Comparison Between Classification Algorithms: Gaussian Mixture Model - GMM and Random Forest - RF, for Landsat 8 Images

Pantoja, Daniel Assunção; Spenassato, Débora; Emmendorfer, Leonardo Ramos

doi:10.24857/rgsa.v16n3-015

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…However, it is advisable to undergo network training for improvement in efficiency and greater spatial accuracy of plant species. Corroborating our findings, Pantoja et al [52] compared the Random Forest algorithm with GMM, indicating that Random Forest outperformed with the following Kappa index: Random Forest (K = 0.94) and Gaussian Mixture Model (K = 0.85).…”

Section: Discussionsupporting

confidence: 85%

UAV-Based Classification of Intercropped Forage Cactus: A Comparison of RGB and Multispectral Sample Spaces Using Machine Learning in an Irrigated Area

Andrade,

Montenegro,

Silva Neto

et al. 2024

AgriEngineering

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Precision agriculture requires accurate methods for classifying crops and soil cover in agricultural production areas. The study aims to evaluate three machine learning-based classifiers to identify intercropped forage cactus cultivation in irrigated areas using Unmanned Aerial Vehicles (UAV). It conducted a comparative analysis between multispectral and visible Red-Green-Blue (RGB) sampling, followed by the efficiency analysis of Gaussian Mixture Model (GMM), K-Nearest Neighbors (KNN), and Random Forest (RF) algorithms. The classification targets included exposed soil, mulching soil cover, developed and undeveloped forage cactus, moringa, and gliricidia in the Brazilian semiarid. The results indicated that the KNN and RF algorithms outperformed other methods, showing no significant differences according to the kappa index for both Multispectral and RGB sample spaces. In contrast, the GMM showed lower performance, with kappa index values of 0.82 and 0.78, compared to RF 0.86 and 0.82, and KNN 0.86 and 0.82. The KNN and RF algorithms performed well, with individual accuracy rates above 85% for both sample spaces. Overall, the KNN algorithm demonstrated superiority for the RGB sample space, whereas the RF algorithm excelled for the multispectral sample space. Even with the better performance of multispectral images, machine learning algorithms applied to RGB samples produced promising results for crop classification.

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Section: Discussionsupporting

confidence: 85%

UAV-Based Classification of Intercropped Forage Cactus: A Comparison of RGB and Multispectral Sample Spaces Using Machine Learning in an Irrigated Area

Andrade,

Montenegro,

Silva Neto

et al. 2024

AgriEngineering

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Surrogate Modelling of an Industrial Distillation Column Obtained from Statistical Techniques and Machine Learning

Leite,

Silva,

Fernandes

et al. 2023

RGSA

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Purpose: To study a case of modeling an industrial distillation system, using the Aspen Plus simulator as the mathematical model and subsequently generating surrogate models using Machine Learning techniques in Matlab. Theoretical Framework: Metamodels are reduced models obtained from data generated by rigorous models, replacing them entirely or partially when the computational codes originating from them require excessively large computational effort to be used feasibly. Method/Design/Approach: The simulation of the process was performed in Aspen Plus. Subsequently, the most important variables were selected, and an experimental design was created using Latin Hypercube Sampling in Matlab, generating points to be used in sensitivity analysis in Aspen Plus. The next step was the construction of metamodels using the Statistics and Machine Learning toolbox in Matlab, employing linear regression and Gaussian process regression models. Finally, a statistical analysis was conducted. Results and Conclusion: The distillation system simulation converged, and the obtained metamodels had good regression indicators, especially the Gaussian process regression models, making them the most suitable for representing the rigorous Aspen Plus model. Research Implications: Understanding the multicomponent distillation process integrated with computational tools and data regression models, leading to a reduction in computational effort. Originality/Value: Development of a tool that enables the simulation and evaluation of a distillation process without the need to acquire software with rigorous equation databases.

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Hydrothermal Alteration Zones Mapping Using Sentinel 2 MSI and Terra Aster Data in The Uruguay Mine, Minas do Camaquã District

Novaes,

Souza,

Temporim

et al. 2024

RGSA

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Objective: This work has as main objective to evaluate the usage of data from the Terra ASTER multispectral sensor, as well as data from Sentinel 2 MSI for the identification of hydrothermal alteration zones in the Uruguay Mine. Another objective of this work was to map a not-so-great scale deposit that present a hydrothermal pattern associated in a tropical country. Method: Were applied principal component analysis and entropy analysis technique to the ASTER surface reflectance data and principal components only to the surface reflectance MSI data. The field spectra provided were obtained with the ASD FieldSpec-3 High-Resolution spectroradiometer. The samples collected contained spectral features characteristic of minerals such as ferric chlorite, hematite, goethite and illite (Phengite). Result and Conclusion: The ASTER data resulted in a good radial highlighted pattern at the mine location indicating that the processing was able to map the target spectral patterns. The MSI data were processed to map the ferric minerals and presented a pattern highlighting the mine contours and slopes, demonstrating the possible occurrence of minerals containing the aimed spectral features. The entropy usage as an intensity factor applied to intensity-hue-saturation (IHS) fusion on ASTER data improved the reliability of the results, by highlighting the pixels with deepest absorbance features, reducing the uncertainty associated with the process. The results were considered good since both processes showed correspondence in the location of the mine. Implication of the research: This work contributes for the understanding of spectroscopy analysis of alteration minerals related to copper mineralization, and techniques of remote sensing to map them. Originality/value: Good results of the application of principal component analysis in the studied area and application of entropy concept in spectral analysis.

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Comparison Between Classification Algorithms: Gaussian Mixture Model - GMM and Random Forest - RF, for Landsat 8 Images

Cited by 3 publications

References 15 publications

UAV-Based Classification of Intercropped Forage Cactus: A Comparison of RGB and Multispectral Sample Spaces Using Machine Learning in an Irrigated Area

UAV-Based Classification of Intercropped Forage Cactus: A Comparison of RGB and Multispectral Sample Spaces Using Machine Learning in an Irrigated Area

Surrogate Modelling of an Industrial Distillation Column Obtained from Statistical Techniques and Machine Learning

Hydrothermal Alteration Zones Mapping Using Sentinel 2 MSI and Terra Aster Data in The Uruguay Mine, Minas do Camaquã District

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