• Proceedings of KOSOMES biannual meeting
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• 2006.11a
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• pp.201-204
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• 2006

태풍의 경우, 주요 자연재해 중의 하나로 태풍의 상황을 정확하게 파악하는 것은 기상예측의 정도를 높이고, 재해를 방지하는데 중요한 역할을 할 수 있다. 일반적으로 태풍의 동향을 감시하는데 있어, MTSAT 등의 기상위성이 주로 활용되고 있다. 근년 인공위성의 원격탐사를 이용하여 광범위의 해양에 대한 해상풍과 파랑의 관측이 가능하게 되었다. 본 연구에서는, 2000년 QuickSCAT위성에 의한 해상풍의 월변동 특성을 조사하고, 7월에 한반도에 영향을 준 태풍 카이탁내의 해상풍을 검토하였다. 추가로 2005 년 8월 30일의 태풍 탈림에 대해서 ENVISAT ASAR Scan SAR에 의한 해상풍 추출을 시도하였다. QuickSCAT 에 의한 풍향을 이용한 방법과 SAR 자체의 패턴을 이용한 방법이 비교되었다.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.143-146
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• 2009

다산기지 주변 해역인 스발바드섬은 북반구 해빙 분포지역의 가장자리에 위치해 있으며 해빙의 이동이 비교적 빠른 지역이다. 지구 온난화의 영향을 받는 대표적인 지역으로 이 지역의 해빙변화에 대한 연구는 지구온난화의 지표로서 중요성을 가진다. 스발바드섬 주변의 해빙에서 얻어진 다편파 SAR 데이터를 분석하여 해빙에 대한 후방산란계수의 특성을 분석하고자 한다. 데이터 획득에는 ENVISAT/ASAR (2002 년 발사 C-밴드, 다편파 사용)과 PALSAR (2006 년 발사, L-밴드, 다편파 사용)의 두 가지 SAR 가 이용되었으며 데이터 획득 시기는 해빙의 변화가 활발한 4 월경이다. 기본적으로 L-밴드와 C-밴드의 두 가지 밴드별 차이에 관한 특성을 알아보고 기타 후방산란계수에 영향을 주는 요소들에 대하여 알아보고자 한다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2009.06a
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• pp.23-24
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• 2009

허베이스피리트호 원유유출 사고는 2007년 12월7일 아침 7시6분경 서해안 만리포 북서쪽 10km 해상에서 크레인을 적재한 1만1800t급 바지선이 정박 중인 홍콩 선적 유조선 허베이 스피리트호(14만6000t급)와 부딪치면서 발생했다. 이와 같은 기름 유출 사고의 경우, 유출 범위를 정확하게 이해하는 것이 중요하다. 여기서는 위 사고 기간에 얻어진 인공위성 자료를 이용하여 기름 유출을 탐지하기 위한 연구결과를 소개한다. 광학과 마이크로파데이터에 대해 유출 범위의 계산 및 해석 알고리듬에 대한 현재까지의 결과를 소개한다. 광학데이터로는 아리랑 2호(다목적실용위성 2호, KOMPSAT II)) MSC(Multi Spectral Camera)자료가 사용되었으며, 합성개구레이더로는 ENVISAT ASAR, TerraSAR-X 및 ALOS PALSAR의 자료가 사용되었다.

• Korean Journal of Remote Sensing
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• v.22 no.3
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• pp.211-219
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• 2006

A random forests classifier is applied to multi-sensor data fusion for supervised land-cover classification in order to account for the importance of variable. The random forests approach is a non-parametric ensemble classifier based on CART-like trees. The distinguished feature is that the importance of variable can be estimated by randomly permuting the variable of interest in all the out-of-bag samples for each classifier. Two different multi-sensor data sets for supervised classification were used to illustrate the applicability of random forests: one with optical and polarimetric SAR data and the other with multi-temporal Radarsat-l and ENVISAT ASAR data sets. From the experimental results, the random forests approach could extract important variables or bands for land-cover discrimination and showed reasonably good performance in terms of classification accuracy.

• Korean Journal of Remote Sensing
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• v.25 no.3
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• pp.243-253
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• 2009

For the last 14 years, space-borne satellite SAR system such as RADARSAT-1, ERS-2, and ENVISAT ASAR have provided a continuous observation over the ocean. However, the data acquired from those systems were limited to C-band frequency until the advent of the first spacebome German X-band SAR system TerraSAR-X in 2007. Korea is also planning to launch the nation's first X-band SAR satellite (KOMPSAT-5) in 2010. It is timely and necessary to develop X-band models for estimating geophysical parameters from these X-band SAR systems. In this study, X-band wind retrieval model was investigated and developed based on numerical ocean scattering model (radar backscattering model and hydrodynamic interaction model). Although these models have not yet been tested and validated for broad ranges of wind conditions, the estimated wind speeds from TerraSAR-X data show generally good agreement with in-situ measurements.

• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.501-505
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• 2009

Quasi-linear bright features persistently appeared on ENVISAT ASAR images as well as X-SAR images along the tidal channels in Gyung-Gi Bay, Korea during the ebb tides. These features are induced by spatial backscatter variations caused by surface convergence (divergence) through the interaction between tidal currents and bathymetry. In order to validate this mechanism, a numerical tidal model simulation is performed on the realistic bathymetry with the tidal boundary conditions. The tide model reproduces the current convergence zone along the tidal channel during the ebb tides, which exactly coincides with the location of bright line features on SAR images.

