• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.649-652
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• 2005

In this paper we present a detail approach of the determination of IFOV (Instantaneous Field of View) of high-resolution (l m) panchromatic satellite image over test site. IFOV is the representative measurements as the determination of the spatial resolution in remote sensed imaging system. It can be defined as some area on the ground with the particular altitude when the satellite acquires the image at any given time. Especially, spatial resolution of passive sensors primarily depends on their IFOV. The determination of IFOV goes through simple steps of procedure as followings: Firstly, the GSD (Ground Sample Distance) should be computed at each point on the geometrically corrected image. Then, The GSD is converted into the IFOV. So we are going to explain our test procedures and results.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.301-304
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• 2008

Since longer wavelength microwave radiation can penetrate clouds, satellite passive microwave sensors can observe sea ice of the entire polar region on a daily basis. Thus, it is becoming popular to derive sea ice motion vectors from a pair of satellite passive microwave sensor images observed at one or few day interval. Usually, the accuracies of derived vectors are validated by comparing with the position data of drifting buoys. However, the number of buoys for validation is always quite limited compared to a large number of vectors derived from satellite images. In this study, the sea ice motion vectors automatically derived from pairs of AMSR-E 89GHz images (IFOV = 3.5 ${\times}$ 5.9km) by an image-to-image cross correlation were validated by comparing with sea ice motion vectors manually derived from pairs of cloudless MODIS images (IFOV=250 ${\times}$ 250m). Since AMSR-E and MODIS are both on the same Aqua satellite of NASA, the observation time of both sensors are the same. The relative errors of AMSR-E vectors against MODIS vectors were calculated. The accuracy validation has been conducted for 5 scenes. If we accept relative error of less than 30% as correct vectors, 75% to 92% of AMSR-E vectors derived from one scene were correct. On the other hand, the percentage of correct sea ice vectors derived from a pair of SSM/I 85GHz images (IFOV = 15 ${\times}$ 13km) observed nearly simultaneously with one of the AMSR-E images was 46%. The difference of the accuracy between AMSR-E and SSM/I is reflecting the difference of IFOV. The accuracies of H and V polarization were different from scene to scene, which may reflect the difference of sea ice distributions and their snow cover of each scene.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.7
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• pp.86-94
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• 1998

The rosette-scan seeker is a device mounted on the infrared guided missile. It offers the positions and iamges of target to missiles servo system by scanning a space about target in rosette pattern with a single detector. An instantaneous field of view (IFOV), which is a diameter of a detector moving along the path of the rosette pattern, has the property that its smaller size provide the less interference of background signals and detector noise. If its size is too small to voer the total field of view (TFOV), however, it produces the invisible regions in the TFOV. In this case, the invisible regions cause the performance of the seeker to deteriorate. For full scan-coverage, it is necessary to design the small IFOV without the invisible regions in the TFOV, as possible. In this paper, we propose the new method of designing the smaller IFOV than the conventional method and verify full coverage of the scanned region. By comparing the nose equivalent flux density (NEFD) of the proposed method with the that of the conventional one, we confirm that the former is better than the latter in terms of performance.

• Proceedings of the KSRS Conference
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• v.1
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• pp.7-10
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• 2006

Four programs, i.e. TRMM, ADEOS2, ASTER, and ALOS are going on in Japanese Earth Observation programs. TRMM and ASTER are operating well, and TRMM operation will be continued to 2009. ADEOS2 was failed, but AMSR-E on Aqua is operating. ALOS (Advanced Land Observing Satellite) was successfully launched on $24^{th}$ Jan. 2006. ALOS carries three instruments, i.e., PRISM (Panchromatic Remote Sensing Instrument for Stereo Mapping), AVNIR-2 (Advanced Visible and Near Infrared Radiometer), and PALSAR (Phased Array L band Synthetic Aperture Radar). PRISM is a 3 line panchromatic push broom scanner with 2.5m IFOV. AVNIR-2 is a 4 channel multi spectral scanner with 10m IFOV. PALSAR is a full polarimetric active phased array SAR. PALSAR has many observation modes including full polarimetric mode and scan SAR mode. After the unfortunate accident of ADEOS2, JAXA still have plans of Earth observation programs. Next generation satellites will be launched in 2008-2012 timeframe. They are GOSAT (Greenhouse Gas Observation Satellite), GCOM-W and GCOM-C (ADEOS-2 follow on), and GPM (Global Precipitation Mission) core satellite. GOSAT will carry 2 instruments, i.e. a green house gas sensor and a cloud/aerosol imager. The main sensor is a Fourier transform spectrometer (FTS) and covers 0.76 to 15 ${\mu}m$ region with 0.2 to 0.5 $cm^{-1}$ resolution. GPM is a joint project with NASA and will carry two instruments. JAXA will develop DPR (Dual frequency Precipitation Radar) which is a follow on of PR on TRMM. Another project is EarthCare. It is a joint project with ESA and JAXA is going to provide CPR (Cloud Profiling Radar). Discussions on future Earth Observation programs have been started including discussions on ALOS F/O.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.157-162
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• 2003

