Implementation of free and open-source geotechnologies in a sanitation company

Aragão, Helder Guimarães; Alves, Vilmar Pereira; Filho, Valfrido Antônio Pereira

doi:10.56238/pacfdnsv1-088

Cited by 1 publication

(1 citation statement)

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“…In recent years, anthropogenic actions on the environment have generated significant negative impacts that compromise the conservation of this natural resource (ARAGÃO et al, 2022) In view of this, the assessment of the changes in the landscape is fundamental for efficiency in territorial management, since it can provide input for decisions related to the use and conservation of natural and environmental resources. To aid in understanding these changes, the landscape is commonly subdivided into cover and land use classes (MACEDO et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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

Pantoja¹,

Spenassato²,

Emmendorfer³

2023

RGSA

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Purpose: Given the importance of monitoring and managing land cover, especially in countries with continental proportions, such as Brazil. This research aimed to compare two remote sensing image classifier algorithms. Method/design/approach: The article compared the Gaussian Mixture Model and Random Forest classification algorithms, using Landsat 8 image, which was classified in a supervised way, in the Dezetsaka plugin of QGIS. The analysis of the performance of each model was performed using the Kappa index and Total Accuracy. Results and conclusion: The results showed that the Random Forest algorithm was more efficient than the Gaussian Mixture Model. Taking the Kappa Index (K) and Total Accuracy (po), the models obtained the following performances in the classification of classes: the Random Forest Model (K= 0.94 and po= 96.31) and the Gaussian Mixture Model obtained (K=0.85 and po=90.60). Research implications: The results can support the choice of classification method by researchers and others interested in monitoring land cover. Originality/value: This is a unique proposal, which compares an algorithm based on Machine Learning with another one from the category of probabilistic models. Interesting, since machine learning techniques have been gaining notoriety in several contexts.

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Section: Introductionmentioning

confidence: 99%