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

Abstract

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.