Publicação

Ensemble classifiers in remote sensing: a comparative analysis

Ver documento

Detalhes bibliográficos
Resumo:Land Cover and Land Use (LCLU) maps are very important tools for understanding the relationships between human activities and the natural environment. Defining accurately all the features over the Earth's surface is essential to assure their management properly. The basic data which are being used to derive those maps are remote sensing imagery (RSI), and concretely, satellite images. Hence, new techniques and methods able to deal with those data and at the same time, do it accurately, have been demanded. In this work, our goal was to have a brief review over some of the currently approaches in the scientific community to face this challenge, to get higher accuracy in LCLU maps. Although, we will be focus on the study of the classifiers ensembles and the different strategies that those ensembles present in the literature. We have proposed different ensembles strategies based in our data and previous work, in order to increase the accuracy of previous LCLU maps made by using the same data and single classifiers. Finally, only one of the ensembles proposed have got significantly higher accuracy, in the classification of LCLU map, than the better single classifier performance with the same data. Also, it was proved that diversity did not play an important role in the success of this ensemble.
Autores principais:Rodríguez, Hernán Cortés
Assunto:Accuracy Bagging Boosting CART Classifiers Ensemble Land Cover and Land Use Maps Linear Discriminant Classifier Majority Voting Neural Networks Random Forest
Ano:2014
País:Portugal
Tipo de documento:dissertação de mestrado
Tipo de acesso:acesso aberto
Instituição associada:Universidade Nova de Lisboa
Idioma:inglês
Origem:Repositório Institucional da UNL

Registos relacionados

Registos relacionados