This page has only limited features, please log in for full access.

Dr. Jeseon Yoo
Marine Disaster Research Center, Korea Institute of Ocean Science and Technology (KIOST), Korea

Basic Info

Basic Info is private.

Research Keywords & Expertise

0 Remote Sensing
0 nearshore processes
0 Coastal monitoring
0 coastal morphology
0 Coastal disaster prevention

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 21 October 2020 in Remote Sensing
Reads 0
Downloads 0

We propose an unsupervised network with adversarial learning, the Raindrop-aware GAN, which enhances the quality of coastal video images contaminated by raindrops. Raindrop removal from coastal videos faces two main difficulties: converting the degraded image into a clean one by visually removing the raindrops, and restoring the background coastal wave information in the raindrop regions. The components of the proposed network—a generator and a discriminator for adversarial learning—are trained on unpaired images degraded by raindrops and clean images free from raindrops. By creating raindrop masks and background-restored images, the generator restores the background information in the raindrop regions alone, preserving the input as much as possible. The proposed network was trained and tested on an open-access dataset and directly collected dataset from the coastal area. It was then evaluated by three metrics: the peak signal-to-noise ratio, structural similarity, and a naturalness-quality evaluator. The indices of metrics are 8.2% (+2.012), 0.2% (+0.002), and 1.6% (−0.196) better than the state-of-the-art method, respectively. In the visual assessment of the enhanced video image quality, our method better restored the image patterns of steep wave crests and breaking than the other methods. In both quantitative and qualitative experiments, the proposed method more effectively removed the raindrops in coastal video and recovered the damaged background wave information than state-of-the-art methods.

ACS Style

Jinah Kim; Dong Huh; Taekyung Kim; Jaeil Kim; Jeseon Yoo; Jae-Seol Shim. Raindrop-Aware GAN: Unsupervised Learning for Raindrop-Contaminated Coastal Video Enhancement. Remote Sensing 2020, 12, 3461 .

AMA Style

Jinah Kim, Dong Huh, Taekyung Kim, Jaeil Kim, Jeseon Yoo, Jae-Seol Shim. Raindrop-Aware GAN: Unsupervised Learning for Raindrop-Contaminated Coastal Video Enhancement. Remote Sensing. 2020; 12 (20):3461.

Chicago/Turabian Style

Jinah Kim; Dong Huh; Taekyung Kim; Jaeil Kim; Jeseon Yoo; Jae-Seol Shim. 2020. "Raindrop-Aware GAN: Unsupervised Learning for Raindrop-Contaminated Coastal Video Enhancement." Remote Sensing 12, no. 20: 3461.

Journal article
Published: 08 February 2020 in Estuarine, Coastal and Shelf Science
Reads 0
Downloads 0

The morphological response of a wave-dominated embayed beach to large storms was investigated via field data collection and numerical simulation. Hydrodynamic and morphological field data collected in Haeundae Beach facing SSE in the south-eastern corner of Korea were analysed. During the southerly storm waves (i.e. almost shore-normal direction), the upper part sediments of the beach were eroded, and redistributed to the surf zone. Meanwhile, easterly storm waves transported sediment from east to west, resulting in coastline advancement in the west, and retreat in the east. In order to investigate the morphological response of the beach with limited sediment availability under storm conditions, the numerical model XBeach was used. With the assumption of unlimited sediment availability, the southerly storms led to overestimation of sediment transports, and unrealistic erosion and deposition patterns in the middle part of the beach. In contrast, the model results with the option of sediment layer thickness showed good agreement with the erosion and deposition patterns analysed from field data. This suggests that sediment availability can be one of the key factors in determining the morphological response in a wave-dominated embayed beach, where sediment supply from neighbouring beaches or rivers is limited.

ACS Style

Kideok Do; Jeseon Yoo. Morphological response to storms in an embayed beach having limited sediment thickness. Estuarine, Coastal and Shelf Science 2020, 234, 106636 .

AMA Style

Kideok Do, Jeseon Yoo. Morphological response to storms in an embayed beach having limited sediment thickness. Estuarine, Coastal and Shelf Science. 2020; 234 ():106636.

Chicago/Turabian Style

Kideok Do; Jeseon Yoo. 2020. "Morphological response to storms in an embayed beach having limited sediment thickness." Estuarine, Coastal and Shelf Science 234, no. : 106636.

Journal article
Published: 15 May 2019 in Journal of Marine Science and Engineering
Reads 0
Downloads 0

In process-based numerical models, reducing the amount of input parameters, known as input reduction (IR), is often required to reduce the computational effort of these models and to enable long-term, ensemble predictions. Currently, a comprehensive performance assessment of IR-methods is lacking, which hampers guidance on selecting suitable methods and settings in practice. In this study, we investigated the performance of 10 IR-methods and 36 subvariants for wave climate reduction to model the inter-annual evolution of nearshore bars. The performance of reduced wave climates is evaluated by means of a brute force simulation based on the full climate. Additionally, we tested how the performance is affected by the number of wave conditions, sequencing, and duration of the reduced wave climate. We found that the Sediment Transport Bins method is the most promising method. Furthermore, we found that the resolution in directional space is more important for the performance than the resolution in wave height. The results show that a reduced wave climate with fewer conditions applied on a smaller timescale performs better in terms of morphology than a climate with more conditions applied on a longer timescale. The findings of this study can be applied as initial guidelines for selecting input reduction methods at other locations, in other models, or for other domains.

