@Article{Esmael2018,
author="Esmael, Agnaldo Aparecido
and dos Santos, Jefersson Alex
and da Silva Torres, Ricardo",
title="On the ensemble of multiscale object-based classifiers for aerial images: a comparative study",
journal="Multimedia Tools and Applications",
year="2018",
month="Oct",
day="01",
volume="77",
number="19",
pages="24565--24592",
abstract="Remote sensing images (RSIs) are increasingly used as data source to produce maps used in several applications. Modern sensors launched into space from the end of the 1990s have been producing high spatial resolution RSIs. The use of classification methods based on regions, called as Geographic Object-Based Image Analysis (GEOBIA), has been demonstrated to be more appropriate to deal with this kind of image. However, finding the appropriate segmentation scale, which is not a trivial task, is crucial for the success of a GEOBIA method. In this paper, we perform a comparative study involving seven methods for RSI multiclass classification that combine different features extracted from different scales: M1-OvA, M2-OvO, M3-AdaMH, M4-Samme, M5-MV, M5-WMV, and M6-Cascade. The first four methods are boosting-based techniques and the last three are based on the majority vote approach. The effectiveness of the proposed methods was evaluated by analyzing the results of experiments conducted in three RSIs datasets. The methods were compared with the baseline SVM with Kernel RBF by measuring the overall accuracy, the Kappa Index, and the accuracy per class. The results show that all the proposed methods are effective for RSI classification.",
issn="1573-7721",
doi="10.1007/s11042-018-6023-4",
url="https://doi.org/10.1007/s11042-018-6023-4"
}