Machine learning classifiers &Classifier ensample: improvement of land cover classification

Taha, Lamyaa; Ibrahim, Rania; Mandouh, Asmaa

ISBN 10: 6206845869 / ISBN 13: 9786206845867

Published by LAP LAMBERT Academic Publishing, 2023

About this Item

Seller Inventory # 26403767458

Bibliographic Details

• Title: Machine learning classifiers &Classifier ensample: improvement of land cover classification
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2023
• Language: English
• ISBN 10: 6206845869
• ISBN 13: 9786206845867
• Binding: Soft cover
• Condition: New

About this title

Synopsis

There are an emergent machine learning(ML) algorithms to classify land-cover and land-use. In this book we focus on the relatively mature methods (seven methods) support vector (SVM) machines, decision trees (DTs), artificial neural networks, k-nearest neighbours (k-NN), naïve Bayes, Boosting and Random forest (RF).Accurate and timely collection of urban land use and land cover information is crucial for many aspects of urban development and environment protection.Accurate land covers classification is challenging. Improving land cover classification is a hot topic. It is needed for many applications such as land use land cover mapping environmental monitoring, natural resource management, urban planning, and management and change detection. Then a number of ensample methods were studied to combine various classifiers.