Hyperion, a space-based imaging spectrometer

By J. Pearlman, P. Barry, C. Segal, J. Shepanski, D. Beiso, S. Carman. IEEE Transactions on Geoscience and Remote Sensing, 2003

571 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Astronomy and Astrophysics, Atmospheric Science

View Paper: https://www.semanticscholar.org/paper/16ba13d5be8f899de362ca784dfcb087c43347eb

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Citations in the corpus

View angle effects on the discrimination of soybean varieties and on the relationships between vegetation indices and yield using off-nadir Hyperion data (2009, background): “The sensorhas a nominal ground instantaneousfield of view (GIFOV) of 30 m (Pearlman et al., 2003).”
Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective* (2016, background): “…resolution compared with the Hyperion (United States National Aeronautics and Space Institute) sensor, which has 220 bands that are 10 nm wide (Pearlman et al. 2003) and can detect subtle differences between similar surface materials that are not detectable using the broad Landsat TM bands.”
Foliar spectral properties following leaf clipping and implications for handling techniques (2006, unknown): “Specifically, we increased the spectral range from between 306 nm and 1138 nm to between 350 nm and 2500 nm in order to correspond better with remote hyperspectral data from sources such as Hyperion (Pearlman et al., 2003; Vane & Goetz, 1993) that have broad spectral ranges and fine resolution.”
A review of machine learning in processing remote sensing data for mineral exploration (2021, methodology): “Hyperion is the first space-borne hyperspectral sensor, which is capable of providing data in the spectrum of VNIR and SWIR, launched in November 2000 as part of NASA’s EO-1 Millennium Mission (Pearlman et al., 2003).”
Interpretation of snow properties from imaging spectrometry (2009, methodology): “One set of results presented here uses data fromHyperion (Pearlman et al., 2003), the first Earth-looking imaging spectrometer on a”
Wavelength selection and spectral discrimination for paddy rice, with laboratory measurements of hyperspectral leaf reflectance (2011, methodology): “(1) A 215-band combination, dividing the spectrum from 350 to 2500 nm into 215 bands by calculating each 10 nm wavelength average, and thus is similar to the band set of the first space-borne hyperspectral sensor, Hyperion (Pearlman et al., 2003).”
A review of neural networks in plant disease detection using hyperspectral data (2018, background): “The hyperspectral sensors are not only orbiting around Earth [22], but also around Mars [23].”
Overview of the Earth Observing One (EO-1) Mission (2002, background): “for hyperspectral application evaluations [8].”
Processing Hyperion and ALI for forest classification (2003, methodology): “The data used in the current analysis are processed to Level 1B1 [ 12 ].”
Investigation of the random forest framework for classification of hyperspectral data (2005, background): “These instruments characterize spectral signatures with much greater detail than traditional multispectral sensors and thereby can potentially provide improved discrimination of targets [1].”