• 자원환경지질
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• 제32권1호
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• pp.19-31
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• 1999

In this study, we have estimated lineaments in the Euiseong area, Kyungbuk Province, from Landsat TM by applying the algorithm developed by Kim and Won et al. which can effectively reduce the look direction bias associated with the Sun's azimuth angle. Fratures over the study area were also mapped in the field at 57 selected sites to compare them with the results from the satellite image. The trends of lineaments estimated from the Landsat TM images are characterized as $N50^{\circ}$~70W, NS~$N10^{\circ}$W, and $N10^{\circ}$~$60^{\circ}$E trends. The spatial distribution of lineaments is also studied using a circular grid, and the results show that the area can be divided into two domains : domain A in which NS~$N20^{\circ}$E direction is dominant, and domain B in which west-north-west direction is prominent. The trends of lineaments can also be classified into seven groups. Among them, only C, D and G trends are found to be dominant based upon Donnelly's nearest neighbor analysis and correlations of lineament desities. In the color composite image produced by overlaying the lineament density map of these C-, D-, and G-trends, G-trend is shown to be developed in the whole study area while the eastern part of the area is dominated by D-trend. C-trend develops extensively over the whole are except the southeastern part. The orientation of fractures measured at 35 points in the field shows major trends of NS~$N30^{\circ}$E, $N50^{\circ}$~$80^{\circ}$W, and N80$^{\circ}$E~EW, which agree relatively well with the lineaments estimated form the satellite image. The rose diagram analysis fo field data shows that WNW-ESE trending discontinuities are developed in the whole area while discontinuities of NS~$N20^{\circ}$E are develped only in the estern part, which also coincide with the result from the satellite image. The combined results of lineaments from the satellite image and fracture orientation of field data at 22 points including 18 minor faults in Sindong Group imply that the WNW-ESE trend is so prominent that Gumchun and Gaum faults are possibly extended up to the lower Sindong Group in the study area.

• 대한원격탐사학회지
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• 제28권6호
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• pp.635-651
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• 2012

To produce a level-3 monthly composite image from daily level-2 Sea-viewing Wide Field-of-view Sensor (SeaWiFS) chlorophyll-a concentration data set in the East Sea, we applied four average methods such as the simple average method, the geometric mean method, the maximum likelihood average method, and the weighted averaging method. Prior to performing each averaging method, we classified all pixels into normal pixels and abnormal speckles with anomalously high chlorophyll-a concentrations to eliminate speckles from the following procedure for composite methods. As a result, all composite maps did not contain the erratic effect of speckles. The geometric mean method tended to underestimate chlorophyll-a concentration values all the time as compared with other methods. The weighted averaging method was quite similar to the simple average method, however, it had a tendency to be overestimated at high-value range of chlorophyll-a concentration. Maximum likelihood method was almost similar to the simple average method by demonstrating small variance and high correlation (r=0.9962) of the differences between the two. However, it still had the disadvantage that it was very sensitive in the presence of speckles within a bin. The geometric mean was most significantly deviated from the remaining methods regardless of the magnitude of chlorophyll-a concentration values. Its bias error tended to be large when the standard deviation within a bin increased with less uniformity. It was more biased when data uniformity became small. All the methods exhibited large errors as chlorophyll-a concentration values dominantly scatter in terms of time and space. This study emphasizes the importance of the speckle removal process and proper selection of average methods to reduce composite errors for diverse scientific applications of satellite-derived chlorophyll-a concentration data.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.214-218
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• 1999

The aim of our study is to develop new algorism for sea fog detection by using Geostational Meteorological Satellite-5(GMS-5) and suggest the techniques of its continuous detection. So as to detect daytime sea fog/stratus(00UTC, May 10, 1999), visible accumulated histogram method and surface albedo method are used. The characteristic value during daytime showed A(min) > 20％ and DA < 10％ when visble accumulated histogram method was applied. And the sea fog region which detected is of similarity in composite image and surface albedo method. In case of nighttime sea fog(18UTC, May 10, 1999), infrared accumulated histogram method and maximum brightness temperature method are used, respectively. Maximum brightness temperature method(T_max method) detected sea fog better than IR accumulated histogram method. In case of T_max method, when infrared value is larger than T_max, fog is detected, where T_max is an unique value, maximum infrared value in each pixel during one month. Then T_max is beneath 700hpa temperature of GDAPS(Global Data Assimilation and Prediction System). Sea fog region which detected by T_max method was similar to the result of National Oceanic and Atmosheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) DCD(Dual Channel Difference). But inland visibility and relative humidity didn't always agreed well.

