• Journal of applied mathematics & informatics
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• v.41 no.3
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• pp.647-656
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• 2023

In this work, we are concerned by the problem of identification of noisy terms which arise in singular problem as for remote sensing problems, and which are modeled by a linear singular parabolic equation. For the reason of missing some data that could be arisen when using the traditional sentinel method, the later will be changed by a new sentinel method for attaining the same purpose. Such new method is a particular least square-like method which permits one to distinguish between the missing terms and the pollution terms. In particular, a sentinel method will be given here in its more realistic setting for singular parabolic problems, where in this case, the observation and the control have their support in different open sets. The problem of finding a new sentinel is equivalent to finding singular optimality system of the least square control for the parabolic equation that we solve.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.163-163
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• 2020

전 세계적으로 기후변화에 따른 온난화 현상으로 인하여 농업에 직접적인 영향을 주는 기상 및 환경요인의 변화가 급격하게 진행되고 있다. 2017년에는 전국의 봄철 강수량이 평년 대비 60% 수준으로 물 부족 현상을 야기하여 극심한 가뭄이 발생하였다. 최근 지역적인 강수량 부족으로 인한 국소적인 가뭄 발생 및 발생빈도가 높아지고 있는 추세이며, 특히 농업가뭄은 농업용수의 주요한 용수공급시설인 농업용 저수지 및 용수공급시설의 지역적 편중 등으로 농업용수 부족 상황이 발생할 위험이 커지고 있다. 따라서, 시기별 저수지의 가용용수능력을 평가하는 것이 중요하며, 이러한 판단을 위하여 위성영상을 이용한 저수지 수표면적 및 용수능력판단이 필요하다. 본 연구에서는 가뭄시기의 저수지 수표면적 및 용수능력판단을 위하여 Sentinel-2 위성영상을 활용하여 2016년부터 2018년까지 충청남도 서산 지역의 농업용 저수지를 대상으로 정규수분지수(Normalized Difference Water Index, NDWI)을 산정하였다. NDWI는 위성영상의 파장 정보를 활용하여 지표면의 수분함유량과 관계를 나타내며, 하천, 호수, 습지 등 수분을 다량으로 함유한 지형지물을 탐지하기 위하여 사용된다. NDWI와 수위-내용적 자료와의 관계로부터 저수지 수표면적을 산출하였으며, 이에 따른 상관성 분석을 통하여 위성영상을 활용한 농업용 저수지의 가용수량 추정방법을 제시하고자 한다.

• Journal of Biomedical Engineering Research
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• v.31 no.2
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• pp.114-122
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• 2010

This paper presents a method that registers a lymphoscintigraphy to the real image captured by a CMOS camera, which helps surgeons to easily and precisely detect sentinel lymph nodes for sentinel lymph node biopsy in breast cancer patients. The proposed method consists of two steps: pre-matching and image registration. In the first step, we localize fiducial markers in a lymphoscintigraphy and a real image of a four quadrant bar phantom by using image processing techniques, and then determines perspective transformation parameters by matching with the corresponding marker points. In the second step, we register a lymphoscintigraphy to a real images of patients by using the perspective transformation of pre-matching. To examine the accuracy of the proposed method, we conducted an experiment with a chest mock-up with radioactive markers. As a result, the euclidean distance between corresponding markers was less than 3mm. In conclusion, the present method can be used to accurately align lymphoscintigraphy and real images of patients without attached markers to patients, and then provide useful anatomical information on sentinel lymph node biopsy.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.12
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• pp.6181-6185
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• 2012

Background: The aim of the study was to evaluate the available breast nomograms (MSKCC, Stanford, Tenon) to predict non-sentinel lymph node metastasis (NSLNM) and to determine variables for NSLNM in SLN positive breast cancer patients in our population. Materials and Methods: We retrospectively reviewed 170 patients who underwent completion axillary lymph node dissection between Jul 2008 and Aug 2010 in our hospital. We validated three nomograms (MSKCC, Stanford, Tenon). The likelihood of having positive NSLNM based on various factors was evaluated by use of univariate analysis. Stepwise multivariate analysis was applied to estimate a predictive model for NSLNM. Four factors were found to contribute significantly to the logistic regression model, allowing design of a new formula to predict non-sentinel lymph node metastasis. The AUCs of the ROCs were used to describe the performance of the diagnostic value of MSKCC, Stanford, Tenon nomograms and our new nomogram. Results: After stepwise multiple logistic regression analysis, multifocality, proportion of positive SLN to total SLN, LVI, SLN extracapsular extention were found to be statistically significant. AUC results were MSKCC: 0.713/Tenon: 0.671/Stanford: 0.534/DEU: 0.814. Conclusions: The MSKCC nomogram proved to be a good discriminator of NSLN metastasis in SLN positive BC patients for our population. Stanford and Tenon nomograms were not as predictive of NSLN metastasis. Our newly created formula was the best prediction tool for discriminate of NSLN metastasis in SLN positive BC patients for our population. We recommend that nomograms be validated before use in specific populations, and more than one validated nomogram may be used together while consulting patients.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.939-946
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• 2021

