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http://dx.doi.org/10.7848/ksgpc.2020.38.6.499

A Study on the Complementary Method of Aerial Image Learning Dataset Using Cycle Generative Adversarial Network  

Choi, Hyeoung Wook (Dept. of Civil Engineering, Pukyong National University)
Lee, Seung Hyeon (Dept. of Computer Science & Engineering, Korea University)
Kim, Hyeong Hun (Team of Satellite Image Processing, CONTEC Co., Ltd)
Suh, Yong Cheol (Dept. of Civil Engineering, Pukyong National University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.38, no.6, 2020 , pp. 499-509 More about this Journal
Abstract
This study explores how to build object classification learning data based on artificial intelligence. The data has been investigated recently in image classification fields and, in turn, has a great potential to use. In order to recognize and extract relatively accurate objects using artificial intelligence, a large amount of learning data is required to be used in artificial intelligence algorithms. However, currently, there are not enough datasets for object recognition learning to share and utilize. In addition, generating data requires long hours of work, high expenses and labor. Therefore, in the present study, a small amount of initial aerial image learning data was used in the GAN (Generative Adversarial Network)-based generator network in order to establish image learning data. Moreover, the experiment also evaluated its quality in order to utilize additional learning datasets. The method of oversampling learning data using GAN can complement the amount of learning data, which have a crucial influence on deep learning data. As a result, this method is expected to be effective particularly with insufficient initial datasets.
Keywords
GAN; CycleGAN; Learning Data; Aerial Image;
Citations & Related Records
Times Cited By KSCI : 7  (Citation Analysis)
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