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Adaptive CNN Ensemble for Complex Multispectral Image Analysis
المؤلفون المشاركون
Jameel, Syed Muslim
Hashmani, Manzoor Ahmed
Rehman, Mobashar
Budiman, Arif
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-21، 21ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-04-15
دولة النشر
مصر
عدد الصفحات
21
التخصصات الرئيسية
الملخص EN
Multispectral image classification has long been the domain of static learning with nonstationary input data assumption.
The prevalence of Industrial Revolution 4.0 has led to the emergence to perform real-time analysis (classification) in an online learning scenario.
Due to the complexities (spatial, spectral, dynamic data sources, and temporal inconsistencies) in online and time-series multispectral image analysis, there is a high occurrence probability in variations of spectral bands from an input stream, which deteriorates the classification performance (in terms of accuracy) or makes them ineffective.
To highlight this critical issue, firstly, this study formulates the problem of new spectral band arrival as virtual concept drift.
Secondly, an adaptive convolutional neural network (CNN) ensemble framework is proposed and evaluated for a new spectral band adaptation.
The adaptive CNN ensemble framework consists of five (05) modules, including dynamic ensemble classifier (DEC) module.
DEC uses the weighted voting ensemble approach using multiple optimized CNN instances.
DEC module can increase dynamically after new spectral band arrival.
The proposed ensemble approach in the DEC module (individual spectral band handling by the individual classifier of the ensemble) contributes the diversity to the ensemble system in the simple yet effective manner.
The results have shown the effectiveness and proven the diversity of the proposed framework to adapt the new spectral band during online image classification.
Moreover, the extensive training dataset, proper regularization, optimized hyperparameters (model and training), and more appropriate CNN architecture significantly contributed to retaining the performance accuracy.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Jameel, Syed Muslim& Hashmani, Manzoor Ahmed& Rehman, Mobashar& Budiman, Arif. 2020. Adaptive CNN Ensemble for Complex Multispectral Image Analysis. Complexity،Vol. 2020, no. 2020, pp.1-21.
https://search.emarefa.net/detail/BIM-1144275
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Jameel, Syed Muslim…[et al.]. Adaptive CNN Ensemble for Complex Multispectral Image Analysis. Complexity No. 2020 (2020), pp.1-21.
https://search.emarefa.net/detail/BIM-1144275
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Jameel, Syed Muslim& Hashmani, Manzoor Ahmed& Rehman, Mobashar& Budiman, Arif. Adaptive CNN Ensemble for Complex Multispectral Image Analysis. Complexity. 2020. Vol. 2020, no. 2020, pp.1-21.
https://search.emarefa.net/detail/BIM-1144275
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1144275
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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