International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 8, No. 12, 8 Dec. 2016
Cover page and Table of Contents: PDF (size: 621KB)
Remote Sensing Textual Image Classification based on Ensemble Learning
Full Text (PDF, 621KB), PP.21-29
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Author(s)
Zhiwei Ye
1,*
Yang Juan
1
Zhang Xu
1
Zhengbing Hu
2
Index Terms
Remote Sensing, Textual Image Classification, Ensemble Learning, Bagging
Abstract
Remote sensing textual image classification technology has been the hottest topic in the filed of remote sensing. Texture is the most helpful symbol for image classification. In common, there are complex terrain types and multiple texture features are extracted for classification, in addition; there is noise in the remote sensing images and the single classifier is hard to obtain the optimal classification results. Integration of multiple classifiers is able to make good use of the characteristics of different classifiers and improve the classification accuracy in the largest extent. In the paper, based on the diversity measurement of the base classifiers, J48 classifier, IBk classifier, sequential minimal optimization (SMO) classifier, Naive Bayes classifier and multilayer perceptron (MLP) classifier are selected for ensemble learning. In order to evaluate the influence of our proposed method, our approach is compared with the five base classifiers through calculating the average classification accuracy. Experiments on five UCI data sets and remote sensing image data sets are performed to testify the effectiveness of the proposed method.
Cite This Paper
Ye zhiwei, Yang Juan, Zhang Xu, Hu Zhengbing,"Remote Sensing Textual Image Classification based on Ensemble Learning", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.12, pp.21-29, 2016. DOI: 10.5815/ijigsp.2016.12.03
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