ACS Style

Bruna De Queiroz; Freek Scheel; Sofia Caires; Dirk-Jan Walstra; Derrick Olij; Jeseon Yoo; Ad Reniers; Wiebe De Boer; De Queiroz; Olij; Yoo; De Boer. Performance Evaluation of Wave Input Reduction Techniques for Modeling Inter-Annual Sandbar Dynamics. Journal of Marine Science and Engineering 2019, 7, 148 .

AMA Style

Bruna De Queiroz, Freek Scheel, Sofia Caires, Dirk-Jan Walstra, Derrick Olij, Jeseon Yoo, Ad Reniers, Wiebe De Boer, De Queiroz, Olij, Yoo, De Boer. Performance Evaluation of Wave Input Reduction Techniques for Modeling Inter-Annual Sandbar Dynamics. Journal of Marine Science and Engineering. 2019; 7 (5):148.

Chicago/Turabian Style

Bruna De Queiroz; Freek Scheel; Sofia Caires; Dirk-Jan Walstra; Derrick Olij; Jeseon Yoo; Ad Reniers; Wiebe De Boer; De Queiroz; Olij; Yoo; De Boer. 2019. "Performance Evaluation of Wave Input Reduction Techniques for Modeling Inter-Annual Sandbar Dynamics." Journal of Marine Science and Engineering 7, no. 5: 148.

Conference paper
Published: 30 December 2018 in Coastal Engineering Proceedings
Reads 0
Downloads 0

Nearshore sandbar patterns can affect the hydrodynamics and, as a result, the beach morphodynamics in the nearshore zone. Hence, spatial and temporal variability in the sandbars can influence beach accretion and erosion. Understanding the variability of the sandbar system can therefore be crucial for informed coastal zone management. So far, the methods to study sandbar dynamics mainly include datasets of video observations or occasional bathymetric surveys. However, at most locations around the world, these types of data are not or only scarcely available. In this paper we present an alternative method to analyze long-term sandbar variability by means of freely available satellite imagery. These images are globally available since the 1980’s and, thus, have the potential to be applicable at any location in the world. Here, we will illustrate the methodology by means of a case study at Anmok beach at the South Korean East coast.

ACS Style

Panagiotis Athanasiou; Wiebe De Boer; Pieter Koen Tonnon; Jeseon Yoo; Matthieu De Schipper; Sierd De Vries; Roshanka Ranasinghe; Ad Reniers. LONG-TERM BAR DYNAMICS USING SATELLITE IMAGERY: A CASE STUDY AT ANMOK BEACH, SOUTH KOREA. Coastal Engineering Proceedings 2018, 1, 91 .

AMA Style

Panagiotis Athanasiou, Wiebe De Boer, Pieter Koen Tonnon, Jeseon Yoo, Matthieu De Schipper, Sierd De Vries, Roshanka Ranasinghe, Ad Reniers. LONG-TERM BAR DYNAMICS USING SATELLITE IMAGERY: A CASE STUDY AT ANMOK BEACH, SOUTH KOREA. Coastal Engineering Proceedings. 2018; 1 (36):91.

Chicago/Turabian Style

Panagiotis Athanasiou; Wiebe De Boer; Pieter Koen Tonnon; Jeseon Yoo; Matthieu De Schipper; Sierd De Vries; Roshanka Ranasinghe; Ad Reniers. 2018. "LONG-TERM BAR DYNAMICS USING SATELLITE IMAGERY: A CASE STUDY AT ANMOK BEACH, SOUTH KOREA." Coastal Engineering Proceedings 1, no. 36: 91.

Journal article
Published: 01 May 2018 in Journal of Coastal Research
Reads 0
Downloads 0
ACS Style

Yeon S. Chang; Bastiaan Huisman; Wiebe de Boer; Jeseon Yoo. Hindcast of Long-term Shoreline Change due to Coastal Interventions at Namhangjin, Korea. Journal of Coastal Research 2018, 85, 201 -205.

AMA Style

Yeon S. Chang, Bastiaan Huisman, Wiebe de Boer, Jeseon Yoo. Hindcast of Long-term Shoreline Change due to Coastal Interventions at Namhangjin, Korea. Journal of Coastal Research. 2018; 85 ():201-205.

Chicago/Turabian Style

Yeon S. Chang; Bastiaan Huisman; Wiebe de Boer; Jeseon Yoo. 2018. "Hindcast of Long-term Shoreline Change due to Coastal Interventions at Namhangjin, Korea." Journal of Coastal Research 85, no. : 201-205.