Comparison of airborne hyperspectral data and eo-1 hyperion for mineral mapping

Kruse, Fred A.; Boardman, Joseph W.; Huntington, J.F.

doi:10.1109/tgrs.2003.812908

Cited by 635 publications

(294 citation statements)

References 15 publications

(23 reference statements)

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“…Limitations to Hyperion data use include relatively low signal-to-noise ratios of approximately 50:1, compared with 500:1 for airborne hyperspectral imaging (Pearlman et al, 2003;Kruse et al, 2003) and atmospheric interference. Quantitative VNIR-SWIR mineral differentiation via Hyperion requires atmospheric correction as seen in the Lhotse Shar ice planetary reflectance spectral signature with prominent atmospheric effects, despite the removal of broad water vapor, oxygen and carbon dioxide absorption features (Fig.…”

Section: Hyperspectral Reflectancementioning

confidence: 99%

Geochemical characterization of supraglacial debris via in situ and optical remote sensing methods: a case study in Khumbu Himalaya, Nepal

2012

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Abstract. Surface glacier debris samples and field spectra were collected from the ablation zones of Nepal Himalaya Ngozumpa and Khumbu glaciers in November and December 2009. Geochemical and mineral compositions of supraglacial debris were determined by X-ray diffraction and X-ray fluorescence spectroscopy. This composition data was used as ground truth in evaluating field spectra and satellite supraglacial debris composition and mapping methods. Satellite remote sensing methods for characterizing glacial surface debris include visible to thermal infrared hyper-and multispectral reflectance and emission signature identification, semi-quantitative mineral abundance indicies and spectral image composites. Satellite derived supraglacial debris mineral maps displayed the predominance of layered silicates, hydroxyl-bearing and calcite minerals on Khumbu Himalayan glaciers. Supraglacial mineral maps compared with satellite thermal data revealed correlations between glacier surface composition and glacier surface temperature. Glacier velocity displacement fields and shortwave, thermal infrared false color composites indicated the magnitude of mass flux at glacier confluences. The supraglacial debris mapping methods presented in this study can be used on a broader scale to improve, supplement and potentially reduce errors associated with glacier debris radiative property, composition, areal extent and mass flux quantifications.

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Section: Hyperspectral Reflectancementioning

confidence: 99%

Geochemical characterization of supraglacial debris via in situ and optical remote sensing methods: a case study in Khumbu Himalaya, Nepal

2012

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“…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%

“…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. The relevance of having HSIs with a low level of noise or, equivalently, with a high signal-to-noise-ratio (SNR), to improve the accuracy of mineral prediction, food spectroscopy, and of various tasks such as classification and retrieving supported by spectral libraries has been pointed out, for example, in [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, EO-1 Hyperion data has been employed extensively on arid sites in Australia and South America, where it has been proven to perform really well for the location and characterization of minerals and alteration zones [5,9]. However, apart from these sites, whose HSIs are little affected by noise, Hyperion data has been less used, mainly because most of its images have low SNR [3].…”

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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“…The high-resolution spectral and spatial data from Hyperion yield repeatable images of high quality, which may allow accurate mineral mapping at a large scale. Excluding those hyperspectral bands that are uncalibrated or disturbed by vapor, there are 196 unique channels for the visible and near-infrared (VNIR) bands and 77-224 for the short-wave infrared (SWIR) bands) available from Hyperion [71][72][73]. Thus, the Hyperion hyperspectral bands covering 426-926 nm (bands 8-57) and 1942-2385 nm (bands 179-223) match the wavelengths of iron oxides and hydroxides within the domains of 400-900 nm and 1900-2400 nm.…”

Section: Discussionmentioning

confidence: 99%

Spectral properties of weathered and fresh rock surfaces in the Xiemisitai metallogenic belt, NW Xinjiang, China

Zhou

Wang

2017

Open Geosciences

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Surfaces weathering of rocks in which mineral materials may be similar to or quite different from the minerals in the underlying parent rock completely control the reflectance spectra of the terrain. Our study of typical weathered and fresh rock samples from the Xiemisitai metallogenic belt, Western Junggar region, Xinjiang, found that weathering results in the formation of new materials that cause differences in the spectral features of fresh and weathered rock surfaces. Alterations induce variations in spectrum brightness, presence and intensity of characteristic absorption features, and spectral slope. Spectral differences between weathered and fresh rock surfaces are small for rhyolite, granite, and tuffaceous sandstone, but large for andesite, basalt, and diorite. Spectral changes in the 350-1000 nm wavelength region are attributed to alteration of iron oxides by atmospheric processes or secondary alteration of iron-rich minerals. Spectral features between 1000-2500 nm are caused by O-H vibrations, with features at 2200-2500 nm solely attributed to hydroxyl groups. The strongest Al-OH bands appear near 2200 nm, while Mg-OH bands are found near 2300 nm and 2350 nm. Results from this study can be used to better characterize and discriminate lithological units and potential mineral zones using hyperspectral and multispectral remote sensing techniques.

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Comparison of airborne hyperspectral data and eo-1 hyperion for mineral mapping

Cited by 635 publications

References 15 publications

Geochemical characterization of supraglacial debris via in situ and optical remote sensing methods: a case study in Khumbu Himalaya, Nepal

Geochemical characterization of supraglacial debris via in situ and optical remote sensing methods: a case study in Khumbu Himalaya, Nepal

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

Spectral properties of weathered and fresh rock surfaces in the Xiemisitai metallogenic belt, NW Xinjiang, China

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