Assessment of ensemble classifiers using the bagging technique for improved land cover classification of multispectral satellite images
Joint Authors
Zahran, Muhammad
Muhammad, Hasan
Negm, Abdelazim
Saavedra, Oliver
Source
The International Arab Journal of Information Technology
Issue
Vol. 15, Issue 2 (31 Mar. 2018), pp.270-277, 8 p.
Publisher
Publication Date
2018-03-31
Country of Publication
Jordan
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
This study evaluates an approach for Land-Use Land-Cover classification (LULC) using multispectral satellite images.
This proposed approach uses the Bagging Ensemble (BE) technique with Random Forest (RF) as a base classifier for improving classification performance by reducing errors and prediction variance.
A pixel-based supervised classification technique with Principle Component Analysis (PCA) for feature selection from available attributes using a Landsat 8 image is developed.
These attributes include coastal, visible, near-infrared, short-wave infrared and thermal bands in addition to Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI).
The study is performed in a heterogeneous coastal area divided into five classes: water, vegetation, grass-lake-type, sand, and building.
To evaluate the classification accuracy of BE with RF, it is compared to BE with Support Vector Machine (SVM) and Neural Network (NN) as base classifiers.
The results are evaluated using the following output: commission, omission errors, and overall accuracy.
The results showed that the proposed approach using BE with RF outperforms SVM and NN classifiers with 93.3% overall accuracy.
The BE with SVM and NN classifiers yielded 92.6% and 92.1% overall accuracy, respectively.
It is revealed that using BE with RF as a base classifier outperforms other base classifiers as SVM and NN.
In addition, omission and commission errors were reduced by using BE with RF and NN classifiers.
American Psychological Association (APA)
Muhammad, Hasan& Negm, Abdelazim& Zahran, Muhammad& Saavedra, Oliver. 2018. Assessment of ensemble classifiers using the bagging technique for improved land cover classification of multispectral satellite images. The International Arab Journal of Information Technology،Vol. 15, no. 2, pp.270-277.
https://search.emarefa.net/detail/BIM-838617
Modern Language Association (MLA)
Muhammad, Hasan…[et al.]. Assessment of ensemble classifiers using the bagging technique for improved land cover classification of multispectral satellite images. The International Arab Journal of Information Technology Vol. 15, no. 2 (Mar. 2018), pp.270-277.
https://search.emarefa.net/detail/BIM-838617
American Medical Association (AMA)
Muhammad, Hasan& Negm, Abdelazim& Zahran, Muhammad& Saavedra, Oliver. Assessment of ensemble classifiers using the bagging technique for improved land cover classification of multispectral satellite images. The International Arab Journal of Information Technology. 2018. Vol. 15, no. 2, pp.270-277.
https://search.emarefa.net/detail/BIM-838617
Data Type
Journal Articles
Language
English
Notes
Includes appendix : p. 277
Record ID
BIM-838617