Lithologic classification using multilevel spectral characteristics

Zhou, Guoqing; Wang, Haoyu; Sun, Yue; Shao, Yun; Yue, Tao

doi:10.1117/1.jrs.13.016513

Cited by 9 publications

(3 citation statements)

References 20 publications

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“…In the aftermath of the data gaps in Landsat imagery caused by ETM+ scan line corrector failures in June 2003 [71], ASTER (Advanced Spaceborne Thermal Emission Reflection Radiometer) has emerged as a reliable substitute for TIR imagery. ASTER comprises a visible and near-infrared subsystem, a shortwave infrared radiometer, and a TIR radiometer [72].…”

Section: Optical Imagerymentioning

confidence: 99%

Remote Sensing for Lithology Mapping in Vegetation-Covered Regions: Methods, Challenges, and Opportunities

Chen,

Wang,

Zhang

et al. 2023

Minerals

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Remote sensing (RS) technology has significantly contributed to geological exploration and mineral resource assessment. However, its effective application in vegetated areas encounters various challenges. This paper aims to provide a comprehensive overview of the challenges and opportunities associated with RS-based lithological identification in vegetated regions which includes the extensively reviewed prior research concerning the identification of lithology in vegetated regions, encompassing the utilized remote sensing data sources, and classification methodologies. Moreover, it offers a comprehensive overview of the application of remote sensing techniques in the domain of lithological mapping. Notably, hyperspectral RS and Synthetic Aperture Radar (SAR) have emerged as prominent tools in lithological identification. In addition, this paper addresses the limitations inherent in RS technology, including issues related to vegetation cover and terrain effects, which significantly impact the accuracy of lithological mapping. To propel further advancements in the field, the paper proposes promising avenues for future research and development. These include the integration of multi-source data to improve classification accuracy and the exploration of novel RS techniques and algorithms. In summary, this paper presents valuable insights and recommendations for advancing the study of RS-based lithological identification in vegetated areas.

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Section: Optical Imagerymentioning

confidence: 99%

Remote Sensing for Lithology Mapping in Vegetation-Covered Regions: Methods, Challenges, and Opportunities

Chen,

Wang,

Zhang

et al. 2023

Minerals

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“…Segundo Ou e Sun (2019), o algoritmo necessita ser treinado para poder distinguir as classes uma das outras. Então, para saber se a classificação teve uma boa precisão, as classes resultantes devem representar as categorias dos dados que o analista identificou originalmente (ZHOU et al, 2019).…”

Section: Classificação Supervisionada De Imagens De Satéliteunclassified

Detalhamento dos processamentos (georreferenciamento, classificação supervisionada, não supervisionada e fusão) em imagens geradas pelos sensores de diferentes satélites para o município de Santa Rosa-RS

Baratto

2020

R. bras. Geom.

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O Sensoriamento Remoto é a ciência e arte de obter informações sobre um objeto, tal ciência simplifica o processo de aquisição de dados espaciais, pois evita levantamentos planialtimétricos in loco e, possibilita explorar áreas de difícil acesso. Nesse sentido, a difusão de conhecimentos e técnicas de processamento sobre imagens geradas por sensores de satélites, ganha espaço no meio acadêmico e profissional. Para explanar tais conhecimentos foi necessário escolher um local para realizar os processamentos das imagens, nesse contexto, foi escolhido o município de Santa Rosa-RS, por ser conhecido nacionalmente como o berço da soja. Sobre as imagens do município de Santa Rosa foram realizados os processos de georreferenciamento, classificação supervisionada e não supervisionada e, fusão, das imagens de diferentes sensores de satélites, como: Landsat 8, Landsat 5, Sentinel-2, ResourceSat-2 e CBERS- 4. Este trabalho teve como objetivo ensinar a sequência computacional dos principais processamentos sobre as imagens dos sensores de satélites, frequentemente realizados em Sensoriamento Remoto, porém, com um nível de detalhamento maior do que os demais tutoriais encontrados na literatura e, até mesmo nos próprios suportes dos softwares. Conforme os resultados obtidos, é possível verificar que este trabalho de fato, ensina minuciosamente os procedimentos supracitados. Ou seja, o trabalho permite ser replicado sem maiores dificuldades.

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“…e identification and classification of rocks and minerals have been the focus in many studies either by using their spectral characteristics [30] or by various spectral processing methods such as spectral angle mapper (SAM) [10,[31][32][33], support vector machines (SVM) [32,34], and principal component analysis (PCA) [35]. Recent studies found an overall classification accuracy of 66% based on the spectral data of various rocks [36] and an accuracy of 67.4% and 69.7% based on SAM and spectral information divergence (SID), respectively [37]. In addition, using a multilayer perceptron (MLP) and a convolutional neural network (CNN) applied on SWIR reflectance spectra, it is possible to identify alteration minerals with a test accuracy of 97.8% [38].…”

Section: Introductionmentioning

confidence: 99%

Quantification of the Spectral Variability of Ore-Bearing Granodiorite under Supervised and Semisupervised Conditions: An Upscaling Approach

Ogen

Denk

Gläßer

et al. 2021

Journal of Spectroscopy

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Reflectance spectroscopy is a nondestructive, rapid, and easy-to-use technique which can be used to assess the composition of rocks qualitatively or quantitatively. Although it is a powerful tool, it has its limitations especially when it comes to measurements of rocks with a phaneritic texture. The external variability is reflected only in spectroscopy and not in the chemical-mineralogical measurements that are performed on crushed rock in certified laboratories. Hence, the spectral variability of the surface of an uncrushed rock will, in most cases, be higher than the internal chemical-mineralogical variability, which may impair statistical models built on field measurements. For this reason, studying ore-bearing rocks and evaluating their spectral variability in different scales is an important procedure to better understand the factors that may influence the qualitative and quantitative analysis of the rocks. The objectives are to quantify the spectral variability of three types of altered granodiorite using well-established statistical methods with an upscaling approach. With this approach, the samples were measured in the laboratory under supervised ambient conditions and in the field under semisupervised conditions. This study further aims to conclude which statistical method provides the best practical and accurate classification for use in future studies. Our results showed that all statistical methods enable the separation of the rock types, although two types of rocks have exhibited almost identical spectra. Furthermore, the statistical methods that supplied the most significant results for classification purposes were principal component analysis combined with k-nearest neighbor with a classification accuracy for laboratory and field measurements of 68.1% and 100%, respectively.

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Lithologic classification using multilevel spectral characteristics

Cited by 9 publications

References 20 publications

Remote Sensing for Lithology Mapping in Vegetation-Covered Regions: Methods, Challenges, and Opportunities

Remote Sensing for Lithology Mapping in Vegetation-Covered Regions: Methods, Challenges, and Opportunities

Detalhamento dos processamentos (georreferenciamento, classificação supervisionada, não supervisionada e fusão) em imagens geradas pelos sensores de diferentes satélites para o município de Santa Rosa-RS

Quantification of the Spectral Variability of Ore-Bearing Granodiorite under Supervised and Semisupervised Conditions: An Upscaling Approach

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