Lithology classification in semi-arid areas based on vegetation suppression integrating microwave and optical remote sensing images: Duolun county, Inner Mongolia autonomous region, China

Jiaxin, Lu; Han, Ling; Zha, Xinlin; Li, Liangzhi

doi:10.1080/10106049.2022.2120639

Cited by 5 publications

(3 citation statements)

References 63 publications

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“…Methods based on spectral and SAR scattering features use features based on different mechanisms for complementary information to detect minerals. For instance, Lu et al 20 combined SAR texture, SAR backscatter, and spectral feature images to form seven feature combinations for lithology classification. Researchers used integrated remote sensing data and spatial analysis techniques to map lithological units.…”

Section: Introductionmentioning

confidence: 99%

Lithological classification and analysis based on random forest and multiple features: a case study in the Qulong copper deposit, China

Chen,

2023

J. Appl. Rem. Sens.

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.Surface cover diversity and the complexity of geological structures can seriously impact the accuracy of mineral mapping. To address this issue, we propose a method for lithological classification and analysis based on random forest (RF) and multiple features. Feature vectors, including spectral, polarization, texture, and terrain features, are constructed to provide multidimensional information. Subsequently, these feature vectors are screened based on their discriminative properties for different lithologies to reduce feature redundancy. Finally, the results of lithological classification can be obtained using the RF algorithm based on the selected features. In the experiments conducted in the Qulong copper deposit area, data from Sentinel-1A, Sentinel-2A, and Terra satellites were used to extract multidimensional features. After calculating the Bhattacharyya distance and analyzing the probability density distribution, 17 features selected were input into the RF classifier, achieving an accuracy of 88.83% in lithological classification. This represents a 7.5% improvement compared to exclusively relying on spectral features, and suggests that the proposed method of combining spectral, polarization, texture, and terrain features provides new possibilities for improving the accuracy of field lithological classification.

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Section: Introductionmentioning

confidence: 99%

Lithological classification and analysis based on random forest and multiple features: a case study in the Qulong copper deposit, China

Chen,

2023

J. Appl. Rem. Sens.

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“…Lithological classification information is important and basic for mineral resource exploration and geological disaster monitoring. Understanding the spatial distribution characteristics and variability of surface lithology is of great significance for regional geological mapping and mineral resource potential prediction in areas with high altitudes and poor transportation [1][2][3][4]. Traditional lithological mapping involves aerial photo examination and mapping based on interpretation keys, examination of the rocks in the field, rock sampling, and their examination in the laboratory.…”

Section: Introductionmentioning

confidence: 99%

Lithological Classification by Hyperspectral Images Based on a Two-Layer XGBoost Model, Combined with a Greedy Algorithm

Lin

Jiang

et al. 2023

Remote Sensing

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Lithology classification is important in mineral resource exploration, engineering geological exploration, and disaster monitoring. Traditional laboratory methods for the qualitative analysis of rocks are limited by sampling conditions and analytical techniques, resulting in high costs, low efficiency, and the inability to quickly obtain large-scale geological information. Hyperspectral remote sensing technology can classify and identify lithology using the spectral characteristics of rock, and is characterized by fast detection, large coverage area, and environmental friendliness, which provide the application potential for lithological mapping at a large regional scale. In this study, ZY1-02D hyperspectral images were used as data sources to construct a new two-layer extreme gradient boosting (XGBoost) lithology classification model based on the XGBoost decision tree and an improved greedy search algorithm. A total of 153 spectral bands of the preprocessed hyperspectral images were input into the first layer of the XGBoost model. Based on the tree traversal structural characteristics of the leaf nodes in the XGBoost model, three built-in XGBoost importance indexes were split and combined. The improved greedy search algorithm was used to extract the spectral band variables, which were imported into the second layer of the XGBoost model, and the bat algorithm was used to optimize the modeling parameters of XGBoost. The extraction model of rock classification information was constructed, and the classification map of regional surface rock types was drawn. Field verification was performed for the two-layer XGBoost rock classification model, and its accuracy and reliability were evaluated based on four indexes, namely, accuracy, precision, recall, and F1 score. The results showed that the two-layer XGBoost model had a good lithological classification effect, robustness, and adaptability to small sample datasets. Compared with the traditional machine learning model, the two-layer XGBoost model shows superior performance. The accuracy, precision, recall, and F1 score of the verification set were 0.8343, 0.8406, 0.8350, and 0.8157, respectively. The variable extraction ability of the constructed two-layer XGBoost model was significantly improved. Compared with traditional feature selection methods, the GREED-GFC method, when applied to the two-layer XGBoost model, contributes to more stable rock classification performance and higher lithology prediction accuracy, and the smallest number of extracted features. The lithological distribution information identified by the model was in good agreement with the lithology information verified in the field.

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“…In the realm of geological element interpretation using DL models, remarkable performance has been attained in completely exposed regions, while accuracy in other areas exhibits notably diminished precision. This phenomenon arises from the intricate geological scenarios in reality, where geological elements such as rock, soil, and water are susceptible to being concealed by vegetation and can undergo modifications due to geological activities [24]. The coverage of geographical environments adversely affects the observability of satellite remote sensing data, resulting in challenges such as object occlusion and the invisibility of geological element features.…”

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confidence: 99%

Improving Geological Remote Sensing Interpretation Via a Contextually Enhanced Multiscale Feature Fusion Network

He,

Zhang,

Dong

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Geological remote sensing interpretation plays a pivotal role in the field of regional geological mapping, encompassing the analysis of rock, soil, and water features. However, these geological elements can be obscured by the surrounding geographical environment and can undergo modifications caused by geological activities. The former hinders the effectiveness of satellite remote sensing data, resulting in the invisibility of element features, while the latter leads to the complex distribution of element features and significant spatial variations of geological elements. Consequently, existing deep learning-based models for interpreting geological elements often exhibit limited accuracy. To address these issues, this study proposes the Contextually Enhanced Multiscale Feature Fusion Network (CEMFFNet) for the efficient interpretation of geological elements. First, the context enhancement module is employed to extract abundant feature information and reinforce contextual features, aiming to capture essential features and strengthen their interconnections. Second, the multiscale feature fusion module incorporates the SimAM attention mechanism to adaptively learn features from different channels, emphasizing the feature information that contributes to interpretation results and maximizing the comprehensive and crucial feature information for each element. Extensive experiments demonstrate the superior performance of both the context enhancement module and the multiscale feature fusion module compared to several representative deep learning networks in terms of overall interpretation accuracy on two datasets. The model demonstrated improvements in oPA and mIoU of 2.4% and 2.8%, respectively, on the Landsat 8 dataset, and 3.5% and 3.2%, respectively, on the Sentinel-2 dataset.

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Lithology classification in semi-arid areas based on vegetation suppression integrating microwave and optical remote sensing images: Duolun county, Inner Mongolia autonomous region, China

Cited by 5 publications

References 63 publications

Lithological classification and analysis based on random forest and multiple features: a case study in the Qulong copper deposit, China

Lithological classification and analysis based on random forest and multiple features: a case study in the Qulong copper deposit, China

Lithological Classification by Hyperspectral Images Based on a Two-Layer XGBoost Model, Combined with a Greedy Algorithm

Improving Geological Remote Sensing Interpretation Via a Contextually Enhanced Multiscale Feature Fusion Network

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