• Proceedings of KOSOMES biannual meeting
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• 2008.05a
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• pp.125-127
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• 2008

허베이스피리트호 원유유출 사고는 2007년 12월7일 아침 7시6분경 서해안 만리포 북서쪽 10km 해상에서 크레인을 적재한 1만1800t급 바지선이 정박 중인 흥콩 선적 유조선 허베이 스피리트호(14만6000t급)와 부딪치면서 발생했다. 이와 같은 기름 유출 사고의 경우, 유출 범위를 정확하게 이해하는 것이 중요하다. 여기서는 위 사고 기간에 얻어진 인공위성 자료를 이용하여 기름 유출을 탐지하기 위한 연구결과를 소개한다. 광학과 마이로파영상에 대해 유출 범위의 계산 및 해석 알고리듬에 대한 현재까지의 결과를 소개한다. 광학영상으로는 아리랑 2호 (다목적 실용위성 2호, KOMPSAT II) MSC(Multi Spectral Camera)자료가 사용되었으며, 합성개구레이더로는 ENVISAT ASAR, TerraSAR-X 및 ALOS PALSAR의 자료가 사용되었다.

• Journal of the Korean Society of Marine Environment & Safety
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• v.15 no.4
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• pp.283-288
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• 2009

Estimations of oil slick area after M/T Herbei Sprit accident in December 2007 were analyzed using ENVITSAT ASAR(Advanced Synthetic Aperture Radar) microwave and KOMPSAT-2 of high resolution data. Monthly end short-term variations of chlorophyll a concentration before end after M/T Herbei Sprit oil spill accident were also analyzed using SeaWiFS/MODIS ocean color data. The oil slick areas estimated by KOMPSAT-2 and ASAR satellites were 59,456 $m^2$ and 1,168 $km^2$, respectively. The winds before end after oil spill accident were prevailed the northerly and northwesterly winds, and the strength of wind in this accident was stronger than 10 m/sec. In Taean and Anmeon-do, monthly mean chlorophyll a concentrations(6.3 mg/$m^3$ and 3.7 mg/$m^3$) in January 2008 alter the oil spill were higher than those(2.9 mg/$m^3$ and 2.5 mg/$m^3$) in December 2007. Short-term variations of chlorophyll a in these areas were decreased alter one or two weeks of oil spill.

• Korean Journal of Remote Sensing
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• v.28 no.5
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• pp.489-499
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• 2012

In this paper, a classifier ensemble framework for remote sensing data classification is presented that combines classification results generated from both different training sets and different classifiers. A core part of the presented framework is to increase a diversity between classification results by using both different training sets and classifiers to improve classification accuracy. First, different training sets that have different sampling densities are generated and used as inputs for supervised classification using different classifiers that show different discrimination capabilities. Then several preliminary classification results are combined via a majority voting scheme to generate a final classification result. A case study of land-cover classification using multi-temporal ENVISAT ASAR data sets is carried out to illustrate the potential of the presented classification framework. In the case study, nine classification results were combined that were generated by using three different training sets and three different classifiers including maximum likelihood classifier, multi-layer perceptron classifier, and support vector machine. The case study results showed that complementary information on the discrimination of land-cover classes of interest would be extracted within the proposed framework and the best classification accuracy was obtained. When comparing different combinations, to combine any classification results where the diversity of the classifiers is not great didn't show an improvement of classification accuracy. Thus, it is recommended to ensure the greater diversity between classifiers in the design of multiple classifier systems.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.26-67
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• 2020

Synthetic Aperture Radar(SAR) is able to photograph the earth's surface regardless of weather conditions, day and night. Because of its possibility to search for hydrological factors such as soil moisture and groundwater, and its importance is gradually increasing in the field of water resources. SAR began to be mounted on satellites in the 1970s, and about 15 or more satellites were launched as of 2020, which around 10 satellites will be launched within the next 5 years. Recently, various types of SAR technologies such as enhancement of observation width and resolution, multiple polarization and multiple frequencies, and diversification of observation angles were being developed and utilized. In this paper, a brief history of the SAR system, as well as studies for estimating soil moisture and hydrological components were investigated. Up to now hydrological components that can be estimated using SAR satellites include soil moisture, subsurface groundwater discharge, precipitation, snow cover area, leaf area index(LAI), and normalized difference vegetation index(NDVI) and among them, soil moisture is being studied in 17 countries in South Korea, North America, Europe, and India by using the physical model, the IEM(Integral Equation Model) and the artificial intelligence-based ANN(Artificial Neural Network). RADARSAT-1, ENVISAT, ASAR, and ERS-1/2 were the most widely used satellite, but the operation has ended, and utilization of RADARSAT-2, Sentinel-1, and SMAP, which are currently in operation, is gradually increasing. Since Korea is developing a medium-sized satellite for water resources and water disasters equipped with C-band SAR with the goal of launching in 2025, various hydrological components estimation researches using SAR are expected to be active.