Full unmixing hydrothermal alteration minerals mapping by integration of pattern recognition network and directed matched filtering algorithm

Fereydooni, Hosein; Moradzadeh, Ali; Pahlavani, Parham; Mojeddifar, Saeed

doi:10.1007/s12145-019-00422-y

Cited by 8 publications

(4 citation statements)

References 30 publications

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“…Thus, the presence of minerals indicative of hydrothermal alteration exhibits diagnostic spectral absorption features. ASTER satellite imagery has been thoroughly used to discriminate hydrothermal alteration [2,[4][5][6][7][8][38][39][40][41][42][43][44], considering its wide spectral coverage in the VNIR and SWIR region.…”

Section: Endmember Selectionmentioning

confidence: 99%

Kaoline Mapping Using ASTER Satellite Imagery: The Case Study of Kefalos Peninsula, Kos Island

Kokkaliari

Kanellopoulos

Illiopoulos

2021

International Conference on Raw Materials and Circular Economy

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The present work aims to map kaolin occurrences on the Kefalos peninsula, SW Kos Island, Greece, through the elaboration of ASTER satellite imagery. The island of Kos is located on the eastern edge of the South Aegean Active Volcanic Arc (SAAVA) and is characterised by its complex geologic structure. During Plio-Pleistocene, the voluminous eruption of the Kos Plateau Tuff was recorded on Kefalos; the largest quaternary eruption in the Mediterranean. Kaolin is the product of hydrothermal alteration of the Pliocene volcanic rocks with rhyolitic composition. Our study emphasises the usefulness of satellite imagery combined with the Mixture Tuned Matched Filtering (MTMF) technique to detect occurrences of industrial minerals, kaolin-group minerals in this case, either in terms of raw mineral exploitation or by mapping hydrothermal alteration.

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Section: Endmember Selectionmentioning

confidence: 99%

Kaoline Mapping Using ASTER Satellite Imagery: The Case Study of Kefalos Peninsula, Kos Island

Kokkaliari

Kanellopoulos

Illiopoulos

2021

International Conference on Raw Materials and Circular Economy

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“…However, mineralogical alteration appears as weak anomalies in multispectral remote sensing images, often obscured by background surface features. The fundamental key to extracting alteration information and zones from remote sensing data has the following four aspects: (i) determining the unique spectral characteristics of detected mineral(s) in VNIR and SWIR regions [3,4]; (ii) eliminating the impact of vegetation, water bodies, and other interfering features [5]; (iii) performing the calculation and selection of band ratios (BR) and combinations for extracting different types of alteration information [6][7][8][9]; and (iv) utilizing alteration extraction methods for mapping alteration zones [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Selection of Landsat-8 Operational Land Imager (OLI) Optimal Band Combinations for Mapping Alteration Zones

Yang,

Jia,

Dong

et al. 2024

Remote Sensing

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In typical alteration extraction methods, e.g., band math and principal component analysis (PCA), the bands or band combinations unitized to extract altered minerals are usually selected based on empirical models or previous rules. This results in significant differences in the alteration of mineral mapping even in the same area, thus greatly increasing the uncertainty of mineral resource prediction. In this paper, an intelligent alteration extraction approach was proposed in which an optimization algorithm, i.e., a genetic algorithm (GA), was introduced into the PCA; this approach is termed GA-PCA and is used for selecting the optimized band combinations of mineralized alterations. The proposed GA-PCA was employed to map iron oxides and hydroxyl minerals using the most commonly adopted multispectral data, i.e., Landsat-8 OLI data, at the Lalingzaohuo polymetallic deposits, China. The results showed that the spectral characteristics of GA-PCA-selected OLI band combinations in the research area were beneficial for enhancing alteration information and were more capable of suppressing the interference of vegetation information. The mapping alteration zones using the GA-PCA approach had a higher agreement with known ore spots, i.e., 25% and 33.3% in ferrous-bearing and hydroxyl-bearing deposits, compared to the classical PCA. Furthermore, two predicted targets (not shown in the classical PCA results) were precisely obtained via analyzing the GA-PCA alteration maps combined with the ore-forming geological conditions of the mine and its tectonic characteristics. This indicated that the intelligent selection of mineral alteration band combinations increased the reliability of remote sensing-based mineral exploration.

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“…The validation showed that the DMF algorithm discriminated the altered mineral with higher accuracy. Fereydooni et al [13] improved the DMF procedure based on a pattern recognition network. The pattern recognition network used results of the DMF algorithm to measure the amount of kaolinite, muscovite, chlorite, and alunite pixels.…”

Section: Introductionmentioning

confidence: 99%

Identifying Hydrothermal Alterations Using Singularity Mapping of PCA Images Based on ASTER Data

Aragh

Mojeddifar

Chegeni

2020

Mining, Metallurgy & Exploration

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This work integrated selective principal component analysis (SPCA) with a singularity fractal model to map hydrothermal alterations of argillic, phyllic, and propylitic in the north-west of Kerman city in Iran. SPCA results were provided for short wave infrared (SWIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor to map phyllic and propylitic alterations. Also, the bands 5-7 were applied to map the argillic alteration zone. SPCA results could present useful information for alteration mapping but it does not show the purest pixels of alteration. Therefore, a fractal model of singularity was applied to highlight the different alteration pixels in the Kerman Cenozoic magmatic arc (KCMA). Thus, the singularity index of different hydrothermal alteration zones was produced. Comparing the obtained results with the field data showed that Kader, Serenu, Meiduk, and Abdar deposits were acceptably identified by alteration mapping. Also, it seems that the singularity index could not discriminate the hydrothermal alterations of argillic and phyllic. The same spectral signature of kaolinite and muscovite minerals is the main reason for misclassifications.

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Full unmixing hydrothermal alteration minerals mapping by integration of pattern recognition network and directed matched filtering algorithm

Cited by 8 publications

References 30 publications

Kaoline Mapping Using ASTER Satellite Imagery: The Case Study of Kefalos Peninsula, Kos Island

Kaoline Mapping Using ASTER Satellite Imagery: The Case Study of Kefalos Peninsula, Kos Island

Selection of Landsat-8 Operational Land Imager (OLI) Optimal Band Combinations for Mapping Alteration Zones

Identifying Hydrothermal Alterations Using Singularity Mapping of PCA Images Based on ASTER Data

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