We perform individual georeferencing on each individual image. This dataset present processing using nine approaches to mosaicking individual georeferenced images. Three of then are new proposed methods developed by the authors. Details of processing and methodology are described on the oficial paper (currently in review process of journal). The authors thank the Graduate Program in Cartographic Sciences (PPGCC) of the School of Science and Technology (UNESP), campus Presidente Prudente, for allowing the development of this research; the National Council for Scientific and Technological Development (CNPq) and the Coordination for the Improvement of Higher Education Personnel (CAPES) for financial assistance dedicated to the project. The authors extend special thanks to the São Paulo Research Foundation (FAPESP) for financial support for the hyperspectral camera (2013/50426-4).

opencc-by-4.0Jan 2019View details →

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Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 1 - Phycocyanin

Accompanying data to publication: Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging Hyperspectral data with all their processing steps - normalization, ROI, subsets, masking - resulting in RABD indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.

opencc-by-4.0Aug 2024View details →

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Exploring the potential of Near Infrared Hyperspectral Imaging and chemometrics to discriminate soil seed bank of two timber species central African : Erythrophleum suaveolens (Guill. & Perr.) Brenan, and Erythrophleum ivorense A. Chev.

The data of this study are accessible by sending a request to the corresponding author at the email address: douhch382@gmail.com. <a href="https://doi.org/10.5281/zenodo.13908452" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13908452</a>

opencc-by-4.0Oct 2024View details →

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Images for a publication "Lossless Hyperspectral Image Compression in Comet Interceptor and Hera Missions with Restricted Bandwith" by Skog et al.

This archive contains following simulated hyperspectral datacubes for ASPECT (Asteroid Spectral Imager) instrument on ESA Hera mission used in the manuscript: <ul> <li>ASPECT_simulated_data_Vis.zip - ASPECT visible channel datacubes noisless and with simulated instrument noise for three exposure times indicated in the file name.</li> <li>ASPECT_simulated_data_NIR.zip - ASPECT near-infrared channels datacubes noisless and with simulated instrument noise for three exposure times indicated in the file name.</li> <li>ASPECT_simulated_data_Vis_NIR_filtered.zip - Same ASPECT visible and near-infrared channels datacubes filtered with noise filters indicated in the file name. The visible datacubes have designation Vis in file name. The remaining datacubes without channel designation are near-infrared ones.</li> </ul> The recorded scene and imaging distance is indicated in file name. D1 - Didymos asteroid, D2 - Dimorphos asteroid. All datacubes are in Matlab (.mat) format and with single wavelength .png preview included. For details please check associated manuscript.

opencc-by-4.0Oct 2024View details →

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WHU-OHS: A benchmark dataset for large-scale Hyperspectral Image classification

The WHU-OHS dataset is made up of 42 OHS satellite images acquired from more than 40 different locations in China. The imagery has a spatial resolution of 10 m (nadir) and a swath width of 60 km (nadir). There are 32 spectral channels ranging from the visible to near-infrared range, with an average spectral resolution of 15 nm. We cropped each image into 512 × 512 pixels with a stride of 32. There are 4822, 513, and 2460 sub-images in the training, validation, and test sets, respectively. For transferability test, we choose eight pairs of OHS images, and each pair contains one source image (S) and one target image (T): S1: Changchun T1: Jilin S2: Wuxi T2: Shanghai S3: Guangzhou T3: Zhongshan S4: Xining T4: Lanzhou S5: Hetian T5: Kelamayi S6: Anyi T6: Nanchang S7: Changde T7: Changsha S8: Tianjin T8: Tangshan The 26 OHS images except for the eight pairs: O1: Baoding O2: Chongqing O3: Fujin O4: Huainan O5: Huhehaote O6: Jinzhong O7: Luliang O8: Manasi_1 O9: Manasi_2 O10: Nanmulin O11: Neimenggu O12: Qingdao O13: Qinghuangdao O14: Shawan O15: Shenyang O16: Shuozhou O17: Songpan O18: Taian O19: Tongjiang_1 O20: Tongjiang_2 O21: Wuzhong O22: Xundian O23: Xuzhou O24: Yidu O25: Zangzu O26: Zhongshan The image patches have been normalized and scaled by 10000 to reduce storage cost. Divide the pixel values by 10000 and then the image patches can be used directly.

opencc-by-4.0Sep 2022View details →

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Gloss estimation of chocolate sprinkles with Hyperspectral Imaging

Gloss is an important characteristic in the quality evaluation in chocolate production. However, standard glossing measuring devices face several challenges when measuring food products, in particular those with curved surfaces and small, such as chocolate sprinkles. Therefore, gloss evaluation of chocolate sprinkles is typically done by human visual assessment. In this respect, hyperspectral imaging (HSI), combining spectroscopy and imaging, has gained attention as a non-destructive and non-contact real-time detection tool for food quality analysis and control. This technique adds an extra dimension to traditional machine vision techniques by providing images at a larger number of more narrow wavebands. This can potentially increase the discrimination power. The main task of this dataset is to classify between 5 classes of chocolate production stages. The labels are indicated on the file names. The labels are: EXTRUDER, GLUCOSE, STAGE1, STAGE2, STAGE3.  The .zip file contains two folders named: - envi_sprinkles_dataset_11_04_22 (Train Set) - envi_sprinkles_test_dataset_11_04_22 (Test Set) In envi_sprinkles_dataset_11_04_22 the file name convention is BATCH_CLASSNAME_INDEX.hdr In envi_sprinkles_test_dataset_11_04_22 the file name convention is CLASSNAME_INDEX.hdr Note that in the train set the batch information is present in the file name, if two samples belongs to the same batch means that they were produced at the same time. The samples format is ENVI. Consist of a header file and ENVI binary data file with file extensions <code>.hdr</code> and <code>.raw</code>, respectively. The function writes the wavelength and metadata information to the ENVI header file and the data cube containing the hyperspectral images to the ENVI binary data file. The image shape is [272x512x16] , [HEIGHT, WIDTH, BANDS], the pixel format is uint16.

opencc-by-4.0Dec 2022View details →

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Hyperspectral image using Specim IQ camera from Chatzivariti Winery (survey date 2021-08-13) #33

The image contains the hyperspectral data from Chatzivariti Winery captured with Specim IQ hyperspectral camera. The grape variety of the field is Traminer (Gewürztraminer). The image captured at 2021-08-13 (#33) and it was processed using Specim IQ Studio.

opencc-by-4.0Feb 2023View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record