• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1475-1490
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• 2021

Detecting oil spill area using statistical characteristics of SAR images has limitations in that classification algorithm is complicated and is greatly affected by outliers. To overcome these limitations, studies using neural networks to classify oil spills are recently investigated. However, the studies to evaluate whether the performance of model shows a consistent detection performance for various oil spill cases were insufficient. Therefore, in this study, two CNNs (Convolutional Neural Networks) with basic structures(Simple CNN and U-net) were used to discover whether there is a difference in detection performance according to the structure of CNN and distribution characteristics of oil spill. As a result, through the method proposed in this study, the Simple CNN with contracting path only detected oil spill with an F1 score of 86.24% and U-net, which has both contracting and expansive path showed an F1 score of 91.44%. Both models successfully detected oil spills, but detection performance of the U-net was higher than Simple CNN. Additionally, in order to compare the accuracy of models according to various oil spill cases, the cases were classified into four different categories according to the spatial distribution characteristics of the oil spill (presence of land near the oil spill area) and the clarity of border between oil and seawater. The Simple CNN had F1 score values of 85.71%, 87.43%, 86.50%, and 85.86% for each category, showing the maximum difference of 1.71%. In the case of U-net, the values for each category were 89.77%, 92.27%, 92.59%, and 92.66%, with the maximum difference of 2.90%. Such results indicate that neither model showed significant differences in detection performance by the characteristics of oil spill distribution. However, the difference in detection tendency was caused by the difference in the model structure and the oil spill distribution characteristics. In all four oil spill categories, the Simple CNN showed a tendency to overestimate the oil spill area and the U-net showed a tendency to underestimate it. These tendencies were emphasized when the border between oil and seawater was unclear.

• Cross-Cultural Studies
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• v.44
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• pp.147-189
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• 2016

This study attempted to investigate the issue how stepmother's narrative was transferred to . For this agenda, the approach of this paper is described below to determine how Bbaengdeok's stepmother narrative was transferred episodically to . First, this paper started from the relation between stepmother narrative and affiliated with each root folk tale in Buddhist text. Among known as root folk tale until now, this paper investigated whether was the root folk tale affiliated to Buddhist literature. At that point, stepmother narrative showed relation with opening eyes motive. It has been reported that the text of opening eyes in Buddhist literature is originated from the south. This paper confirmed that 's stepmother narrative was related to Buddhist literature originated from south. Next, this paper investigated the background of opening eyes motive of stepmother types. After entering Gyeongpan , it formed Bbaengdeok narrative of Wanpan related to Literary geography of Jeollado enjoying space of Wanpan . There are two evidences for this. The first one is which was passed down around Jeollado. In the course of sudden Buddhist folklore's flow into the country, it is that combined stepmother narrative of sudden Buddhist literature with the characteristics of northern Buddhist literature which was reconstructed into son's filial behavior of northern Buddhist literature to make father's blinded eyes open. The other is the Buddhist trend of the late 18th Chosun when northern Buddhist literature and sudden Buddhist literature were combined. It appeared that stepmother narrative was formed in the Buddhist background of the late 18th Chosun when northern Buddhist literature and sudden Buddhist literature were combined.