• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.409-409
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• 2023

하천 수위는 합리적인 수자원의 이용 및 관리를 위해 반드시 필요한 수문 자료이다. 우리나라에서는 수위 측정을 위해 유역 내에 관측소를 설치하여 장비 또는 인력을 통해 수위를 측정하고 있다. 하지만, 많은 관측소를 운영하고 관리하기에는 예산과 인력이 소모되는 한계가 있다. 위성 영상을 통한 시계열 분석은 전지구적 모니터링과 관측 분야에 중요한 역할을 수행할 것으로 기대되고 있으며, 특히 위성 영상자료를 활용한 수자원 분야 연구가 활발히 진행되고 있다. 위성 영상을 활용하여 수면적을 감지하고 수위와 유량을 판별하는 많은 연구가 진행되었지만, 하천 하상의 경사와 단면 형태에 따라 수면적이 변하여 정량적인 수위 추정에는 한계가 존재한다. 본 연구에서는 Sentinel-1의 SAR 영상과 InSAR 기법을 통해 낙동강 유역의 홍수 전후의 하천 수위 변화를 분석하였다. Sentinel-1 IW 모드의 Single Look Complex(SLC) 영상 12장과 ESA 영상 처리 툴인 SNAP을 활용하여 VV(Vertical-Vertical) 데이터의 간섭을 통해 센티미터(cm) 단위지표 변화에 따른 수위 변위를 분석하였다. 위성 영상을 통해 추출한 수위 변위와 계측 수위 및 단면 자료의 정합성을 비교한 결과, 제방과 수체 경계면 식생과 하상 세굴로 인한 오차로 정량적이 수위의 정합성에는 한계가 존재하였지만, 수위의 정량적인 변동성을 확인할 수 있었으며, 수위 변화의 반응속도를 판별할 수 있었다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.152-153
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• 2023

AIS has been widely used for maritime traffic assessment for its convenience. However, AIS has problems with position missing due to radio interference and transmission distance limit. On the other hand, satellite radar determines the location of ships over a wide sea regardless of the problems. This study proposes a noble method of stepwise threshold determination to detect ships from Sentinel-1. The proposed method is up to 25 times faster than the existing moving window-based threshold determination method, and the detection accuracy is similar.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.999-1009
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• 2019

The importance of high-resolution sea ice maps of the Arctic Ocean is increasing due to the possibility of pioneering North Pole Routes and the necessity of precise climate prediction models. In this study,sea ice classification algorithms for two deep learning models were examined using Sentinel-1 A/B SAR data to generate high-resolution sea ice classification maps. Based on current ice charts, three classes (Open Water, First Year Ice, Multi Year Ice) of training data sets were generated by Arctic sea ice and remote sensing experts. Ten sea ice classification algorithms were generated by combing two deep learning models (i.e. Simple CNN and Resnet50) and five cases of input bands including incident angles and thermal noise corrected HV bands. For the ten algorithms, analyses were performed by comparing classification results with ground truth points. A confusion matrix and Cohen's kappa coefficient were produced for the case that showed best result. Furthermore, the classification result with the Maximum Likelihood Classifier that has been traditionally employed to classify sea ice. In conclusion, the Convolutional Neural Network case, which has two convolution layers and two max pooling layers, with HV and incident angle input bands shows classification accuracy of 96.66%, and Cohen's kappa coefficient of 0.9499. All deep learning cases shows better classification accuracy than the classification result of the Maximum Likelihood Classifier.

• Korean Journal of Remote Sensing
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• v.34 no.6_4
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• pp.1555-1565
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• 2018

Detection of subtle ground deformation of volcanoes plays an important role in evaluating the risk and possibility of volcanic eruptions. Ground-fixed observation equipment is difficult to maintain and cost-inefficient. In contrast, satellite remote sensing can regularly monitor at low cost. In this paper, following the study of Chadwick et al. (2006), which applied the interferometric SAR (InSAR) technique to the Sierra Negra volcano, Galapagos. In order to investigate the deformation of the volcano before 2005 eruption, the recent activities of this volcano were analyzed using Sentinel-1, the latest SAR satellite. We obtained the descending mode Sentinel-1A SAR data from January 2017 to January 2018, applied the Persistent Scatter InSAR, and estimated the depth and expansion quantity of magma in recent years through the Mogi model. As a result, it was confirmed that the activity pattern of volcano prior to the eruption in June 2018 was similar to the pattern before the eruption in 2005 and was successful in estimating the depth and expansion amount. The results of this study suggest that satellite SAR can characterize the activity patterns of volcano and can be possibly used for early monitoring of volcanic eruption.

• Journal of Digestive Cancer Research
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• v.5 no.1
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• pp.44-49
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• 2017

Background: Recently, endoscopic submucosal dissection (ESD) with laparoscopic sentinel lymph node dissection, named ESN or endoscopic full-thickness gastric resection with laparoscopic sentinel lymph node dissection, named Hybrid-natural orifice transluminal endoscopic surgery (NOTES) was suggested the possibility of minimally invasive treatment for patients with early gastric cancer (EGC) who were beyond the indication of ESD. This study aimed to evaluate the outcomes of ESN or Hybrid-NOTES. Methods: We retrospectively analyzed patients treated with these therapies from January 2009 to May 2013 in terms of short- and long-term outcomes. Each patient was diagnosed with EGC but was not included in ESD indications and had the high risk of lymph node metastasis (LNM). Results: A total of 42 patients with EGC treated by ESN or Hybrid-NOTES. Of the 21 patients who underwent ESN, a total of 4 patients underwent additional gastrectomy, 1 with LNM, 1 with surgical complication, and 2 with noncurative resection. Of the 21 patients who underwent Hybrid-NOTES, a total of 5 patients underwent additional surgery, 1 with LNM, 2 with surgical complication, and 2 with noncurative resection. Overall survival was 100% over a mean follow-up of 75 months, but 3 patients underwent ESD or gastrectomy with metachronous lesion. And 1 patient who had received ESN was found to have a metastatic lymph node and undergo palliative chemotherapy. Conclusion: ESN or Hybrid-NOTES showed favorable short-and long-term outcomes. These methods may be utilized as a bridge between ESD and gastrectomy in the case of EGC which is more likely to have LNM beyond the ESD indications.

