Overview of the Earth Observing One (EO-1) Mission

Ungar, Stephen

doi:10.1109/igarss.2002.1025108

Cited by 68 publications

(80 citation statements)

References 14 publications

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“…Two technology demonstration missions, Hyperion onboard NASA's Earth Observing-1 (EO-1) spacecraft [32] and the Compact High Resolution Imaging Spectrometer (CHRIS) on ESA's Proba-1 microsatellite [33], have been the main providers of space-based hyperspectral data over the last few decades. Even though both missions have largely exceeded their planned one-year lifetime, they are still very valuable sources of publicly-available spaceborne hyperspectral data.…”

Section: Introductionmentioning

confidence: 99%

The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation

et al. 2015

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Imaging spectroscopy, also known as hyperspectral remote sensing, is based on the characterization of Earth surface materials and processes through spectrally-resolved measurements of the light interacting with matter. The potential of imaging spectroscopy for Earth remote sensing has been demonstrated since the 1980s. However, most of the developments and applications in imaging spectroscopy have largely relied on airborne spectrometers, as the amount and quality of space-based imaging spectroscopy data remain relatively low to date. The upcoming Environmental Mapping and Analysis Program (EnMAP) German imaging spectroscopy mission is intended to fill this gap. An overview of the main characteristics and current status of the mission is provided in this contribution. The core payload of EnMAP consists of a dual-spectrometer instrument measuring in the optical spectral range between 420 and 2450 nm with a spectral sampling distance varying between 5 and 12 nm and a reference signal-to-noise ratio of 400:1 in the visible and near-infrared and 180:1 in the shortwave-infrared parts of the spectrum. EnMAP images will cover a 30 km-wide area in the across-track direction with a ground sampling distance of 30 m. An across-track tilted observation capability will enable a target revisit time of up to four days at the Equator and better at high latitudes. EnMAP will contribute to the development and exploitation of spaceborne imaging spectroscopy applications by making high-quality data freely available to scientific users worldwide.

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Section: Introductionmentioning

confidence: 99%

The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation

et al. 2015

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“…It has also become known as "Hyperspectral Imaging" or "HSI". Spectral mapping using imaging spectrometer data is well established and routinely used for numerous applications [3][4][5][6][7][8][9]. Probably the most widely documented use of these data is for identifying and mapping specific minerals associated with hydrothermally altered rocks (rocks changed by hot water moving through them), which occur in active geothermal systems and relict mineralized systems (ore deposits) worldwide [1,[9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Effect of Reduced Spatial Resolution on Mineral Mapping Using Imaging Spectrometry—Examples Using Hyperspectral Infrared Imager (HyspIRI)-Simulated Data

et al. 2011

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Abstract:The Hyperspectral Infrared Imager (HyspIRI) is a proposed NASA satellite remote sensing system combining a visible to shortwave infrared (VSWIR) imaging spectrometer with over 200 spectral bands between 0.38 and 2.5 μm and an 8-band thermal infrared (TIR) multispectral imager, both at 60 m spatial resolution. Short Wave Infrared (SWIR) (2.0-2.5 μm) simulation results are described here using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data in preparation for the future launch. The simulated data were used to assess the effect of the HyspIRI 60 m spatial resolution on the ability to identify and map minerals at hydrothermally altered and geothermal areas. Mineral maps produced using these data successfully detected and mapped a wide variety of characteristic minerals, including jarosite, alunite, kaolinite, dickite, muscovite-illite, montmorillonite, pyrophyllite, calcite, buddingtonite, and hydrothermal silica. Confusion matrix analysis of the datasets showed overall classification accuracy ranging from 70 to 92% for the 60 m HyspIRI simulated data relative to 15 m spatial resolution data. Classification accuracy was lower for similar minerals and smaller areas, which were not OPEN ACCESSRemote Sens. 2011, 3 1585 mapped well by the simulated 60 m HyspIRI data due to blending of similar signatures and spectral mixing with adjacent pixels. The simulations demonstrate that HyspIRI SWIR data, while somewhat limited by their relatively coarse spatial resolution, should still be useful for mapping hydrothermal/geothermal systems, and for many other geologic applications requiring mineral mapping.

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“…On August 1, 2003, a Hyperion image (level 1 radiometric product) covering a part of the island from north to south was acquired. Hyperion is the first spaceborne hyperspectral instrument to acquire both visible near infrared (VNIR) (400-1000 nm) and shortwave infrared (900-2500 nm) spectra 39 . The image exhibits 30 m spatial resolution and comprises a total of 242 bands in the aforementioned range of the electromagnetic spectrum, out of which 198 are the useful ones, whereas the rest of them contain no data.…”

Section: Application In Hyperspectral Remote Sensing Classificationmentioning

confidence: 99%

Reducing the Complexity of Genetic Fuzzy Classifiers in Highly-Dimensional Classification Problems

Stavrakoudis¹,

Galidaki²,

Gitas³

et al. 2012

IJCIS

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This paper introduces the Fast Iterative Rule-based Linguistic Classifier (FaIRLiC), a Genetic Fuzzy Rule-Based Classification System (GFRBCS) which targets at reducing the structural complexity of the resulting rule base, as well as its learning algorithm's computational requirements, especially when dealing with high-dimensional feature spaces. The proposed methodology follows the principles of the iterative rule learning (IRL) approach, whereby a rule extraction algorithm (REA) is invoked in an iterative fashion, producing one fuzzy rule at a time. The REA is performed in two successive steps: the first one selects the relevant features of the currently extracted rule, whereas the second one decides the antecedent part of the fuzzy rule, using the previously selected subset of features. The performance of the classifier is finally optimized through a genetic tuning post-processing stage. Comparative results in a hyperspectral remote sensing classification as well as in 12 real-world classification datasets indicate the effectiveness of the proposed methodology in generating high-performing and compact fuzzy rule-based classifiers, even for very high-dimensional feature spaces.

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Overview of the Earth Observing One (EO-1) Mission

Cited by 68 publications

References 14 publications

The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation

The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation

Effect of Reduced Spatial Resolution on Mineral Mapping Using Imaging Spectrometry—Examples Using Hyperspectral Infrared Imager (HyspIRI)-Simulated Data

Reducing the Complexity of Genetic Fuzzy Classifiers in Highly-Dimensional Classification Problems

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