[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124883-en":3,"doc-seo-124883-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124883,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","A Performance Comparison of Three Machine Learning Algorithms for Urban Land Cover Classification using High Resolution Imagery - read online","Urban land cover classification using high-resolution imagery supports many applications requiring detailed, accurate land-cover products. Classification reliability depends on the chosen machine learning (ML) algorithm and its configuration. This study compares three major ML classifiers—Support Vector Machine (SVM), Naïve Bayes, and ensemble methods—on an urban high-resolution dataset, including multiple model variants for each family. Model performance is evaluated using confusion matrices and ROC curves. The Subspace Discriminant ensemble achieves the highest accuracy (85.1%), followed by Medium Gaussian SVM (84.5%) and Gaussian Naïve Bayes (81.5%).","A Performance Comparison of Three Machine Learning Algorithms for Urban Land Cover Classification using High  \nResolution Imagery  \n1Atijosan, Abimbola & 2 Muibi, Kolawole  \n1,2 COPINE, National Space Research and Development Agency, Obafemi Awolowo University Campus, Ile-Ife, Nigeria.  \n: [bimbo06wole@yahoo.com](bimbo06wole@yahoo.com); +2348036868260  \nReceived: 22.05.2024 Revised: 28.06.2024 Accepted: 30.06.2024 Published: 02.07.2024  \nAbstract:  \nUrban land cover classification using high-resolution imagery is important for many applications where detailed and precise urban land cover products are needed. Machine learning algorithms are currently some of the most commonly used methods for classifying high-resolution imagery due to their impressive capabilities. However, the reliability of the land cover products obtained from the classification of high-resolution urban imageries is dependent upon the accuracy of the Machine Learning (ML) classification algorithm used. The need for an appropriate selection ofclassifiers for urban land cover classification and their applicable settings necessitates the performance comparison of major ML algorithms used for classification. In this study, we compared the performance of three major Machine Learning (ML) classifier algorithms using a high-resolution image dataset ofan urban area. The algorithms are Support Vector Machine (SVM), Naïve Bayes, and Ensemble classifiers. The performance of three model types of SVM classifiers namely Medium Gaussian, Linear, and Quadratic SVM, two model types of Naïve Bayes classifiers namely Gaussian and Kernel Naïve Bayes, and three model types of ensemble classifiers namely Bagged Trees, Subspace Discriminant, and RUSBoosted Trees were compared. Performance evaluation was carried out using Confusion Matrix (CM) and Receiver Operating Curves (ROC) plots. Results obtained from the comparison of the three ML classifier algorithms show that the Subspace Discriminant ensemble classifier had the highest accuracy at 85.1%, closely followed by the Medium Gaussian SVM classifier (84.5%) and Gaussian Naïve Bayes classifier (81.5%) . This research provides insights into the selection of classifiers for future urban land cover classification and their applicable settings.  \nKeywords: Support Vector Machine, Naïve Bayes Classifiers, Ensemble Classifiers, High-resolution imagery, Urban land cover  \n1. Introduction  \nLand cover classification is an remote sensing task that makes  \nimportant and challenging use of intelligent algorithms  \nto interpret remotely sensed imagery and classify each pixel into a predefined land cover class [1, 2] . Urban land cover information is essential for various urban planning applications such as urban land resource management, urban environment monitoring, change detection, and nature conservation [3] .  \nWith improvements in remote sensing data acquisition technologies, a large amount of remotely sensed images with high spatial resolution are increasingly becoming widespread and available at little or no cost [4] . This opens new vistas and opportunities for urban land cover information classification ata very detailed level, therefore, allowing effective urban monitoring, planning, and management with a higher level of discrimination [5] . Urban land cover information obtained from the classification of high-resolution images is essential for many applications where the need for detailed and precise urban land cover maps is indispensable [3] . The accuracy of the classification algorithm used for the classification then becomes  \na matter of great importance as accurate land cover information is crucial for many remote sensing applications and of particular concern in urban land cover classification [6, 7, 8] .  \nMachine learning-based classifier algorithms are currently some of the most commonly used methods for the classification  \nof high-resolution imagery due to their impressive computational and spatial analysis capabiliti","cbCaifoGmSyoEFJc","https://ap.wps.com/l/cbCaifoGmSyoEFJc","pdf",860590,1,6,"English","en",105,"# Abstract\n# Introduction\n## Background and importance of urban land cover classification\n## Role of high-resolution imagery\n## Need for ML classifier performance comparison\n# Methods (Overview)\n## Classifiers and model variants considered\n## Performance evaluation metrics (CM and ROC)","[{\"question\":\"Which three main machine learning algorithms are compared in the study?\",\"answer\":\"The study compares Support Vector Machine (SVM), Naïve Bayes, and ensemble classifiers for urban land cover classification.\"},{\"question\":\"How is model performance evaluated?\",\"answer\":\"Performance is assessed using confusion matrix (CM) and Receiver Operating Curves (ROC) plots.\"},{\"question\":\"Which classifier achieves the highest accuracy and what is the value?\",\"answer\":\"The Subspace Discriminant ensemble classifier has the highest accuracy at 85.1%.\"}]","A Performance Comparison of Three Machine Learning Algorithms for Urban Land Cover Classification using High Resolution Imagery - 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