Expert system-based mineral mapping in northern death valley, California/Nevada, using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)

Kruse, Fred A.; Lefkoff, A. B.; Dietz, John B.

doi:10.1016/0034-4257(93)90024-r

Cited by 221 publications

(133 citation statements)

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“…The potential of airborne and spaceborne imaging spectrometers has long been exploited to monitor the Earth's surface and atmosphere and to provide valuable information for the better understanding of a large number of environmental processes (e.g., [3,4]). Those applications include, for example, vegetation monitoring and ecology (e.g., [5][6][7][8][9][10][11]), geology and soils (e.g., [12][13][14][15][16][17]), coastal and inland waters (e.g., [18][19][20][21]), mapping of snow properties [22,23] and archaeological prospection [24,25].…”

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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“…Among the 18 minerals presented in this area, three of them (Alunite, Chalcedony, and Kaolinite) are selected because their outcrops can be spatially and spectrally clearly identified with both high SNR AVIRIS and low SNR Hyperion HSI data simultaneously. In hyperspectral geological mapping and exploration, minerals can be more accurately identified with high SNR HSI data [4]. Therefore, the mapping accuracy of the three minerals provides evidence of the quality of the noise-reduced HSI data, and can be considered as an indicator of denoising performance.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…. , e p ] ∈ R n b ×p is obtained by solving Equation (4). As already mentioned, the p rows of Z are termed eigen-images.…”

Section: Eigen-image Filtering Exploiting Self-similaritymentioning

confidence: 99%

“…Research work [3] highlights the advantages of using hyperspectral data to identify alteration minerals and defines their zonation. The work [4] uses a knowledge-based expert system to produce mineral maps from AVIRIS HSI data. Kruse et al [5] compare Hyperion HSI data with airborne AVIRIS HSI data to show that Hyperion HSI data provides the ability to remotely map basic surface mineralogy.…”

Section: Introductionmentioning

confidence: 99%

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A New Low-Rank Representation Based Hyperspectral Image Denoising Method for Mineral Mapping

Gao

Yao

et al. 2017

Remote Sensing

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Abstract:Hyperspectral imaging technology has been used for geological analysis for many years wherein mineral mapping is the dominant application for hyperspectral images (HSIs). The very high spectral resolution of HSIs enables the identification and the diagnosis of different minerals with detection accuracy far beyond that offered by multispectral images. However, HSIs are inevitably corrupted by noise during acquisition and transmission processes. The presence of noise may significantly degrade the quality of the extracted mineral information. In order to improve the accuracy of mineral mapping, denoising is a crucial pre-processing task. By leveraging on low-rank and self-similarity properties of HSIs, this paper proposes a state-of-the-art HSI denoising algorithm that implements two main steps: (1) signal subspace learning via fine-tuned Robust Principle Component Analysis (RPCA); and (2) denoising the images associated with the representation coefficients, with respect to an orthogonal subspace basis, using BM3D, a self-similarity based state-of-the-art denoising algorithm. Accordingly, the proposed algorithm is named Hyperspectral Denoising via Robust principle component analysis and Self-similarity (HyDRoS), which can be considered as a supervised version of FastHyDe. The effectiveness of HyDRoS is evaluated in a series of mineral mapping experiments using noise-reduced AVIRIS and Hyperion HSIs. In these experiments, the proposed denoiser yielded systematically state-of-the-art performance.

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“…There has been great success in using hyperspectral data to characterize mineral provinces (F.A. Kruse, 1988Kruse, , 1990Kruse, , 1993J.W. Boardman and J.F.…”

Section: O Introductionmentioning

confidence: 99%

Geobotanical characterization of a geothermal system using hyperspectral imagery: Long Valley Caldera, CA

Carter¹,

Cochran²,

Martini³

et al. 1998

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We have analyzed hyperspectral Airborne Visible-Infrared Imaging System (AVIRIS) imagery taken in September of 1992 in Long VaJley Caldera, CA, a geothermally active region expressed surficially by hot springs and fumaroles. Geological and vegetation mapping are attempted through spectral classification of imagery. Particular hot spring areas in the caldera are targeted for analysis. The data is analyzed for unique geobotanical patterns in the vicinity of hot springs as well as gross identification of dominant plant and mineral species. Spectra used for the classifications come from a vegetation spectral library created for plant species found to be associated with geothermal processes. This library takes into account the seasonality of vegetation by including spectra for species on a monthly basis. Geological spectra are taken from JPL and USGS mineral libraries. Preliminary classifications of hot spring areas indicate some success in mineral identification and less successful vegetation species identification. The small spatial extent of individual plants demands either sub-pixel analysis or increased spatial resolution of imagery. Future work will also include preliminary analysis of a hyperspectral thermal imagery dataset and a multitemporal air photo dataset. The combination of these remotely sensed datasets for Long Valley will yield a valuable product for geothermal exploration efforts in other regions. .O INTRODUCTIONActive geothermal regions can be characterized by a specific suite of minerals and vegetation. Processes occurring in such environments facilitate the unique interaction between the biological and geological world resulting in predictable patterns. In general, geothermal processes include fumarolic activity, increased geothermal gradients, and discharge of hot water both at the surface and at depth. The interaction of these geothermal processes with the in situ geology results in alteration of the rock to characteristic hydrothermal mineral assemblages. The further interaction of both the ongoing and sometimes cyclic geothermal processes and the mineral assemblages with the vegetation results in patterns dictated by a plant's growth threshold. These thresholds are constrained partly by temperature and pH. One way to study these patterns is through the use of hyperspectral imagery such as that provided by the AVIRIS instrument. There has been great success in using hyperspectral data to characterize mineral provinces (F.A.Kruse, 1988(F.A.Kruse, , 1990(F.A.Kruse, , 1993J.W. Boardman and J.F. Huntington, 1996). Less work has been done with vegetation due to the complex nature of delineating different species of plants which are chemically very similar to one another. However, airborne hyperspectral data has also been successfully used to characterize vegetation communities and condition (R. Merton, 1998;S.M. de Jong, 1996, R.N. Clark et al, 1995. We attempt a geobotanical approach aimed at analysis of hyperspectral imagery of a geothermal region in Long Valley Caldera, California. A s...

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Expert system-based mineral mapping in northern death valley, California/Nevada, using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)

Cited by 221 publications

References 15 publications

The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation

The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation

A New Low-Rank Representation Based Hyperspectral Image Denoising Method for Mineral Mapping

Geobotanical characterization of a geothermal system using hyperspectral imagery: Long Valley Caldera, CA

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