Performance Comparison of CNN Based Hybrid Systems Using UC Merced Land-Use Dataset

YAŞAR, FATMA GÜNSELİ; UTKU, Semih

doi:10.21205/deufmd.2023257516

Performance Comparison of CNN Based Hybrid Systems Using UC Merced Land-Use Dataset

Fatma Günseli YAŞAR ÇİKLAÇANDİR, (İzmir Katip Çelebi Üniversitesi, Mühendislik-Mimarlık Fakültesi, Bilgisayar Mühendisliği Bölümü, İzmir, Türkiye)

Semih UTKU (Dokuz Eylül Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, İzmir, Türkiye)

Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi

10 7

Yıl: 2023 Cilt: 25 Sayı: 75 Sayfa Aralığı: 725 - 737 Metin Dili: İngilizce DOI: 10.21205/deufmd.2023257516 İndeks Tarihi: 04-10-2023

Performance Comparison of CNN Based Hybrid Systems Using UC Merced Land-Use Dataset

Öz:

Remote sensing is the technology of collecting and examining data about the earth with special sensors. The data obtained are used in many application areas. The classification success of remote sensing images is closely related to the accuracy and reliability of the information to be used. For this reason, especially in recent studies, it is seen that Convolutional Neural Network (CNN), which has become popular in many fields, is used and high successes have been achieved. However, it is also an important need to obtain this information quickly. Therefore, in this study, it is aimed to get results as successful as CNN and in a shorter time than CNN. Hybrid systems in which features are extracted with CNN and then classification is performed with machine learning algorithms have been tested. The successes of binary combinations of two different CNN architectures (ResNet18, GoogLeNet) and four different classifiers (Support Vector Machine, K Nearest Neighbor, Decision Tree, Discriminant Analysis) have been compared with various metrics. GoogLeNet & Support Vector Machine (93.33%) is the method with the highest accuracy rate, while ResNet18 & Decision Tree (50.95%) is the method with the lowest accuracy rate.

Anahtar Kelime: Remote Sensing Convolutional Neural Network Machine Learning Hybrid Systems Uzaktan Algılama Konvolüsyonel Sinir Ağı Makine Öğrenmesi Hibrit Sistemler

UC Merced Land-Use Veri Kümesi Kullanılarak CNN Tabanlı Hibrit Sistemlerin Performans Karşılaştırması

Öz:

Uzaktan algılama, yeryüzü ile ilgili verilerin özel sensörler aracılığıyla toplanması ve incelenmesi teknolojisidir. Elde edilen veriler birçok uygulama alanında kullanılmaktadır. Uzaktan algılama görüntülerinin sınıflandırma başarısı, kullanılacak bilgilerin doğruluğu ve güvenilirliği ile yakından ilgilidir. Bu nedenle özellikle son yıllarda yapılan çalışmalarda birçok alanda popüler hale gelen Konvolüsyonel Sinir Ağlarının (CNN) kullanıldığı ve yüksek başarılar elde edildiği görülmektedir. Ancak bu bilgilerin hızlı bir şekilde elde edilmesi de önemli bir ihtiyaçtır. Dolayısıyla bu çalışmada CNN kadar başarılı ve CNN'den daha kısa sürede sonuç alınması amaçlanmaktadır. CNN ile özniteliklerin çıkarıldığı ve daha sonra makine öğrenmesi algoritmaları ile sınıflandırmanın yapıldığı hibrit sistemler test edilmiştir. İki farklı CNN mimarisi (ResNet18, GoogLeNet) ve dört farklı sınıflandırıcının (Support Vector Machine, K Nearest Neighbor, Decision Tree, Discriminant Analysis) ikili kombinasyonlarının başarıları çeşitli metriklerle karşılaştırılmıştır. GoogLeNet & SVM (%93,33) en yüksek doğruluk oranına sahip yöntem olurken, ResNet18 & DT (%50,95) en düşük doğruluk oranına sahip yöntemdir.

Anahtar Kelime: Uzaktan Algılama Konvolüsyonel Sinir Ağı Makine Öğrenmesi Hibrit Sistemler

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık

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APA	YAŞAR F, UTKU S (2023). Performance Comparison of CNN Based Hybrid Systems Using UC Merced Land-Use Dataset. , 725 - 737. 10.21205/deufmd.2023257516
Chicago	YAŞAR FATMA GÜNSELİ,UTKU Semih Performance Comparison of CNN Based Hybrid Systems Using UC Merced Land-Use Dataset. (2023): 725 - 737. 10.21205/deufmd.2023257516
MLA	YAŞAR FATMA GÜNSELİ,UTKU Semih Performance Comparison of CNN Based Hybrid Systems Using UC Merced Land-Use Dataset. , 2023, ss.725 - 737. 10.21205/deufmd.2023257516
AMA	YAŞAR F,UTKU S Performance Comparison of CNN Based Hybrid Systems Using UC Merced Land-Use Dataset. . 2023; 725 - 737. 10.21205/deufmd.2023257516
Vancouver	YAŞAR F,UTKU S Performance Comparison of CNN Based Hybrid Systems Using UC Merced Land-Use Dataset. . 2023; 725 - 737. 10.21205/deufmd.2023257516
IEEE	YAŞAR F,UTKU S "Performance Comparison of CNN Based Hybrid Systems Using UC Merced Land-Use Dataset." , ss.725 - 737, 2023. 10.21205/deufmd.2023257516
ISNAD	YAŞAR, FATMA GÜNSELİ - UTKU, Semih. "Performance Comparison of CNN Based Hybrid Systems Using UC Merced Land-Use Dataset". (2023), 725-737. https://doi.org/10.21205/deufmd.2023257516

APA	YAŞAR F, UTKU S (2023). Performance Comparison of CNN Based Hybrid Systems Using UC Merced Land-Use Dataset. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 25(75), 725 - 737. 10.21205/deufmd.2023257516
Chicago	YAŞAR FATMA GÜNSELİ,UTKU Semih Performance Comparison of CNN Based Hybrid Systems Using UC Merced Land-Use Dataset. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 25, no.75 (2023): 725 - 737. 10.21205/deufmd.2023257516
MLA	YAŞAR FATMA GÜNSELİ,UTKU Semih Performance Comparison of CNN Based Hybrid Systems Using UC Merced Land-Use Dataset. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, vol.25, no.75, 2023, ss.725 - 737. 10.21205/deufmd.2023257516
AMA	YAŞAR F,UTKU S Performance Comparison of CNN Based Hybrid Systems Using UC Merced Land-Use Dataset. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi. 2023; 25(75): 725 - 737. 10.21205/deufmd.2023257516
Vancouver	YAŞAR F,UTKU S Performance Comparison of CNN Based Hybrid Systems Using UC Merced Land-Use Dataset. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi. 2023; 25(75): 725 - 737. 10.21205/deufmd.2023257516
IEEE	YAŞAR F,UTKU S "Performance Comparison of CNN Based Hybrid Systems Using UC Merced Land-Use Dataset." Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 25, ss.725 - 737, 2023. 10.21205/deufmd.2023257516
ISNAD	YAŞAR, FATMA GÜNSELİ - UTKU, Semih. "Performance Comparison of CNN Based Hybrid Systems Using UC Merced Land-Use Dataset". Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 25/75 (2023), 725-737. https://doi.org/10.21205/deufmd.2023257516