Assessment of ensemble classifiers using the bagging technique for improved land cover classification of multispectral satellite images
المؤلفون المشاركون
Zahran, Muhammad
Muhammad, Hasan
Negm, Abdelazim
Saavedra, Oliver
المصدر
The International Arab Journal of Information Technology
العدد
المجلد 15، العدد 2 (31 مارس/آذار 2018)، ص ص. 270-277، 8ص.
الناشر
تاريخ النشر
2018-03-31
دولة النشر
الأردن
عدد الصفحات
8
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes appendix : p. 277
رقم السجل
BIM-838617
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر