KLASIFIKASI JENIS LAHAN MENGGUNAKAN CNN BERBASISKAN CITRA SATELIT

MAULANA, FUAD and Fachrurrozi, Muhammad and Rachmatullah, M Naufal (2022) KLASIFIKASI JENIS LAHAN MENGGUNAKAN CNN BERBASISKAN CITRA SATELIT. Undergraduate thesis, Sriwijaya University.

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Abstract

Remote sensing is a useful technique for mapping and monitoring geographic areas. Land classification based on satellite imagery is one of the applications of remote sensing. Classifying images manually requires a lot of time and effort. The CNN method can be used to automate the image classification process. However, with various CNN architectures that have been found, it is necessary to conduct experiments to find which CNN architecture is good to use. In this study, a comparison of image classification was carried out using 3 CNN architectures, namely VGG-16, ResNet-50, and EfficientNet-B0. Architecture training and testing was carried out on the EuroSAT dataset consisting of 10 classes with a total of 27,000 images. The CNN model uses a dataset with a split ratio of 80% as training data and 20% as test data. The experiment was carried out with two variations of the input shape, with a size of 64 x 64 pixels and 224 x 224 pixels. The results showed that the best Overall Accuracy (OA) was owned by ResNet-50 at 96.93%, followed by VGG-16 at 95.22%, while EfficientNet-B0 had a fairly low accuracy with a value of 31.96%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	CNN, Satelit
Subjects:	G Geography. Anthropology. Recreation > G Geography (General) > G142 Aerial geography Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. T Technology > T Technology (General) > T1-995 Technology (General) T Technology > T Technology (General) > T1-995 Technology (General) > T14 Philosophy. Theory. Classification. Methodology Cf. CB478 Technology and civilization
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Users 22236 not found.
Date Deposited:	08 Feb 2023 01:48
Last Modified:	08 Feb 2023 01:48
URI:	http://repository.unsri.ac.id/id/eprint/89331

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