• 한국위성정보통신학회논문지
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• 제8권1호
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• pp.83-88
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• 2013

최근 스마트폰 사용자가 늘어나면서 증강현실 (Augmented Reality) 기술을 활용한 제품 소개 또는 광고 마케팅이 활발히 이뤄지고 있다. 이는 실제 환경에 가상 물체나 정보를 합성하여 원래의 실제 환경에 존재하는 사물처럼 보이게 하는 증강현실을 활용한 애플리케이션들이다. 본 논문에서는 모바일 환경에서 증강현실의 기술 요소 및 관련 표준화 동향을 살펴보고, 이미지 기반의 증강현실 기술에 대한 표준화 필요성에 대해 논의한다.

• 한국대기환경학회지
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• 제34권3호
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• pp.493-507
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• 2018

Due to high spatio-temporal variability of amount and optical/microphysical properties of atmospheric aerosols, satellite-based observations have been demanded for spatiotemporal monitoring the major aerosols. Observations of the heavy aerosol episodes and determination on the dominant aerosol types from a geostationary satellite can provide a chance to prepare in advance for harmful aerosol episodes as it can repeatedly monitor the temporal evolution. A new geostationary observation sensor, namely the Advanced Himawari Imager (AHI), onboard the Himawari-8 platform, has been observing high spatial and temporal images at sixteen wavelengths from 2016. Using observed spectral visible reflectance and infrared brightness temperature (BT), the algorithm to find major aerosol type such as volcanic ash (VA), desert dust (DD), polluted aerosol (PA), and clean aerosol (CA), was developed. RGB color composite image shows dusty, hazy, and cloudy area then it can be applied for comparing aerosol detection product (ADP). The CALIPSO level 2 vertical feature mask (VFM) data and MODIS level 2 aerosol product are used to be compared with the Himawari-8/AHI ADP. The VFM products can deliver nearly coincident dataset, but not many match-ups can be returned due to presence of clouds and very narrow swath. From the case study, the percent correct (PC) values acquired from this comparisons are 0.76 for DD, 0.99 for PA, 0.87 for CA, respectively. The MODIS L2 Aerosol products can deliver nearly coincident dataset with many collocated locations over ocean and land. Increased accuracy values were acquired in Asian region as POD=0.96 over land and 0.69 over ocean, which were comparable to full disc region as POD=0.93 over land and 0.48 over ocean. The Himawari-8/AHI ADP algorithm is going to be improved continuously as well as the validation efforts will be processed by comparing the larger number of collocation data with another satellite or ground based observation data.

• 한국측량학회지
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• 제38권1호
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• pp.23-33
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• 2020

최근 UAV (Unmanned Aerial Vehicle)를 이용하여 고해상도 영상을 편리하게 취득할 수 있게 되면서 저비용으로 소규모 지역의 관측 및 공간정보 제작이 가능하게 되었다. 특히, 농업환경 모니터링을 위하여 작물생산 지역의 피복지도 생성에 대한 연구가 활발히 진행되고 있으며, 랜덤 포레스트와 SVM (Support Vector Machine) 및 CNN(Convolutional Neural Network) 을 적용하여 분류 성능을 비교한 결과 영상분류에서 딥러닝 적용에 대하여 활용도가 높은 것으로 나타났다. 특히, 위성영상을 이용한 피복분류는 위성영상 데이터 셋과 선행 파라메터를 사용하여 피복분류의 정확도와 시간에 대한 장점을 가지고 있다. 하지만, 무인항공기 영상은 위성영상과 공간해상도와 같은 특성이 달라 이를 적용하기에는 어려움이 있다. 이러한 문제점을 해결하기 위하여 위성영상 데이터 셋이 아닌 UAV를 이용한 데이터 셋과 국내의 소규모 복합 피복이 존재하는 농경지 분석에 활용이 가능한 딥러닝 알고리즘 적용 연구를 수행하였다. 본 연구에서는 최신 딥러닝의 의미론적 영상분류인 DeepLab V3+, FC-DenseNet (Fully Convolutional DenseNets), FRRN-B (Full-Resolution Residual Networks) 를 UAV 데이터 셋에 적용하여 영상분류를 수행하였다. 분류 결과 DeepLab V3+와 FC-DenseNet의 적용 결과가 기존 감독분류보다 높은 전체 정확도 97%, Kappa 계수 0.92로 소규모 지역의 UAV 영상을 활용한 피복분류의 적용가능성을 보여주었다.

• 한국지구과학회지
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• 제40권4호
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• pp.406-413
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• 2019

Red-Green-Blue (RGB) imagery techniques are useful for both forecasters and public users because they are intuitively understood, have advantageous visualization, and do not lose observational information. This study presents a novel RGB convective cloud product and its application to tropical cyclone analysis using Communication, Oceanography, and Meteorology (COMS) satellite observations. The RGB convective cloud product was developed using the brightness temperature differences between WV ($6.75{\mu}m$) and IR1 ($10.8{\mu}m$), and IR2 ($12.0{\mu}m$) and IR1 ($10.8{\mu}m$) as well as the brightness temperature in the IR1 bands of the COMS, with the threshold values estimated from the Korea Meteorological Administration (KMA) radar observations and the EUMETSAT RGB recipe. To verify the accuracy of the convective cloud RGB product, the product was applied to the center positions analysis of two typhoons in 2013. Thus, the convective cloud RGB product threshold values were estimated for WV-IR1 (-20 K to 15 K), IR1 (210 K to 300 K), and IR1-IR2 (-4 K to 2 K). The product application in typhoon analysis shows relatively low bias and root mean square errors (RMSE)s of 23 and 28 km for DANAS in 2013, and 17 and 22 km for FRANCISCO in 2013, as compared to the best tracks data from the Regional Specialized Meteorological Center (RSMC) in Tokyo. Consequently, our proposed RGB convective cloud product has the advantages of high accuracy and excellent visualization for a variety of meteorological applications.

