Lithology classification in semi-arid area combining multi-source remote sensing images using support vector machine optimized by improved particle swarm algorithm

Lu, Jiaxin; Liu, Han; Liu, Lei; Wang, Junfeng; Xia, Zhaode; Jin, Dingjian; Zha, Xinlin

doi:10.1016/j.jag.2023.103318

Cited by 9 publications

(4 citation statements)

References 37 publications

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“…The Classification and Regression Trees (CART) [78] selects training samples and their corresponding feature variables and classifies them by generating a classification rule tree through iterative binary splitting of the training samples. The Support Vector Machine (SVM) [79] delivers effective classification results even from intricate and noisy data. It originates from statistical learning theory, which uses decision trees to segregate classes, maximizing the margin between them.…”

Section: Classification Methodsmentioning

confidence: 99%

Eucalyptus Plantation Area Extraction Based on SLPSO-RFE Feature Selection and Multi-Temporal Sentinel-1/2 Data

Lin,

Ren,

et al. 2023

Forests

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An accurate and efficient estimation of eucalyptus plantation areas is of paramount significance for forestry resource management and ecological environment monitoring. Currently, combining multidimensional optical and SAR images with machine learning has become an important method for eucalyptus plantation classification, but there are still some challenges in feature selection. This study proposes a feature selection method that combines multi-temporal Sentinel-1 and Sentinel-2 data with SLPSO (social learning particle swarm optimization) and RFE (Recursive Feature Elimination), which reduces the impact of information redundancy and improves classification accuracy. Specifically, this paper first fuses multi-temporal Sentinel-1 and Sentinel-2 data, and then carries out feature selection by combining SLPSO and RFE to mitigate the effects of information redundancy. Next, based on features such as the spectrum, red-edge indices, texture characteristics, vegetation indices, and backscatter coefficients, the study employs the Simple Non-Iterative Clustering (SNIC) object-oriented method and three different types of machine-learning models: Random Forest (RF), Classification and Regression Trees (CART), and Support Vector Machines (SVM) for the extraction of eucalyptus plantation areas. Each model uses a supervised-learning method, with labeled training data guiding the classification of eucalyptus plantation regions. Lastly, to validate the efficacy of selecting multi-temporal data and the performance of the SLPSO–RFE model in classification, a comparative analysis is undertaken against the classification results derived from single-temporal data and the ReliefF–RFE feature selection scheme. The findings reveal that employing SLPSO–RFE for feature selection significantly elevates the classification precision of eucalyptus plantations across all three classifiers. The overall accuracy rates were noted at 95.48% for SVM, 96% for CART, and 97.97% for RF. When contrasted with classification outcomes from multi-temporal data and ReliefF–RFE, the overall accuracy for the trio of models saw an increase of 10%, 8%, and 8.54%, respectively. The accuracy enhancement was even more pronounced when juxtaposed with results from single-temporal data and ReliefF-RFE, at increments of 15.25%, 13.58%, and 14.54% respectively. The insights from this research carry profound theoretical implications and practical applications, particularly in identifying and extracting eucalyptus plantations leveraging multi-temporal data and feature selection.

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Section: Classification Methodsmentioning

confidence: 99%

Eucalyptus Plantation Area Extraction Based on SLPSO-RFE Feature Selection and Multi-Temporal Sentinel-1/2 Data

Lin,

Ren,

et al. 2023

Forests

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“…Here, it is particularly important to mention that China's Gaofen, Huanjing, and Ziyuan series satellites are notable sources of surface imagery data, offering spatial resolutions ranging from sub-meters to hundreds of meters. GF-1 [40,85], GF-2 [86], GF-3 [86], GF-5 [41,87], HJ-1A CCD [53], ZY-1 02D [38], and ZY-3 [88] are widely utilized for lithological mapping, and their detailed specifications can be found in Table 4. Among them, GF-1, GF-2, HJ-1A CCD, and ZY-3 loaded multispectral scanner, GF-5 and ZY-1 02D loaded hyperspectral scanner, and GF-3 loaded SAR camera.…”

Section: High-resolution Satellite Sources From Chinamentioning

confidence: 99%

“…However, it is crucial to consider that acquiring this data involves financial costs and each satellite has its own strengths and limitations. For example, GF-2 excels in areas such as high spatial resolution, multiple spectral bands, and frequent revisit periods, but it has limitations in terms of coverage and relatively lower radiometric resolution, which may constrain certain fine-scale analysis applications [86]. Conversely, GF-3 offers benefits like multiple polarization modes and frequent revisit periods, but it has lower spatial resolution and a restricted number of spectral bands [86].…”

Section: High-resolution Satellite Sources From Chinamentioning

confidence: 99%

“…SVM, combined with Sentinel-1, ALOS PALSAR, Landsat OLI, ASTER, and ALI data achieved a classification accuracy exceeding 85% in densely vegetated regions of southern Tunisia [45]. In Duolun County, Inner Mongolia Autonomous Region, China, GF-2, Sentinel-2A, ASTER, and GF-3 RS data, along with a particle swarm optimization (PSO)-based SVM classifier, led to a lithological classification accuracy of 90.90% [86].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

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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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A depth graph attention-based multi-channel transfer learning network for fluid classification from logging data

Li,

Qiao,

Sun

2024

Physics of Fluids

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Fluid classification is a fundamental task in the field of geological sciences to achieve effective reservoir characterization and hydrocarbon exploration. Traditional fluid classification methods are often limited by long processing times and an inability to capture complex relationships within the data. To address this issue, this paper proposes a novel deep learning approach—the Deep Graph Attention Multi-channel Transfer Learning Network (DGMT), aimed at improving the efficiency and accuracy of fluid classification from logging data. This model comprises three key components: a graph attention layer, a multi-channel feature extractor, and a transfer learning module. The graph attention layer is designed to handle spatial dependencies between different logging channels, enhancing classification accuracy by focusing on critical features. The multi-channel feature extractor integrates information from various data sources, ensuring comprehensive utilization of the rich information in logging data. The transfer learning module allows the model to transfer knowledge from pre-trained models of similar tasks, accelerating the training process and significantly improving the model's generalization ability and robustness. This feature enables the DGMT model to adapt to different geological environments and logging conditions, showing superior performance over traditional methods. To validate the effectiveness of the DGMT model, we conducted experiments on actual logging datasets containing multiple oil wells. The experimental results indicate that, compared to common machine learning algorithms and other deep learning methods, the DGMT model significantly improves in accuracy and other classification performance metrics.

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Lithology classification in semi-arid area combining multi-source remote sensing images using support vector machine optimized by improved particle swarm algorithm

Cited by 9 publications

References 37 publications

Eucalyptus Plantation Area Extraction Based on SLPSO-RFE Feature Selection and Multi-Temporal Sentinel-1/2 Data

Eucalyptus Plantation Area Extraction Based on SLPSO-RFE Feature Selection and Multi-Temporal Sentinel-1/2 Data

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

A depth graph attention-based multi-channel transfer learning network for fluid classification from logging data

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