• Korean Journal of Remote Sensing
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• v.35 no.5_2
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• pp.773-779
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• 2019

We developed the automatic ground deformation monitoring system using Sentinel-1 satellites which is operating by European Space Agency (ESA) for the Korea Peninsula's ground disaster monitoring. Ground deformation occurring over a long-term period are difficult to monitoring because it occurred in a wide area and required a large amount of satellite data for analysis. With the development of satellites, the methods to regularly observe large areas has been developed. These accumulated satellite data are used for time series ground displacement analysis. The National Disaster Management Research Institute (NDMI) established an automation system for all processes ranging from acquiring satellite observation data to analyzing ground displacement and expressing them. Based on the system developed in this research, ground displacement data on the Korean Peninsula can be updated periodically. In the future, more diverse ground displacement information could be provided if automated small regional analysis systems, multi-channel analysis method, and 3D analysis system techniques are developed with the existing system.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1551-1565
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• 2020

On April 4, 2019, a forest fire started in Goseong County and lasted for three days, burning the neighboring areas of Sokcho. The strong winds moved the blaze from one region to another region and declared the worst wildfire in South Korea in years. More than 1,880 facilities, including 400 homes, were burnt down. The fire burned a total area of 529 hectares (1,307 acres), which involved 13,000 rescuers and 16,500 military troops to control the fire occurrence. Thousands of people were evacuated, and two people are dead. This study generated post-wildfire maps to provide necessary data for evacuation and mitigation planning to respond to this destructive wildfire, also prevent further damage and restore the area affected by the wildfire. This study used KOMPSAT-3A and Sentinel-2 imagery to map the post-wildfire condition. The SVM showed higher accuracy (overall accuracy 95.29%) compared with ANN (overall accuracy of 94.61%) for the KOMPSAT-3A. Moreover, for Sentinel-2, the SVM attained a higher accuracy (overall accuracy of 91.52%) than the ANN algorithm (overall accuracy 90.11%). In total, four post-wildfire burned area maps were generated; these results can be used to assess the area affected by the Sokcho wildfire and wildfire mitigation planning in the future.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.559-570
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• 2022

When a satellite image has low spatial resolution, it is difficult to detect small objects. In this research, we aim to check the effect of super resolution on object detection. Super resolution is a software method that increases the resolution of an image. Unpaired super resolution network is used to improve Sentinel-2's spatial resolution from 10 m to 3.2 m. Faster-RCNN, RetinaNet, FCOS, and S²ANet were used to detect vessels in the Sentinel-2 images. We experimented the change in vessel detection performance when super resolution is applied. As a result, the Average Precision (AP) improved by at least 12.3% and up to 33.3% in the ship detection models trained with the super-resolution image. False positive and false negative cases also decreased. This implies that super resolution can be an important pre-processing step in object detection, and it is expected to greatly contribute to improving the accuracy of other image-based deep learning technologies along with object detection.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.233-243
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• 2021

Photovoltaic panels are hazardous electronic waste that has heavy metal as one of the hazardous components. Each year, hazardous electronic waste is increasing worldwide and every heavy rainfall exposes the photovoltaic panel to become the source of heavy metal soil contamination. the development needs a monitoring technology for this hazardous exposure. this research use relationships between SAR temporal baseline and coherence of Sentinel-1 satellite to detected photovoltaic panel. Also, the photovoltaic plant detection tested using the difference between that photovoltaic panel and the other difference surface of coherence. The author tested the photovoltaic panel and its environment to calculate differences in coherence relationships. As a result of the experiment, the coherence of the photovoltaic panel, which is assumed to be a permanent scatterer, shows a bias that is biased toward a median value of 0.53 with a distribution of 0.50 to 0.65. Therefore, further research is needed to improve errors that may occur during processing. Additionally, the author found that the change detection using a temporal baseline is possible as the rate of reduction of coherence of photovoltaic panels differs from those of artificial objects such as buildings. This result could be an efficient way to continuously monitor regardless of weather conditions, which was a limitation of the existing optical satellite image-based photovoltaic panel detection research and to understand the spatial distribution in situations such as photovoltaic panel loss.

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.153-166
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• 2022

Waterstorage is one of the factorsthat most directly represent the amount of available water resources. Since the effects of drought can be more intuitively expressed, it is also used in variousstudies for drought evaluation. In a recent study, hydrological drought was evaluated through information on observing reservoirs with optical images. The short observation cycle and diversity of optical satellites provide a lot of data. However, there are some limitations because it is vulnerable to the influence of weather or the atmospheric environment. Therefore, thisstudy attempted to conduct a study on estimating the drought index using Synthetic Aperture Radar (SAR) image with relatively little influence from the observation environment. We produced the waterbody of Baekgok and Chopyeong reservoirs using SAR images of Sentinel-1 satellites and calculated the Reservoir Area Drought Index (RADI), a hydrological drought index. In order to validate the applicability of RADI to drought monitoring, it was compared with Reservoir Storage Drought Index (RSDI) based on measured storage. The two indices showed a very high correlation with the correlation coefficient, r=0.87, Area Under curve, AUC=0.97. These results show the possibility of regional-scale hydrological drought monitoring of SAR-based RADI. As the number of available SAR images increases in the future, it is expected that the utilization of drought monitoring will also increase.