Classifiers ensemble in remote sensing: a comparative analysis
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/158176
comunitat-uji-handle2:10234/71345
comunitat-uji-handle3:10234/141145
comunitat-uji-handle4:
TFG-TFMMetadatos
Título
Classifiers ensemble in remote sensing: a comparative analysisAutoría
Tutor/Supervisor
Grangel Seguer, Reyes; Caetano, Mário; Henriques, RobertoTutor/Supervisor; Universidad.Departamento
Universitat Jaume I. Departament de Llenguatges i Sistemes InformàticsFecha de publicación
2014-04Editor
Universitat Jaume IResumen
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 ... [+]
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. [-]
Palabras clave / Materias
Màster Universitari Erasmus Mundus en Tecnologia Geoespacial | Erasmus Mundus University Master's Degree in Geospatial Technologies | Máster Universitario Erasmus Mundus en Tecnología Geoespacial | accuracy | bagging | Boosting | CART | Classifiers Ensemble | Diversity | Feature Selection | Land Cover and Land Use Maps | Linear Discriminant Classifier | Majority Voting | Neural Networks | Random Forest | Regularized Discriminant Classifier | Remote Sensing Imagery | Stacked Description | Single Classifiers | Support Vector Machine
Descripción
Treball final de Màster Universitari Erasmus Mundus en Tecnologia Geoespacial (Pla de 2013). Codi: SIW013. Curs acadèmic 2013-2014
Tipo de documento
info:eu-repo/semantics/masterThesisDerechos de acceso
info:eu-repo/semantics/openAccess
Aparece en las colecciones
El ítem tiene asociados los siguientes ficheros de licencia:
Excepto si se señala otra cosa, la licencia del ítem se describe como: Attribution-NonCommercial-ShareAlike 4.0 Spain
Ítems relacionados
Mostrando ítems relacionados por Título, autoría o Palabra clave.
-
Dissimilarity-Based Linear Models for Corporate Bankruptcy Prediction
García, Vicente; Marqués Marzal, Ana Isabel; Sánchez Garreta, Josep Salvador; Ochoa Domínguez, Humberto de Jesús Springer (2019-03)Bankruptcy prediction has acquired great relevance for financial institutions due to the complexity of global economies and the growing number of corporate failures, especially since the world financial crisis of 2008. In ... -
Special issue on logics and artificial intelligence
Falomir, Zoe; Costa, Vicent; Plaza, Enric; Gibert, Karina Oxford University Press (2020-01-18)There is a significant range of ongoing challenges in artificial intelligence (AI) dealing with reasoning, planning, learning, perception and cognition, among others. In this scenario, many-valued logics emerge as one of ... -
Exploring the synergetic effects of sample types on the performance of ensembles for credit risk and corporate bankruptcy prediction
García, Vicente; Marqués Marzal, Ana Isabel; Sánchez Garreta, Josep Salvador Elsevier (2018-07)Credit risk and corporate bankruptcy prediction has widely been studied as a binary classification problem using both advanced statistical and machine learning models. Ensembles of classifiers have demonstrated their ...