• Journal of the Korean Society of Marine Environment & Safety
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• v.14 no.2
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• pp.163-168
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• 2008

Ship detection from satellite remote sensing is a crucial application for global monitoring for the purpose of protecting the marine environment and ensuring marine security. It permits to monitor sea traffic including fisheries, and to associate ships with oil discharge. An automatic ship detection approach for RADARSAT Fine Synthetic Aperture Radar (SAR) image is described and assessed using in situ ship validation information collected during field experiments conducted on August 6, 2004. Ship detection algorithms developed here consist of five stages: calibration, land masking, prescreening, point positioning, and discrimination. The fine image was acquired of Ulsan Port, located in southeast Korea, and during the acquisition, wind speeds between 0 m/s and 0.4 m/s were reported. The detection approach is applied to anchoring ships in the anchorage area of the port and its results are compared with validation data based on Vessel Traffic Service (VTS) radar. Our analysis for anchoring ships, above 68 m in length (LOA), indicates a 100% ship detection rate for the RADARSAT single beam mode. It is shown that the ship detection performance of SAR for smaller ships like barge could be higher than the land-based radar. The proposed method is also applied to estimate the ship's dimensions of length and breadth from SAR radar cross section(RCS), but those values were comparatively higher than the actual sizes because of layover and shadow effects of SAR.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.1
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• pp.15-22
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• 2022

In order to improve the accuracy of the deep learning object detection technique, the effect of magnification rate conditions and seasonal factors on detection accuracy in aerial photographs and drone images was analyzed through experiments. Among the deep learning object detection techniques, Mask R-CNN, which shows fast learning speed and high accuracy, was used to detect the vehicle to be detected in pixel units. Through Seoul's aerial photo service, learning images were captured at different screen magnifications, and the accuracy was analyzed by learning each. According to the experimental results, the higher the magnification level, the higher the mAP average to 60%, 67%, and 75%. When the magnification rates of train and test data of the data set were alternately arranged, low magnification data was arranged as train data, and high magnification data was arranged as test data, showing a difference of more than 20% compared to the opposite case. And in the case of drone images with a seasonal difference with a time difference of 4 months, the results of learning the image data at the same period showed high accuracy with an average of 93%, confirming that seasonal differences also affect learning.

• Journal of the Korean earth science society
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• v.40 no.1
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• pp.9-23
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• 2019

Super-refraction of radar beams tends to occur primarily under a particular vertical structure of temperature and water vapor pressure profiles. A quality control process for the removal of anomalous propagation (AP) ehcoes are required because APs are easily misidentified as precipitation echoes. For this purpose, we collected X-band polarimetric radar parameters (differential reflectivity, cross-correlation coefficient, and differential phase) only including non-precipitation echoes (super-refraction and clear-sky ground echoes) and precipitation echoes, and compared the echo types regarding the relationships among radar reflectivities, polarimetric parameters, and the membership functions. We developed a removal algorithm for the non-precipitation echoes using the texture approach for the polarimetric parameters. The presented algorithm is qualitatively validated using the S-band Jindo radar in Jeollanam-do. Our algorithm shows the successful identification and removal of AP echoes.

• The Journal of Engineering Geology
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• v.4 no.3
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• pp.333-341
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• 1994

The low velocity body was detected during the invesfigation of the crustal structune and upper mantle in the Korean Peninsula using ray method and observational seismic data. We observed the arrival time delays of P and S waves that pass through the Bugok hot spring area and the chugaryong rift zone in the Korean Peninsula. The present geothermal exploration accounts for the high heat flow in these regions, suggesting that the area are the 'delay shadows' produced by a deep, low velocity body(Resenberg et aL, 1980). We tried to verify the hypothesis that the low-velocity body is caused by the partial melting in the lower crust can be explained by the lateral variation(inhomogeneous model) of the lower crust velocity using Ray Method(Cerveny and Psencik, 1983).

• Geophysics and Geophysical Exploration
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• v.9 no.4
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• pp.299-303
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• 2006

Geologic noise, especially located at shallow depth, can be a great obstacle in the interpretation of geophysical data. Thus, it is important to suppress geologic noise in order to accurately detect major anomalous bodies in the survey area. In the inversion of geophysical data, model parameters at shallow depth, which have small size and low contrast of physical property, can be regarded as one of geologic noise. The least-squares method with smoothness constraint has been widely used in the inversion of geophysical data. The method imposes a big penalty on the large model parameter, while a small penalty on the small model parameter. Therefore, it is not easy to suppress small anomalous boies. In this study, we developed a new inversion scheme which can effectively suppress geologic noise by imposing a big penalty on the slowly varying model parameter and a small penalty on the largely varying model parameter. We call the method MTE (main-target emphasizing) inversion. Applying the method to the inversion of 2.5D small loop EM data, we can ensure that it is effective in suppressing small anomalous boies and emphasizing major anomalous bodies in the survey area.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.589-591
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• 2023

