MaNGA is a component of the Fourth-Generation Sloan Digital Sky Survey whose goal is to map the detailed composition and kinematic structure of nearby galaxies. MaNGA uses integral field unit (IFU) spectroscopy to measure spectra for hundreds of points within each galaxy. MaNGA’s goal is to understand the “life history” of present-day galaxies from imprinted clues of their birth and assembly, through their ongoing growth via star formation and merging, to their death from quenching at late times. The primary MaNGA data products are composed of 3-D calibrated data cubes produced by the DRP[89] and 2-D maps of derived quantities, such as emission line fluxes, gas and stellar kinematics, and stellar population properties, produced by the DAP[149] from those cubes. The 3-D data cubes are constructed from a few tens to a few thousands of individual spectra that have been combined onto a regular grid. The 2-D maps of derived quantities are constructed by analyzing individual or binned groups of spaxels and constructing maps of the quantities at the relevant on-sky location.
We introduce HSIRS, a large scale dataset of hyper-spectral images along with corresponding manually annotated segmentation maps for material characterization and classification based on spectral signature. Such data can be used to simulate any type of spectrometer and to train DNNs end-to-end for spectral reconstruction and image segmentation tasks. HSIRS features scenes containing real and fake (made of polyester, plastic or ceramic) food items with different backgrounds and scene layouts, some scenes contain also color checkers. Spectral bands are sequentially captured using a VariSpecTM tunable color filter and the scene is illuminated with 4 Halogen light sources.
Tecnalia Hyperspectral Dataset contains different non-ferreous fractions of Waste from Electric and Electronic Equipment (WEEE) of Copper, Brass, Aluminum, Stainless Steel and White Copper. Images were captured by a hyperspectral Specim PHF Fast10 camera that is able to capture wavelengths in the range 400 to 1000 nm with a spectral resolution of less than 1 nm. The PHF Fast10 camera is equipped with a CMOS sensor (1024 × 1024 resolution), a Camera Link interface and a special Fore objective OL10. The provided dataset contains 76 uniformly distributed wave-lengths in the spectral range [415.05 nm, 1008.10 nm]. Illumination setup, as described in \cite{picon2012real}, was specifically designed to reduce the specular reflections generated by the surface of the non-ferrous materials and to provide a homogeneous and even illumination that covers the wavelengths sensitive to the hyperspectral camera. The illumination system consists of a parabolic surface that uniformly distributes the light generated by 9 halogens and 18 white LEDs covering the spectral range between 400 to 1000 nm.
Towards automated analysis of large environments, hyperspectral sensors must be adapted into a format where they can be operated from mobile robots. In this dataset, we highlight hyperspectral datacubes collected from the Hyper-Drive imaging system. Our system collects and registers datacubes spanning the visible to shortwave infrared (660-1700 nm) in 33 wavelength channels. The system also simultaneously captures the ambient solar spectrum reflected off a white reference tile. The dataset consists of 500 labeled datacubes from on-road and off-road terrain compliant with the ATLAS. This work first appeared at WHISPERS 2023 In Athens, Greece and is a product of the Robotics and Intelligent Vehicles Research Laboratory (RIVeR) at Northeastern University. In addition to the 500 labeled hyperspectral datacubes, raw ROS bagfiles generated of each of the sensor feeds at a higher frame rate are available here. These files are provided as an additional resource and do not contain semantic labels, but contain ~10,000 additional hyperspectral datacubes of in-between frames from the labeled dataset. It also contains additional datatypes for terrain analysis such as inertial measurement unit (IMU) data. To the best of the authors knowledge, it is the largest vehicle-centric hyperspectral dataset currently available! This research was sponsored by the United States Army Core of Engineers (USACE) Engineer Research and Development Center (ERDC) Geospatial Research Laboratory (GRL) and was accomplished under Cooperative Agreement Federal Award Identification Number (FAIN) W9132V-22-2-0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of USACE EDRC GRL or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.