Creating Ensembles of Classifiers through UMDA for Aerial Scene Classification
arxiv(2023)
摘要
Aerial scene classification, which aims to semantically label remote sensing
images in a set of predefined classes (e.g., agricultural, beach, and harbor),
is a very challenging task in remote sensing due to high intra-class
variability and the different scales and orientations of the objects present in
the dataset images. In remote sensing area, the use of CNN architectures as an
alternative solution is also a reality for scene classification tasks.
Generally, these CNNs are used to perform the traditional image classification
task. However, another less used way to classify remote sensing image might be
the one that uses deep metric learning (DML) approaches. In this sense, this
work proposes to employ six DML approaches for aerial scene classification
tasks, analysing their behave with four different pre-trained CNNs as well as
combining them through the use of evolutionary computation algorithm (UMDA). In
performed experiments, it is possible to observe than DML approaches can
achieve the best classification results when compared to traditional
pre-trained CNNs for three well-known remote sensing aerial scene datasets. In
addition, the UMDA algorithm proved to be a promising strategy to combine DML
approaches when there is diversity among them, managing to improve at least
5.6
available classifiers for the construction of the final ensemble of
classifiers.
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