• 대한원격탐사학회지
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• 제38권6_1호
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• pp.1343-1356
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• 2022

광학위성영상을 이용해 농작물을 모니터링 할 때 가장 문제가 되는 것은 구름이나 그림자이다. 구름과 그림자의 영향을 줄이기 위해 일정 주기동안 최대 정규식생지수를 선택하는 합성기법이 사용되었다. 그러나, 본 방법은 구름의 영향을 줄이기는 하나, 일정 주기 동안 최대 정규식생지수(Normalized Difference Vegetation Index, NDVI)값만을 사용하기 때문에 NDVI가 감소하는 현상을 신속히 보여주기 어렵다. 이에 따라, 구름의 영향을 최소화하면서 식생의 분광정보를 최대한 유지하기 위한 방안으로 합성 시 여러 환경인자를 정의하고, 이에 대한 점수를 부여하여 합성 시 가장 적합한 화소를 선택하는 방법인 점수 기반 합성기법이 제시되었다. 본 연구에서는 Sentinel-2A/B Level2A 반사율 영상과, 부가정보로 제공되는 구름, 그림자, Aerosol Optical Thickness(AOT), 촬영날짜, 센서천정각 등을 이용한 점수 기반 식생지수 합성기법을 개발하였다. 2021년동안 당진 논지역과 태백 고랭지 배추밭을 대상으로 15일 주기와 한달 주기로 점수기반 합성기법을 적용한 결과, 구름의 영향을 받은 우기만을 제외하고 15일 주기 합성 시 한달 주기에 비해 보다 빠르고 자세한 NDVI값의 변화를 볼 수 있었다. 특정 영상에서는 합성 NDVI영상에서 부분적으로 날짜별 차이가 나타나 공간적으로 이질적인 부분이 보이기도 하는데, 이는 사용한 구름, 그림자 정보의 부정확성으로 인한 것으로 사려된다. 향후 입력정보의 정확도를 향상시키고, Maximum NDVI Composite (MNC) 기반 합성기법과 정량적 비교를 수행할 예정이다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.386-389
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• 1999

In this study, visible and near infrared channels of NOAA/AVHRR data were used to classify land use and vegetation types over Korean peninsula. Analyzing forest stand structures and prediction of forest productivity using satellite data were also reviewed. Land use and land cover classification was made by unsupervised clustering methods. After monthly Normalized Difference Vegetation Index (NDVI) composite images were derived from April to November 1998, the derived composite images were used as temporal feature vector's in this clustering analysis. Visually interpreted, the classification result was satisfactory in overall for it matched well with the general land cover patterns. But subclassification of forests into coniferous, deciduous, and mixed forests were much confused due to the effects of low ground resolution of AVHRR data and without defined classification scheme. To investigate into the forest stand structures, digital forest type maps were used as an ancillary data. Forest type maps, which were compiled and digitalized by Forestry Research Institute, were registered to AVHRR image coordinates. Two data sets were compared and percent forest cover over whole region was estimated by multiple regression analysis. Using this method, other forest stand structure characteristics within the primary data pixels are expected to be extracted and estimated.

• 한국정보통신학회논문지
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• 제6권6호
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• pp.812-818
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• 2002

본 연구에서는 1993년 야간 가시밴드 위성원격탐사 DMSP/OLS 자료를 이용하여 동해 전해역 야간 어선의 시공간적 변화를 GIS(Geographic Information System)에 적용하여 살펴보았다. 야간 어선의 월별 및 계절분포를 조사하기 위해 연구 영역을 위도 30$^{\circ}$ $N-44^{\circ} N, 경도 124^{\circ} E-142^{\circ}$ E을 선택했다. 어선의 시공간적 분포 분석에 이용한 GIS 소프트웨어는 ArcView 3.2로서 그 확장기능 중에서 Image Analyst를 이용하였다. 야간 가시밴드 Operational Linescan System(OLS) 영상은 야간 어선의 시공간적 분포에 대한 유용한 정보를 제공한다. 분석된 결과를 보면, 야간 어선이 밀집된 해역은 대마도와 대한해협주변, 한국 동해안 연안지역, 일본 혼슈열도 연안지역, 대화퇴 주변해역이었다