중저해상도 광학영상의 순간시야각(instantaneous filed of view -IFOV)에 포함되는 공간에는 반사특성이 상이한 두 개 이상의 지표물이 존재하는 경우가 대부분이다. 영상분류와 같은 기존의 영상처리기법에서는 하나의 화소가 단일의 지표물을 대표한다는 가정에서 접근하였으나, 최근 화소의 혼합정도를 세분하는 분광혼합분석(spectral mixture analysis)기법이 개발되고 있다. 분광혼합분석법을 이용하여 혼합된 화소에 포함된 지표물을 분해(unmixing) 하고 그 효과를 분석하고자 하여 경기도 광릉국립수목원의 시험림 지역을 대상으로 Landsat-7 ETM＋영상을 이용하여 선형혼합 모델을 적용하였고, 그 결과 각각의 화소를 6개의 End-member의 혼합비로 구분하였다. Endmember의 비율을 나타낸 영상을 분석하여 점유비율에 따른 활엽수와 침엽수의 구분을 할 수 있었고, 각 임상별의 특징도 얻을 수 있었다. 특히 침엽수의 경우 그림자의 효과가 높다는 특성도 파악 할 수 있었다. 분광혼합분석법은 기존의 전통 분류방법과는 달리 다양한 산림의 정보를 추출해 낼 수 있다.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.255-259
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• 2004

본 논문에서는 다목적 위성 2호의 주 탑재체인 MSC (Multi-Spectral Camera)의 영상자료 검보정을 위한 검보정 target 준비 작업에 대해 설명한다. MSC 영상 자료에 대한 검보정 작업은 다목적 위성 2호의 발사 후 초기 운영 기간 (LEOP: Launch and Early Operation Phase)인 3개월 동안 수행될 예정이다. 위성 발사 전까지 MSC 영상 자료에 대한 검보정을 수행하기 위해 필요한 준비 작업들이 현재 한국항공우주연구원에서 진행중이다. LEOP 기간 동안 MSC 영상 자료를 검보정하기 위해서, MSC의 센서 특성에 따라 7가지 정도의 검보정 target에 대한 설계 초안이 완성되었으며, 향후 target에 대한 설계를 완성한 후에 2004년 중에 한 두 부지에 몇 가지 target들을 건설하고, 다목적 위성 2호의 궤도 특성을 고려하여 일부 target은 운반이 가능하도록 제작할 예정이다. 검보정 target이 촬영된 MSC 영상 자료의 분석을 통해, GSD (Ground Sample Distance), Aliasing, Linearity, Edge Slope & Response, MTF (Modulation Transfer Function), FOV & IFOV, Absolute radiometric validation, Position Accuracy 등의 MSC 검보정 요소 값들을 측정할 계획이다.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.1
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• pp.122-128
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• 2014

Remote fourier transform infrared(FTIR) chemical imaging detection system allows detection and identification of gases in the atmosphere from long distances. In this paper, the appropriate field of view(FOV) of the FTIR imaging system was examined and the main performance of the system for the interferometer was described. For the determination of the FOV, simulations of gas dispersion range were performed with the NBC reporting and modeling software(NBC-RAMS) developed by ADD. As a result, minimum 192 mrad of FOV was required for the remote FTIR imaging system to visualize chemical warfare agents dispersed in several hundred meters. At the same time, 0.75 mrad of instantaneous field of view(IFOV) for a linear interferometer proper to take a FOV for the chemical agent imaging.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.439-439
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• 2022