Monitoring of post wildfire provides important information for vegetation restoration. In particular, remote sensing data are known to provide useful information necessary for monitoring. However, there are insufficient research results which is monitoring the vegetation recovery using remote sensing data. This study is directed to monitoring post-wildfire vegetation restoration. It proposes a method for monitoring vegetation restoration using Sentinel-2 satellite data by compositing Tasseled Cap linear regression trend in a post wildfire study sites. Although it is a simple visualization technique using satellite images, it was able to confirm the possibility of effective monitoring.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1187-1198
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• 2021

Sea wind isrecently drawing attraction as one of the sources of renewable energy. Thisstudy describes a new method to produce a 10 m resolution sea wind field using Sentinel-1 images and low-resolution NWP (Numerical Weather Prediction) data with artificial intelligence technique. The experiment for the South East coast in Korea, 2015-2020,showed a 40% decreased MAE (Mean Absolute Error) than the generic CMOD (C-band Model) function, and the CC (correlation coefficient) of our method was 0.901 and 0.826, respectively, for the U and V wind components. We created 10m resolution sea wind maps for the study area, which showed a typical trend of wind distribution and a spatially detailed wind pattern as well. The proposed method can be applied to surveying for wind power and information service for coastal disaster prevention and leisure activities.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.545-557
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• 2022

This study used high-resolution satellite images and supervised classification technique based on machine learning method in order to detect the areas affected by wildfires in the demilitarized zone (DMZ) where direct access is difficult. Sentinel-2 A/B was used for high-resolution satellite images. Land cover map was calculated based on the SVM supervised classification technique. In order to find the optimal combination to classify the DMZ wildfire damage area, supervised classification according to various kernel and band combinations in the SVM was performed and the accuracy was evaluated through the error matrix. Verification was performed by comparing the results of the wildfire detection based on satellite image and data by the wildfire statistical annual report in 2020 and 2021. Also, wildfire damage areas was detected for which there is no current data in 2022. This is to quickly determine reliable results.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.737-746
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• 2022

Utilizing weather independent SAR images along with machine learning based object detector is effective in robust vessel monitoring. While conventional SAR images often applied amplitude data from Single Look Complex, exploitation of polarimetric parameters acquired from multiple polarimetric SAR images was yet to be implemented to vessel detection utilizing machine learning. Hence, this study used four polarimetric parameters (H, p₁, DoP, DPRVI) retrieved from eigen-decomposition and two backscattering coefficients (γ₀, _VV, γ₀, _VH) from radiometric calibration; six bands in total were respectively exploited from 52 Sentinel-1 SAR images, accompanied by vessel training data extracted from AIS information which corresponds to acquisition time span of the SAR image. Evaluating different cases of combination, the use of polarimetric indexes along with amplitude values derived enhanced vessel detection performances than that of utilizing amplitude values exclusively.

• The Journal of Engineering Geology
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• v.32 no.2
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• pp.271-280
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• 2022

Landfast sea ice forms near coastlines in polar regions. Continuous monitoring of this sea ice is important, as it plays a key role in the marine ecosystem and affects the operation of nearby research stations. This study detected landfast sea ice around Jang Bogo research station in East Antarctica by stacking interferometric coherence images of Sentinel-1 synthetic aperture radar (SAR) data with 6-, 12- and 18-day temporal baselines. A total of 50 landfast sea ice maps were generated covering July 2017 to June 2018. The time series revealed regional differences in the timing of the maximum extent as well as growth rate of landfast sea ice. Overall, detecting landfast sea ice using interferometric SAR coherence seems promisingly feasible; however, limitations remain owing to low backscattering coefficients from new and smooth sea ice surfaces and subtle movements of sea ice in contact with the Campbell Glacier Tongue.

• Journal of Korean Society of Disaster and Security
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• v.14 no.4
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• pp.37-45
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• 2021

Flooding damage may occur due to an unexpected increase in rainfall in summer. Previously, the roughness coefficient, which is a major factor of conveyance, was calculated through on-site measurement, but in case of on-site measurement, there are many limits in accurately grasping changes in vegetation. In this study, the vegetation index (NDVI) was calculated using the Sentinel-2 optical images, and the modified roughness coefficient was calculated through the density and distribution area of the vegetation. Then the calculated roughness coefficient was applied to HEC-RAS 1D model and verified by comparing the results with the water level at the water level station directly downstream of the Soyang River dam. As a result, the error rate of the water level decreased about 14% compared to applying the previous roughness coefficient. Through this, it is expected that it will be possible to refine the flood level of rivers in consideration of seasonal flood characteristics and to efficiently maintain rivers in specific sections.