Development and operations of the EO-1 Hyperion Imaging Spectrometer

Pearlman, Jay; Segal, C.; Liao, Lushalan B.; Carman, Stephen L.; Folkman, Mark A.; Browne, William Frank; Ong, Lawrence; Ungar, Stephen

doi:10.1117/12.494251

Cited by 35 publications

(23 citation statements)

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“…The National Polar-orbiting Operational Environmental Satellite System (NPOESS) National Polar-orbiting Partnership's Visible Infrared Imaging Radiometer Suite (VIIRS) has similar discrete-band coverage as MODIS, with slightly better radiometric accuracies of 1.2 to 1.6 % (Xiong et al, 2014). Hyperion (Pearlman et al, 2000), with continuous spectral coverage from 400 to 2500 nm and 10 nm spectral resolution, has a 3.5 % radiometric uncertainty (Beiso, 2002). With similar spectral coverage and resolution, the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) has an uncertainty on the order of 4 % (Green et al, 1998).…”

Section: Introductionmentioning

confidence: 99%

Radiometric flight results from the HyperSpectral Imager for Climate Science (HySICS)

Kopp

Smith

Belting

et al. 2017

Geosci. Instrum. Method. Data Syst.

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Abstract. Long-term monitoring of the Earth-reflected solar spectrum is necessary for discerning and attributing changes in climate. High radiometric accuracy enables such monitoring over decadal timescales with non-overlapping instruments, and high precision enables trend detection on shorter timescales. The HyperSpectral Imager for Climate Science (HySICS) is a visible and near-infrared spatial/spectral imaging spectrometer intended to ultimately achieve ∼ 0.2 % radiometric accuracies of Earth scenes from space, providing an order-of-magnitude improvement over existing spacebased imagers. On-orbit calibrations from measurements of spectral solar irradiances acquired by direct views of the Sun enable radiometric calibrations with superior long-term stability than is currently possible with any manmade spaceflight light source or detector. Solar and lunar observations enable in-flight focal-plane array (FPA) flat-fielding and other instrument calibrations. The HySICS has demonstrated this solar cross-calibration technique for future spaceflight instrumentation via two high-altitude balloon flights. The second of these two flights acquired high-radiometricaccuracy measurements of the ground, clouds, the Earth's limb, and the Moon. Those results and the details of the uncertainty analyses of those flight data are described.

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Section: Introductionmentioning

confidence: 99%

Radiometric flight results from the HyperSpectral Imager for Climate Science (HySICS)

Kopp

Smith

Belting

et al. 2017

Geosci. Instrum. Method. Data Syst.

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“…With pushbroom scanning mode, the instrument is capable of acquiring orbital images with 242 bands (10 nm of bandwidth) in the visible (VIS), near infrared (NIR) and shortwave infrared (SWIR) (400 -2500 nm range) with a spatial resolution of 30 m and a swath width of 7.7 km [1]. The sensor allows the extraction of an almost continuous reflectance spectrum for each scene element [2,3].…”

Section: Introductionmentioning

confidence: 99%

Mapping Land Cover Classes in the Southern State of Rio Grande Do Sul, Brazil, Using Multiple Endmember Spectral Mixture Analysis (MESMA) Model Applied to Hyperion/EO-1 Hyperspectral Data

Linn

Rolim

2015

IJARSG

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In this paper we evaluated the potential use of Multiple Endmember Spectral Mixture Analysis (MESMA) applied to EO-1 Hyperion hyperspectral data to separate land covers (soil = dunes and dry fields; green vegetation = pinus, eucalyptus and grasslands; water = without sediments, with sediments, and with chlorophyll; and shade), in the southern state of Rio Grande do Sul, Brazil. The approach involved (a) preprocessing and atmospheric correction of Hyperion image; (b) sequential use of Minimum Noise Fraction (MNF), Pixel Purity Index (PPI) and n-Dimensional Visualizer techniques in the visible to shortwave range for the initial selection of a group of endmember, and another group of pixels for model validation; (c) use of the software Visualization and Image Processing for Environmental Research (VIPER) Tools to perform the final selection of endmembers based on the spectral library, and to obtain MESMA models; and (d) evaluation of resulting fraction images and RMSE values to determine the optimal number of endmembers of the MESMA model. Results showed that a four-endmember MESMA model described the diversity of the scene components, including that of materials within the same class (e.g., pinus and eucalyptus) and produced the largest fractions and the lowest RMSE values on a per-pixel basis. Results also showed the performance of MESMA applied to Hyperion data to discriminate properly land covers in the coastal plains, even considering the low signal-to-noise ratio of the instrument.

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“…The scenario for both is the detection of a sub-pixel road in a forested background, with the use of a road spectrum (from a library or previous data collection) and a spectral matched filter. The analysis uses a model for the Hyperion [11] sensor, and an atmospheric compensation algorithm with 1% (1 σ) accuracy.…”

Section: Example Analysis Resultsmentioning

confidence: 99%

Spectral imaging system performance forecasting

Kerckes

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003

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-The quantitative forecasting of spectral imaging system performance is an important capability at every stage of system development including system requirement definition, system design, and even sensor operation. However, due to the complexity of the end-to-end remote sensing system involved, the analyses are often performed piecemeal by various groups, and then merged together. The ability to understand system sensitivities also supports the best use of an operational system and is thus desirable.It was with this perspective and goal to better perform end-toend remote sensing system analyses that work was undertaken in the late 1980's to develop models that can be efficiently used as part of the system design and operation. Both simulation and analytical models were developed. The simulation approach has the advantage of creating an actual image, which can include non-linear effects or specified instrument artifacts, while the analytical approach has the benefit of being much simpler computationally and amenable to large numbers of comprehensive trade studies.In the mid 1990's, the analytical approach was extended to the case of unresolved object detection. By taking advantage of the spectral information, objects and materials that are not spatially resolved in the imagery can still be detected and identified. Subsequently, this model, which was developed for the reflective solar part of the optical spectrum, was extended to the thermal infrared. Here, surfaces are characterized not only by their spectral emissivity means and covariances, but also their physical temperature mean and standard deviation. The model has also been extended to explore linear unmixing applications through the implementation of multiple classes in the target class. This has allowed the exploration of the role of class variability in unmixing abundance estimation.This paper provides an overview of this model development activity as well as show examples of how it can be used in the various applications. Examples include the impact of system parameters sub-pixel object detection and abundance estimation applications. Key capabilities as well as limitations of this analytical modeling approach are identified. System understanding developed through the use of the model is highlighted and the future enhancements are discussed.

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Development and operations of the EO-1 Hyperion Imaging Spectrometer

Cited by 35 publications

References 0 publications

Radiometric flight results from the HyperSpectral Imager for Climate Science (HySICS)

Radiometric flight results from the HyperSpectral Imager for Climate Science (HySICS)

Mapping Land Cover Classes in the Southern State of Rio Grande Do Sul, Brazil, Using Multiple Endmember Spectral Mixture Analysis (MESMA) Model Applied to Hyperion/EO-1 Hyperspectral Data

Spectral imaging system performance forecasting

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