현재 수치예보 시스템은 항공기, 위성 등 다양한 센서에서 얻은 다종 관측 데이터를 동화하여 대기 상태를 추정하고 있지만, 관측변수 또는 물리량이 서로 다른 관측들을 처리하기 위한 계산 복잡도가 매우 높다. 본 연구에서 기존 시스템의 계산 효율성을 개선하여 관측을 평가하거나 전처리하는 데에 효율적으로 활용하기 위해, 각 관측의 특성을 고려한 자기 지도학습 방법을 통해 멀티모달 기상관측으로부터 실제 대기 상태를 추정하는 방법론을 제안하고자 한다. 비균질적으로 수집되는 멀티모달 기상관측 데이터를 융합하기 위해, (i) 기상관측의 heterogeneous network를 구축하여 개별 관측의 위상정보를 표현하고, (ii) pretext task 기반의 self-supervised learning을 바탕으로 개별 관측의 특성을 표현한다. (iii) Graph neural network 기반의 예측 모델을 통해 실제에 가까운 대기 상태를 추정한다. 제안하는 모델은 대규모 수치 시뮬레이션 시스템으로 수행되는 기존 기술의 한계점을 개선함으로써, 이상 관측 탐지, 관측의 편차 보정, 관측영향 평가 등 관측 전처리 기술로 활용할 수 있다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.4
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• pp.875-884
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• 2000

Sea fog/stratus is very difficult to detect because of the characteristics of air-sea interaction and locality ,and the scantiness of the observed data from the oceans such as ships or ocean buoys. The aim of our study develops new algorism for sea fog detection by using Geostational Meteorological Satellite-5(GMS-5) and suggests the technics of its continuous detection. In this study, atmospheric synoptic patterns on sea fog day of May, 1999 are classified; cold air advection type(OOUTC, May 10, 1999) and warm air advection type(OOUTC, May 12, 1999), respectively, and we collected two case days in order to analyze variations of water vapor at Osan observation station during May 9-10, 1999.So as to detect daytime sea fog/stratus(OOUTC, May 10, 1999), composite image, visible accumulated histogram method and surface albedo method are used. The characteristic value during day showed A(min) .20% and DA < 10% when visible accumulated histogram method was applied. And the sea fog region which is detected is similar in composite image analysis and surface albedo method. Inland observation which visibility and relative humidity is beneath 1Km and 80%, respectively, at OOUTC, May 10,1999; Poryoung for visble accumulated histogram method and Poryoung, Mokp'o and Kangnung for surface albedo method. In case of nighttime sea fog(18UTC, May 10, 1999), IR accumulated histogram method and Maximum brightness temperature method are used, respectively. Maxium brightness temperature method dectected sea fog better than IR accumulated histogram method with the charateristic value that is T_max < T_max_trs, and then T_max is beneath 700hPa temperature of GDAPS(Global Data Assimilation and Prediction System). Sea fog region which is detected by Maxium brighness temperature method was similar to the result of National Oceanic and Atmosheric Administratio/Advanced Very High Resolution Radiometer (NOAA/AVHRR) DCD(Dual Channel Difference), but usually visibility and relative humidity are not agreed well in inland.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.398-398
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• 2021

