Alteration mapping using multispectral images: Cuprite mining district, Esmeralda County, Nevada

Ashley, Roger P.; Abrams, Michael J.

doi:10.3133/ofr80367

Cited by 51 publications

(96 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The study area is located in Cuprite in western Nevada, U.S.A. Cuprite is one of the most popular sites for testing the performance of new sensors and spectral mapping methods in geologic remote sensing studies (e.g., Abrams et al, 1977;Ashley and Abrams, 1980;Swayze et al, 2014). In this area, the Tertiary volcanic rocks were hydrothermally altered in the Midto Late-Miocene epoch.…”

Section: Study Area and Datamentioning

confidence: 99%

“…In this area, the Tertiary volcanic rocks were hydrothermally altered in the Midto Late-Miocene epoch. The hydrothermally altered rocks were divided into three groups: silicified rocks, opalized rocks and argillized rocks, as shown in Figure 1 (Ashley and Abrams, 1980).…”

Section: Study Area and Datamentioning

confidence: 99%

“…The spatial resolution is 2 to 10 m. Figure 1. Alteration map of Cuprite, Nevada (Hook and Rast, 1990) redrawn from Ashley and Abrams (1980).…”

Section: Study Area and Datamentioning

confidence: 99%

See 2 more Smart Citations

Geological Mapping by Combining Spectral Unmixing and Cluster Analysis for Hyperspectral Data

Ishidoshiro¹,

Yamaguchi²,

Noda³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Commission VIII, WG VIII/5 KEY WORDS: Geology, Mineral, Unmixing, Cluster Analysis, Hyperspectral, Cuprite ABSTRACT:Spectral unmixing of hyperspectral data often fails to select some minerals and rocks having flat spectra but no diagnostic absorption features as endmembers, even if they are actually important endmembers. To avoid this problem, we propose a novel approach that combined two methods: spectral unmixing and full-pixel classification. First, all pixels were divided into two categories, hydrothermally altered areas and unaltered rocks based on the absorption depth of 2.0 to 2.5 µm. For the hydrothermally altered areas, endmembers were extracted by the Improved Causal Random Pixel Purity Index (ICRPPI) method, which was improved from the existing Pixel Purity Index (PPI) and Causal Random Pixel Purity Index (CRPPI) methods. Endmember abundance in each pixel was calculated by linear spectral unmixing. In a separate operation, k-means clustering was applied to the unaltered rock areas. Finally, the results of these two methods were combined to generate a single distribution map of rocks and minerals. This approach was applied to the airborne hyperspectral HyMap data of Cuprite, Nevada, U.S.A. We confirmed that our mapping result was consistent with the existing geological map as well as our field survey result.

show abstract

Section: Study Area and Datamentioning

confidence: 99%

Section: Study Area and Datamentioning

confidence: 99%

See 1 more Smart Citation

Geological Mapping by Combining Spectral Unmixing and Cluster Analysis for Hyperspectral Data

Ishidoshiro¹,

Yamaguchi²,

Noda³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Cuprite, NV, USA, located approximately 200 km northwest of Las Vegas, is well understood mineralogically [5,36,37] and has been used as a geological remote sensing test site since the early 1980s [36,[38][39][40][41]. 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.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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

Gao

Yao

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

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.

show abstract

“…The site is ideal for remote sensing because it is relatively well known, there is little vegetation, there are a variety of minerals with sharp absorption features, and there are some areas of pure minerals. Materials at the surface consist primarily of volcanic rocks that have been hydrothermally altered (changed by hot water passing through the rocks) in a fossilized hot-springs environment [8,15]. Common hydrothermal alteration minerals present include silica (chalcedony), kaolinite, dickite, alunite, buddingtonite (an ammonium feldspar), muscovite, jarosite, and montmorillonite [8].…”

Section: Application Of Spectral Library Analysis Results To Airbornementioning

confidence: 99%

Spectral-feature-based analysis of reflectance and emission spectral libraries and imaging spectrometer data

Kruse

2012

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII

View full text Add to dashboard Cite

This research demonstrates the application of spectral-feature-based analysis to identifying and mapping Earth-surface materials using spectral libraries and imaging spectrometer data. Feature extraction utilizing a continuum-removal and local minimum detection approach was tested for analysis of both reflectance and emissivity spectral libraries by extracting and characterizing spectral features of rocks, soils, minerals, and man-made materials. Library-derived information was then used to illustrate both reflectance-and emissivity-feature-based spectral mapping using imaging spectrometer data (AVIRIS and SEBASS). An additional spectral library of emission spectra from selected nocturnal lighting types was used to develop a database of key spectral features that allowed mapping and characterization of night lights from ProSpecTIR-VS imaging spectrometer data. Results from these case histories demonstrate that the spectralfeature-based approach can be used with either reflectance or emission spectra and applied to a wide variety of imaging spectrometer data types for extraction of key surface composition information.

show abstract

Alteration mapping using multispectral images: Cuprite mining district, Esmeralda County, Nevada

Cited by 51 publications

References 1 publication

Geological Mapping by Combining Spectral Unmixing and Cluster Analysis for Hyperspectral Data

Geological Mapping by Combining Spectral Unmixing and Cluster Analysis for Hyperspectral Data

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

Spectral-feature-based analysis of reflectance and emission spectral libraries and imaging spectrometer data

Contact Info

Product

Resources

About