인공위성은 위성통신, 기상 등 다양한 분야에서 활용되고 있지만 재난과 위성영상 특성 매칭의 제약으로 재난 상황에서는 제한적으로 사용되었다. 국내외 위성 갯수의 증가로 위성영상을 준-실시간으로 확보 가능함에 따라 활용할 수 있는 범위가 증가하여 최근에는 재난·재해에 신속하게 대비하기 위한 연구가 활발히 진행되고 있다. 본 연구는 재난 발생 지역의 위성 영상 확보를 위해 촬영된 영상과 미래시점의 촬영 예정인 영상의 촬영 예정 시간 및 영역을 빠른 시간 내 분석하여 최적 위성영상 확보에 기반이 되고자 한다. 행정안전부에서 분류한 재난·재해 유형에 따라 재난 예측, 탐지, 사후처리를 위한 위성자료의 확보를 위하여 다양한 위성과 탑재된 센서들의 궤도, 공간 해상도, 파장대 등의 위성영상의 적시성을 분석하여 최적 위성을 정의하였다. 위성 궤도 시뮬레이션은 TLE(Two Line Element) 정보를 이용하는 SGP4(Simplified General Perturbations version 4) 모델에 적용하여 개발하였다. 최신 TLE 정보를 이용하여 위성 궤도 정보 및 센서 정보(공간 해상도, Swath width, incidence angle IFOV 등)을 적용하였다. 수집된 위성 궤도 정보를 기반으로 위성의 궤도를 예측하여 예측된 위치에서의 촬영 영역을 산정하는 분석 기능을 수행하여 최종 시뮬레이션 데이터를 생성한다. 개발된 위성 궤도 시뮬레이션 알고리즘을 토대로 태풍 미탁 사례에 적용하였다. 위성 궤도 시뮬레이션 알고리즘을 태풍 미탁 사례에 적용한 결과 다종 위성리스트 중 위성 궤도 분석을 통해 최단기간 획득 가능한 위성 중 정지 궤도 기상위성인 Himawari-8, GK-2A는 태풍 경로 모니터링, 광학 위성인 Sentinel-2, PlanetScope는 건물 피해 지역, SAR 위성인 Sentinel-1, ICEYE는 홍수 지역을 탐지하는데 최적 위성 영상으로 분석되었다.

• Korean Journal of Remote Sensing
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• v.23 no.6
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• pp.507-520
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• 2007

Satellite passive microwave(PM) sensors have been observing polar sea ice concentration(SIC), ice temperature, and snow depth since 1970s. Among them SIC is playing an important role in the various studies as it is considered the first factor for the monitoring of global climate and environment changes. Verification and correction of PM SIC is essential for this purpose. In this study, we calculated SIC from KOMPSAT-1 EOC images obtained from Arctic sea ice edges from July to August 2005 and compared with SSM/I SIC calculated from NASA Team(NT) algorithm. When we have no consideration of sea ice types, EOC and SSM/I NT SIC showed low correlation coefficient of 0.574. This is because there are differences in spatial resolution and observing time between two sensors, and the temporal and spatial variation of sea ice was high in summer Arctic ice edge. For the verification of SSM/I NT SIC according to sea ice types, we divided sea ice into land-fast ice, pack ice, and drift ice from EOC images, and compared them with SSM/I NT SIC corresponding to each ice type. The concentration of land-fast ice between EOC and SSM/I SIC were calculated very similarly to each other with the mean difference of 0.38%. This is because the temporal and spatial variation of land-fast ice is small, and the snow condition on the ice surface is relatively dry. In case of pack ice, there were lots of ice ridge and new ice that are known to be underestimated by NT algorithm. SSM/I NT SIC were lower than EOC SIC by 19.63% in average. In drift ice, SSM/I NT SIC showed 20.17% higher than EOC SIC in average. The sea ice with high concentration could be included inside the wide IFOV of SSM/I because the drift ice was located near the edge of pack ice. It is also suggested that SSM/I NT SIC overestimated the drift ice covered by wet snow.

• Journal of Korean Society of Forest Science
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• v.78 no.3
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• pp.263-273
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• 1989

Accurate and cost-effective classification of forest vegetation is the primary goal for forest management and utilization of forest resources. Aerial photograph and remote sensing are the most frequent and effective method in forest resources inventories. TM and MSS are the principal observing instruments on the Landsat-4 and -5 earth observing satellite. Especially TM has considerably greater spatial, spectral, and radiometric resolution power than MSS, that is, the IFOV of TM at a nadir is 30m compared to 80m for MSS. In this study, we used TM data to classify forest types and compared the result with forest type map manufactured by interpretation of B/W infrared photographs. As a result, land use types were well defined with TM data. But classifying forest types was a little difficult and indistinct. However, the spectral signatures of forest in every season and growing stages remained as problems to be solved, and also the most effective selection and combination method of bands for differentiating the spectral plots among classes.