섬진강은 한국 주요 5대강 중 하나로 유량 변동계수가 가장 크다. 이로 인해, 극심한 가뭄이나 홍수의 발생 확률이 높을 뿐만 아니라, 가뭄에서 홍수 또는 홍수에서 가뭄으로 갑작스러운 극한 수문 기상 변화가 일어날 수 있다. 수자원의 안정적인 확보와 수재해로 인한 피해를 최소화하기 위한 수자원 관리와 장기적 수문분석이 필요하다. 이에 본 연구에서는 섬진강 유역의 수문 관측소(56개)에서 10년 이상 장기 관측된 일유량 자료(1997년~2020년)를 이용하여 비모수 검정 방법을 통한 추세 분석과 변동점을 탐색하였다. 우선, 일유량 관측 자료를 이용하여 누락된 일유량 관측값으로 생겨날 수 있는 불확실성을 배제하기 위해 관측 기간 중 누락된 일유량 관측값들의 월별 비율을 조사하였다. 그리고 월별 일유량 관측값 누락이 없는 관측소들의 월평균 하천 유량 값으로 연평균 하천 유량 값을 계산하였다. 관측 기간 동안 결측된 값이 없는 28개의 관측소를 대상으로 비모수 검정 방법을 통한 연별 추세 분석(Mann-Kendall Test)과 변동점 탐색(Pettitt Test)을 하였다. 연별 추세 분석 경우 28개의 관측소 중 8개의 관측소에서 통계적으로 의미 있는 추세(신뢰도> 99%)가 탐지되었다. 이들 중 3개의 관측소에서는 증가하는 추세를 보였고 5개의 관측소에서 감소하는 추세가 보였다. 7개의 관측소에서는 통계적으로 의미가 있는 변동점도 탐색되었고 그 변동점이 탐색된 연도는 2011년(4개), 2012년(3개)로 나타났다. 계절적 추세 분석에서는 28개의 관측소 중 각각 봄(MAM) 11개, 여름(JJA) 11개, 가을(SON) 9개, 겨울(DJF) 11개 관측소에서 통계적 추세(신뢰도> 99%)가 탐지되었다. 또한 봄 17개, 여름 7개, 가을 18개, 겨울 18개 관측소에서 변동점이 탐색되었고, 그 연도는 관측소마다 달랐다. 이러한 유량의 추세와 변동점의 원인(기후적/인위적 요소)을 더욱 잘 이해하기 위해, 계절별 유량과 강수량의 상관관계 분석이 연구될 필요가 있다. 이러한 장기 수문기후학적 추세와 변동성에 대한 이해는 농업이 중요한 섬진강 유역의 수자원 관리와 기후변화에 선제대응 할 수 있는 기초를 마련할 것이다.

• Korean Journal of Remote Sensing
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• v.18 no.2
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• pp.117-125
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• 2002

Multitemporal analysis with remotely sensed data is complicated by numerous intervening factors, including atmospheric attenuation and occurrence of clouds that obscure the relationship between ground and satellite observed spectral measurements. Using an adaptive reconstruction system, dynamic compositing approach was developed to recover missing/bad observations. The reconstruction method incorporates temporal variation in physical properties of targets and anisotropic spatial optical properties into image processing. The adaptive system performs the dynamic compositing by obtaining a composite image as a weighted sum of the observed value and the value predicted according to local temporal trend. The proposed system was applied to the sequence of NDVI images of AVHRR observed on the Korean Peninsula from 1999 year to 2000 year. The experiment shows that the reconstructed series can be used as an estimated series with complete data for the observations including bad/missing values. Additionally, the gradient image, which represents the amount of temporal change at the corresponding time, was generated by the proposed system. It shows more clearly temporal variation than the data image series.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.370-370
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• 2017

최근 기후변화와 이상기후의 영향으로 인하여 홍수재해의 시 공간적 패턴은 보다 복잡해지고, 예측이 어려워지고 있다. 이러한 기상이변에 따른 홍수피해를 예방하기 위한 비구조적 대책으로 홍수위험등급 및 범람범위 등의 정보를 포함하고 있는 홍수위험지도의 작성이 필요하다. 실제로 고정밀도 홍수위험지도를 작성하기 위해서는 지형, 지질, 기상 등의 디지털 정보 및 사회 경제와 관련된 다양한 DB를 필요로 하며, 강우-유출-범람해석 모델링을 통해 범람면적 및 침수깊이 등의 정보를 획득하게 된다. 하지만 일부지역, 특히 개발도상국에서는 이러한 계측 홍수 데이터가 부족하거나 획득할 수가 없어 홍수위험지도 제작이 불가능하거나 그 정확도가 매우 낮은 실정이다. 따라서 본 연구에서는 ASTER 또는 SRTM과 같은 범용 DEM 등 지형자료만을 기반으로 한 선형이진분류법(Liner binary classifiers)과 ROC분석(Receiver Operation Characteristics)을 이용하여 미계측 유역 (DB부재 또는 부족으로 강우-유출-범람해석 모델링이 불가능한 북한지역)의 홍수위험지역을 탐지하고, 적용성을 평가하고자 한다. 5개의 단일 지형학적 지수와 6개의 복합 지형학적 지수를 이용하여 Area Under the Curve (AUC)를 계산하고, Sensitivity (민감도)와 Specificity (특이도)가 가장 높은 지수를 선별하여 홍수위험지도를 작성하고, 실제 홍수범람 영상(2007년 북한 함경남도지역 용흥강 홍수)과 비교 분석하였다. 본 연구에서 제시하는 선형이진분류법과 ROC분석 방법은 홍수범람해석을 위한 다양한 기초정보를 필요로 하지 않고, 지형정보만을 사용하기 때문에 관측 데이터가 없거나 부족한 지역에 대해서 우선적으로 홍수위험지역을 탐지하고, 선별하는데 유용할 것으로 판단된다.