Mineral mapping using spaceborne Tiangong‐1 hyperspectral imagery and ASTER data: A case study of alteration detection in support of regional geological survey at Jintanzi‐Malianquan area, Beishan, Gansu Province, China

Liu, Lei; Feng, Jiang; Han, Ling; Zhou, Jun; Xu, Xinliang; Liu, Rui

doi:10.1002/gj.3260

Cited by 12 publications

(4 citation statements)

References 28 publications

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“…A correlation was established between identified alteration zones and known gold–copper deposits in the study area. The study by Liu et al ( ) is an extension of previous work on mineral mapping using spaceborne Tiangong‐1 hyperspectral imagery and ASTER data over a much wider and geologically more complex area near Beishan, Gansu Province. The region has important Au–Cu–Ni–Cr belts.…”

Section: Volume 3: Structure and Main Themesmentioning

confidence: 99%

Tianshan Orogen along the Silk Road (Volume 3): Orogen links, geochemistry, geochronology, mineral deposits, and environments

Suo

et al. 2018

Geological Journal

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This special issue (Volume 3) belongs to the special issues on the orogens along the Silk Road. We selected 34 papers to focus on the mineral deposits, orogeny‐links, geochemistry, geochronology, and environments related to the Tianshan Orogen of the Silk Road. We hope that these papers will highlight more geological research along the Silk Road to promote the construction and development of the Road and Belt.

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Section: Volume 3: Structure and Main Themesmentioning

confidence: 99%

Tianshan Orogen along the Silk Road (Volume 3): Orogen links, geochemistry, geochronology, mineral deposits, and environments

Suo

et al. 2018

Geological Journal

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“…Two recent studies over sites in China described combined use of ASTER data with other, seldom-reported, remote sensing instruments. Liu et al [108] combined information from the Chinese hyperspectral scanner on the Tiangong-1 space station, with ASTER data for regional alteration mapping in the Jintanzi-Malianquan area. The hyperspectral instrument allowed detection of muscovite, kaolinite, chlorite, epidote calcite and dolomite, while ASTER data allowed detection of the first five, not dolomite.…”

Section: Alteration Mapping With Aster Data and Other Data Sourcesmentioning

confidence: 99%

Twenty Years of ASTER Contributions to Lithologic Mapping and Mineral Exploration

Abrams

Yamaguchi

2019

Remote Sensing

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The Advanced Spaceborne Thermal Emission and Reflection Radiometer is one of five instruments operating on the National Aeronautics and Space Administration (NASA) Terra platform. Launched in 1999, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) has been acquiring optical data for 20 years. ASTER is a joint project between Japan’s Ministry of Economy, Trade and Industry; and U.S. National Aeronautics and Space Administration. Numerous reports of geologic mapping and mineral exploration applications of ASTER data attest to the unique capabilities of the instrument. Until 2000, Landsat was the instrument of choice to provide surface composition information. Its scanners had two broadband short wave infrared (SWIR) bands and a single thermal infrared band. A single SWIR band amalgamated all diagnostic absorption features in the 2–2.5 micron wavelength region into a single band, providing no information on mineral composition. Clays, carbonates, and sulfates could only be detected as a single group. The single thermal infrared (TIR) band provided no information on silicate composition (felsic vs. mafic igneous rocks; quartz content of sedimentary rocks). Since 2000, all of these mineralogical distinctions, and more, could be accomplished due to ASTER’s unique, high spatial resolution multispectral bands: six in the SWIR and five in the TIR. The data have sufficient information to provide good results using the simplest techniques, like band ratios, or more sophisticated analyses, like machine learning. A robust archive of images facilitated use of the data for global exploration and mapping.

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“…Hyperspectral (HS) imaging is extensively used in earth observation satellites for various applications [1]: lithological mapping [2]- [6], environmental monitoring [7]- [9], and agricultural activities [10]- [12], since it captures more spectral information than its counterparts: multispectral and RGB imaging. However, HS images (HSIs) are of poor spatial resolution and are corrupted by noise and interference from atmospheric alterations and instrumentation imperfections [13].…”

Section: Introductionmentioning

confidence: 99%

GAUSS: Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness

Ranasinghe¹,

Weerasooriya²,

Godaliyadda³

et al. 2022

Preprint

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In recent hyperspectral unmixing (HU) literature, the application of deep learning (DL) has become more prominent, especially with the autoencoder (AE) architecture. We propose a split architecture and use a pseudo-ground truth for abundances to guide the 'unmixing network' (UN) optimization. Preceding the UN, an 'approximation network' (AN) is proposed, which will improve the association between the centre pixel and its neighbourhood. Hence, it will accentuate spatial correlation in the abundances as its output is the input to the UN and the reference for the 'mixing network' (MN). In the Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness (GAUSS), we proposed using one-hot encoded abundances as the pseudo-ground truth to guide the UN; computed using the k-means algorithm to exclude the use of prior HU methods. Furthermore, we release the single-layer constraint on MN by introducing the UN generated abundances in contrast to the standard AE for HU. Secondly, we experimented with two modifications on the pre-trained network using the GAUSS method. In GAUSS blind , we have concatenated the UN and the MN to back-propagate the reconstruction error gradients to the encoder. Then, in the GAUSSprime, abundance results of a signal processing (SP) method with reliable abundance results were used as the pseudo-ground truth with the GAUSSarchitecture. According to quantitative and graphical results for four experimental datasets, the three architectures either transcended or equated the performance of existing HU algorithms from both DL and SP domains.

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Mineral mapping using spaceborne Tiangong‐1 hyperspectral imagery and ASTER data: A case study of alteration detection in support of regional geological survey at Jintanzi‐Malianquan area, Beishan, Gansu Province, China

Cited by 12 publications

References 28 publications

Tianshan Orogen along the Silk Road (Volume 3): Orogen links, geochemistry, geochronology, mineral deposits, and environments

Tianshan Orogen along the Silk Road (Volume 3): Orogen links, geochemistry, geochronology, mineral deposits, and environments

Twenty Years of ASTER Contributions to Lithologic Mapping and Mineral Exploration

GAUSS: Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness

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