<title>EO-1/Hyperion hyperspectral imager design, development, characterization, and calibration</title>

Abstract

The Hyperion Imaging Spectrometer is one of three principal instruments aboard the EO-1 spacecraft. Its mission as a technology demonstrator is to evaluate on-orbit issues for imaging spectroscopy and to assess the capabilities of a spacebased imaging spectrometer for earth science and earth observation missions. The instrument provides earth imagery at 30 meter spatial resolution, 7.5 km swath width in 220 contiguous spectral bands at 10 nm spectral resolution. Spectral range is from 0.4 µm to 2.5 µm. The instrument includes internal and solar calibration sub-systems. This paper will review the design, construction and calibration of the Hyperion instrument. The on-orbit plans and operations will be presented along with updated calibration and characterization measurements.

An assessment of hydrothermal alteration in the Santiaguito lava dome complex, Guatemala: implications for dome collapse hazards

“…30 m spatial resolution) were subset from 242 to 196 bands to eliminate repeating and unused bands. They were also calibrated to reflectance using ACORN, which corrects for atmospheric moisture on a pixel by pixel basis (e.g., Gao et al 1993), as well as the column by column radiometric shifts in wavelength caused by the instrument's pushbroom engineering design (Folkman et al 2001).…”

A Semi-automated GIS Model for Extracting Geological Structural Information from a Spaceborne Thematic Image

“…While multispectral data can also be used as an input for the classification, in the current case IS was used in order to enhance classification of strata and consequently enhance the accuracy of extracted structural data. The presented model made use of Earth Observing-1 (EO-1) Hyperion spaceborne imaging spectrometer data (Folkman et al, 2001). Hyperion data has potential advantages over multispectral instruments such as Landsat-ETM+ due to its high spectral resolution data that provides an enhanced level of information for atmospheric correction in order to derive surface reflectance and achieve better classification results.…”

Mapping Mangrove Species Using Hyperspectral Data: A Case Study of Pichavaram Mangrove Ecosystem, Tamil Nadu

“…However, it is not necessary to use all these bands for identifying and differentiating the species (Folkman 2001). Extraction of end members is required to classify the data based on spectral variation of the species visible from satellite data.…”

“…The prospective of hyperspectral remote sensing is to store data in hundreds of contiguous narrow bands which provide useful information to assess, enhance and discriminate individual features. This greatly increases the level of detail by a complete spectrum of a feature (Folkman 2001) However, it is very difficult to extract, analyze or classify hyperspectral satellite data without the proper image processing algorithm, owing to its high dimensionality (Green 1998). High dimensionality challenges the precision of the estimates of class distribution like the mean and the covariance in the feature space.…”