Hyperspectral estimation of soil composition contents based on kernel principal component analysis and machine learning model

Nie, Lin; Liu, Haiqi; Yang, Jiajia; Liu, Hanlin

doi:10.1117/1.jrs.14.034507

Cited by 4 publications

(2 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although KPCA was selected 12 times as the best dimension reduction techniques, it possessed the worst results 54 times per 100 repetitions and then the advantages of using kernel was not confirmed in this study. (Lin et al 2020) reported that the machine learning models combined with KPCA were effective for estimating soil composition contents, however, the advantages of KPCA were not confirmed for chlorophyll content estimation from Zizania Latifolia. The clear relationships between chlorophyll content and reflectance over green peak and rededge have been used for estimating chlorophyll content from reflectance (Sims and Gamon 2002) and then it was not effective to express their relationships using non-liner models.…”

Section: Discussionmentioning

confidence: 99%

Estimating chlorophyll content of Zizania latifolia with hyperspectral data and random forest

Nofrizal

Sonobe

Yamashita

et al. 2022

International Journal of Engineering and Geosciences

View full text Add to dashboard Cite

The amount of chlorophyll in a plant useful to indicate its physiological activity and then changes in chlorophyll content have been used as a good indicator of disease as well as nutritional and environmental stresses on plants. Chlorophyll content estimation is one of the most applications of hyperspectral remote sensing data. The aim of this study is to evaluate dimensionality reduction for estimating chlorophyll contents from hyperspectral reflectance. Random Forest (RF) has been applied to assess biochemical properties such as chlorophyll content from remote sensing data; however, an approach integrating with dimensionality reduction techniques has not been fully evaluated. A total of 200 Zizania latifolia leaves with 5 treatments from Shizuoka University field were measured for reflectance and chlorophyll content. then, the regression models were generated based on RF with three dimensionality reduction methods including principal component analysis, kernel principal component analysis and independent component analysis. This research clarified that PCA is the best method for dimensionality reduction for estimating chlorophyll content in Zizania Latifolia with a RMSE value of 5.65 ± 0.58 μg cm -2 .

show abstract

Section: Discussionmentioning

confidence: 99%

Estimating chlorophyll content of Zizania latifolia with hyperspectral data and random forest

Nofrizal

Sonobe

Yamashita

et al. 2022

International Journal of Engineering and Geosciences

View full text Add to dashboard Cite

show abstract

“…In this study, 1, 2, 3, and 4 were used to indicate the degree of possible coal-gas outburst risk levels in the mine, where 1, 2, 3, and 4 indicate that no coal-gas outburst will occur (0 m 3 /t), the risk of coal-gas outburst disasters is small (0-5 m 3 /t), coal-gas outburst disasters will generally occur (5-10 m 3 /t), and serious coal-gas outburst disasters will occur (10 m 3 /t or more), respectively. KPCA [36,37] is mainly used to address nonlinear complex problems. It essentially employs a nonlinear function with extremely high nonlinear-processing capability, through which the introduced function maps a large amount of complex information in the original space to linearly divisible high-dimensional data.…”

Section: Selection Of Mutagenic Factorsmentioning

confidence: 99%

Risk Prediction of Coal and Gas Outburst in Deep Coal Mines Based on the SAPSO-ELM Algorithm

Yang

Fang

Wang

et al. 2022

IJERPH

View full text Add to dashboard Cite

Effective risk prevention and management in deep coal mines can reduce the occurrences of outburst accidents and casualties. To address the low accuracy and inefficiency of coal–gas outburst prediction in deep coal mines, this study proposes a deep coal–gas outburst risk prediction method based on kernal principal component analysis (KPCA) and an improved extreme learning machine (SAPSO-ELM) algorithm. Firstly, high-dimensional nonlinear raw data were processed by KPCA. Secondly, the extracted sequence of outburst-causing indicator principal components were used as the input variables for the simulated annealing particle swarm algorithm (SAPSO), which was proposed to optimize the input layer weights and implied layer thresholds of the ELM. Finally, a coal and gas outburst risk prediction model for a deep coal mine based on the SAPSO-ELM algorithm was developed. The research results show that, compared with the ELM and PSO-ELM algorithms, the SAPSO-ELM optimization algorithm significantly improved the accuracy of risk prediction for coal–gas outbursts in deep coal mines, and the accuracy rate was as high as 100%. This study enriches the theory and methods of safety management in deep coal mines, and effectively helps coal mine enterprises in improving their ability to manage coal–gas outburst risks.

show abstract

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

Lin

et al. 2022

Environmental Pollutants and Bioavailability

View full text Add to dashboard Cite

Hyperspectral estimation of soil composition contents based on kernel principal component analysis and machine learning model

Cited by 4 publications

References 38 publications

Estimating chlorophyll content of Zizania latifolia with hyperspectral data and random forest

Estimating chlorophyll content of Zizania latifolia with hyperspectral data and random forest

Risk Prediction of Coal and Gas Outburst in Deep Coal Mines Based on the SAPSO-ELM Algorithm

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

Contact Info

Product

Resources

About