Hyperspectral estimation of petroleum hydrocarbon content in soil using ensemble learning method and LASSO feature extraction

Wu, Ming‐Xue; Lin, Nan; Li, Genjun; Liu, Hanlin; Li, Delin

doi:10.1080/26395940.2022.2102543

Cited by 9 publications

(4 citation statements)

References 53 publications

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“…To improve efficiency and reduce cost, it is common practice to have a limited number of known soil samples for training learning models, and yet the insufficient sample size often leads to challenges in accurately acquiring the desired hypothesis [27]. In addition, most the actual target assumptions often reside outside the hypothesis space in applications of machine learning models, which complicates the model learning structure, increasing the computational complexity, and the performance in improving the accuracy and efficiency of the estimation model is moderate [28]. Numerous studies have shown that machine learning models usually suffer from high computational complexity and model overfitting problems [29,30].…”

Section: Introductionmentioning

confidence: 99%

Estimation and Mapping of Soil Organic Matter Content Using a Stacking Ensemble Learning Model Based on Hyperspectral Images

Wu,

Dou,

Lin

et al. 2023

Remote Sensing

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Fast and accurate SOM estimation and spatial mapping are significant for cultivated land planning and management, crop growth monitoring, and soil carbon pool estimation. It is a key problem to construct a fast and efficient estimation model based on hyperspectral remote sensing image data to realize the inversion mapping of SOM in large areas. In order to solve the problem that the estimation accuracy is not high due to the influence of hyperspectral image quality and soil sample quantity during the estimation model construction, this study explored a method for constructing an estimation model of SOM contents based on a new stacking ensemble learning algorithm and hyperspectral images. Surface soil samples in Huangzhong County of Qinghai Province were collected, and their ZY1-02D hyperspectral remote sensing images were investigated. As input data, a feature band dataset was constructed using the Pearson correlation coefficient and successive projections algorithm. Based on the dataset, a new SOM estimation model under the stacking ensemble learning framework combined with heterogeneous models was developed by optimizing the combination of base and meta-learners. Finally, the spatial distribution map of SOM was plotted based on the result of the model over the study area. The result suggested that the input data quality of the estimation model is improved by constructing a feature band dataset. The multi-class ensemble learning estimation model with the combination strategy of the base and meta-learners has better predictive effects and stability than the single-algorithm and single-level ensemble models with homogeneous learners. The coefficient of determination is 0.829, the residual prediction deviation is 2.85, and the predictive set root mean square error is 1.953. The results can provide new ideas for estimating SOM content using hyperspectral images and ensemble learning algorithms, and serve as a reference for mapping large-scale SOM spatial distribution using space-borne hyperspectral images.

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Section: Introductionmentioning

confidence: 99%

Estimation and Mapping of Soil Organic Matter Content Using a Stacking Ensemble Learning Model Based on Hyperspectral Images

Wu,

Dou,

Lin

et al. 2023

Remote Sensing

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“…ERT introduces greater randomness in node partitioning by selecting a subset of features randomly at each node during segmentation to ensure the difference between each decision tree. Therefore, the variance of the decision tree is reduced, and the generalization ability is improved [49].…”

Section: Extremely Randomized Tree (Ert)mentioning

confidence: 99%

Evaluating Feature Selection Methods and Machine Learning Algorithms for Mapping Mangrove Forests Using Optical and Synthetic Aperture Radar Data

Shen,

Miao,

Wang

et al. 2023

Remote Sensing

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Mangrove forests, mostly found in the intertidal zone, are among the highest-productivity ecosystems and have great ecological and economic value. The accurate mapping of mangrove forests is essential for the scientific management and restoration of mangrove ecosystems. However, it is still challenging to perform the rapid and accurate information mapping of mangrove forests due to the complexity of mangrove forests themselves and their environments. Utilizing multi-source remote sensing data is an effective approach to address this challenge. Feature extraction and selection, as well as the selection of classification models, are crucial for accurate mangrove mapping using multi-source remote sensing data. This study constructs multi-source feature sets based on optical (Sentinel-2) and SAR (synthetic aperture radar) (C-band: Sentinel-1; L-band: ALOS-2) remote sensing data, aiming to compare the impact of three feature selection methods (RFS, random forest; ERT, extremely randomized tree; MIC, maximal information coefficient) and four machine learning algorithms (DT, decision tree; RF, random forest; XGBoost, extreme gradient boosting; LightGBM, light gradient-boosting machine) on classification accuracy, identify sensitive feature variables that contribute to mangrove mapping, and formulate a classification framework for accurately recognizing mangrove forests. The experimental results demonstrated that using the feature combination selected via the ERT method could obtain higher accuracy with fewer features compared to other methods. Among the feature combinations, the visible bands, shortwave infrared bands, and the vegetation indices constructed from these bands contributed the greatest to the classification accuracy. The classification performance of optical data was significantly better than SAR data in terms of data sources. The combination of optical and SAR data could improve the accuracy of mangrove mapping to a certain extent (0.33% to 4.67%), which is essential for the research of mangrove mapping in a larger area. The XGBoost classification model performed optimally in mangrove mapping, with the highest overall accuracy of 95.00% among all the classification models. The results of the study show that combining optical and SAR remote sensing data with the ERT feature selection method and XGBoost classification model has great potential for accurate mangrove mapping at a regional scale, which is important for mangrove restoration and protection and provides a reliable database for mangrove scientific management.

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“…Thus, the development of a fast, convenient, and on-site method for detecting soil organic pollutants is beneficial for improving agricultural economic benefits and preserving soils for future generations. Three-dimensional fluorescence spectroscopy, 18 visible to near-infrared spectroscopy, 19 and hyperspectroscopy 20 are currently used for rapid, nondestructive quantitative detection of petroleum hydrocarbons in soil, but they are generally employed for detecting their components.…”

Section: Introductionmentioning

confidence: 99%

Rapid Detection of Total Petroleum Hydrocarbons in Soil Using Advanced Fluorescence Imaging Techniques

Shi,

Yang,

Zhao

et al. 2024

ACS Omega

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Hyperspectral estimation of petroleum hydrocarbon content in soil using ensemble learning method and LASSO feature extraction

Cited by 9 publications

References 53 publications

Estimation and Mapping of Soil Organic Matter Content Using a Stacking Ensemble Learning Model Based on Hyperspectral Images

Estimation and Mapping of Soil Organic Matter Content Using a Stacking Ensemble Learning Model Based on Hyperspectral Images

Evaluating Feature Selection Methods and Machine Learning Algorithms for Mapping Mangrove Forests Using Optical and Synthetic Aperture Radar Data

Rapid Detection of Total Petroleum Hydrocarbons in Soil Using Advanced Fluorescence Imaging Techniques

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