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Ensemble classifiers in remote sensing: a comparative analysis
| Nome: | Descrição: | Tamanho: | Formato: | |
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| 2.61 MB | Adobe PDF |
Autores
Resumo(s)
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.
Descrição
Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
Palavras-chave
Accuracy Bagging Boosting CART Classifiers Ensemble Land Cover and Land Use Maps Linear Discriminant Classifier Majority Voting Neural Networks Random Forest