• Korean Journal of Remote Sensing
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• v.22 no.4
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• pp.285-294
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• 2006

A numerical formula that presents relationship between a point of a satellite image and its ground position is called a sensor model. For precise geolocation of satellite images, we need an error-free sensor model. However, the sensor model based on GOES ephemeris data has some error, in particular after Image Motion Compensation (IMC) mechanism has been turned off. To solve this problem, we investigated three sensor models: collinearity model, direct linear transform (DLT) model and orbit-based model. We applied matching between GOES images and global coastline database and used successful results as control points. With control points we improved the initial image geolocation accuracy using the three models. We compared results from three sensor models. As a result, we showed that the orbit-based model is a suitable sensor model for precise geolocation of GOES-9 Images.

• Korean Journal of Remote Sensing
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• v.23 no.4
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• pp.297-309
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• 2007

The first Korean geostationary weather satellite, Communications, Oceanography and Meteorology Satellite (COMS) will be launched in 2008. The ground station for COMS needs to perform geometric correction to improve accuracy of satellite image data and to broadcast geometrically corrected images to users within 30 minutes after image acquisition. For such a requirement, we developed automated and fast geometric correction techniques. For this, we generated control points automatically by matching images against coastline data and by applying a robust estimation called RANSAC. We used GSHHS (Global Self-consistent Hierarchical High-resolution Shoreline) shoreline database to construct 211 landmark chips. We detected clouds within the images and applied matching to cloud-free sub images. When matching visible channels, we selected sub images located in day-time. We tested the algorithm with GOES-9 images. Control points were generated by matching channel 1 and channel 2 images of GOES against the 211 landmark chips. The RANSAC correctly removed outliers from being selected as control points. The accuracy of sensor models established using the automated control points were in the range of $1{\sim}2$ pixels. Geometric correction was performed and the performance was visually inspected by projecting coastline onto the geometrically corrected images. The total processing time for matching, RANSAC and geometric correction was around 4 minutes.

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.20-24
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• 2007

센서의 광학 해상력에 따라 센서에 도달하는 에너지의 앙은 영상의 화소값으로 변환되므로，이 관계는 위성 영상의 정량적인 광학적 특성을 이해하기 위한 바탕이 되며，영상의 질을 유지하기 위한 척도가 되기도 한다. 위성영상의 광학적 특성은 ON 값 을 실제 지표물의 반사에너지 값인 Radiance와 Reflectance로 의 변환을 통하여 추정할 수 있으며，이러한 과정을 절대복사보정(Absolute Radiometric Calibration) 이라고 한다. 절대복사보정 과정에서 센서에 도달하는 태양 에너지의 양을 추정하기 위하여 복사전달모델이 사용된다. 태양 에너지가 대기를 통과함에 따라 여러 가지 상호 작용이 일어나게 되므로 복사 전달모델을 사용하기 위해서는 다양한 입력 변수가 필요하게 된다. 이러한 입력 변수로는 지표물의 반사율， 대기자료，그리고 센서의 특성 등이 포함되며， 이 연구에서는 KOMPSAT-2 위성의 MSC 영상의 절대복사보정 과정에서 복사전달모델의 결과에 영향을 미치는 입력 변수의 특성을 살펴보고자 한다.

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.70-75
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• 2007

2008년 12월에 우리나라 최초의 통신해양기상위성(Communications， Oceanography and Meteorology Satellite, COMS)이 발사될 예정이다. 통신해양기상위성의 영상데이터의 기하보정을 위하여 다음과 같은 연구를 수행하였다. 기상위성은 정지궤도상에 위치하여 전지구적인 영상을 얻는다. 영상의 전지구적인 해안선은 구름 등으로 가려져서 명확한 정보를 제공할 수 없게 된다. 구름 등으로 방해되지 않는 명확한 해안선 정보를 얻기 위하여 구름 추출을 한다. 실시간으로 기상정보를 얻는 기상위성의 특성상 정합에 전체 영상을 사용하면 수행시간이 다소 소요된다. 정합시 전체 영상에서 정합을 위한 후보점 추출을 위하여 GSHHS(Global Self-consistent Hierarchical High-resolution Shoreline)의 해안선 데이터베이스를 사용하여 211 개 의 랜드마크 칩들을 구축하였다. 이때 구축된 랜드마크 칩은 실험에 사용한 GOES-9의 위치 동경 155도를 반영하여 구축하였다. 전체 영상에서 구축된 랜드마크 칩들의 위치를 중심으로 구름추출을 수행한다. 전체 211 개의 후보점 중 구름이 제거된 나머지 후보점에 대하여 정합을 수행한다. 랜드마크 칩과 위성영상 간의 정합 중 참정합과 오정합이 존재하는데 자동으로 오정합을 검출하기 위하여 강인추정기법 (RANSAC, Random Sample Consensus)을 사용한다. 이때 자동으로 판별되어 오정합이 제거된 정합결과로 최종적인 기하보정을 수행한다. 기하보정을 위한 센서모델은 GOES-9 위성의 센서특정을 고려하여 개발되었다. 정합 및 RANSAC결과로 얻어진 기준점으로 정밀 센서모델을 수립하여 기하보정을 실시하였다. 이때 일련의 수행과정을 통신해양기상위성의 실시간 처리요구사항에 맞도록 속도를 최적화하여 진행되도록 개발하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.1155-1156
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• 2023

본 논문은 다수의 저정밀 GPS(Global Positioning System) 센서를 활용하여, 보다 정밀하고 정확한 GPS 좌표 보정 모델을 설계하고자 한다. 제안 모델에서는 다수의 저정밀 센서에서 GPS 좌표를 수합한 뒤, 최소자승법과 선형칼만필터로 값을 보정한다. 본 논문에서는 Horizontal Accuracy 가 2m 인 저정밀 GPS 센서를 활용하여 제안 모델을 설계하였으며, 저정밀 센서와 제안 모델 비교 실험에서 제안 모델의 46% 정밀도 향상을 확인하였다.

• Aerospace Engineering and Technology
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• v.9 no.1
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• pp.28-34
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• 2010

All pixels of image sensor do not react uniformly when the light of same radiance enters into the camera. This non-uniformity has a direct influence on the image quality. However we can overcome it by calibration process under the special test-setup. Usually it is used the algorithm to get the correction coefficients under the specific illumination condition. But, this method has drawback in the very low or very high illumination due to pixel non-linearity. This paper describes the robust algorithm, which calculates the correction coefficients based on the pixel non-linearity model, against thew hole radiance. The paper shows the non-uniformity test results with the own camera and the specified test equipments as well. The results shows the best performance over the entire radiance when this method is applied.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.1
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• pp.35-43
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• 2012

In general, load carrying capacity, one of the load & resistance capacities in bridges, has more margins than the load carrying capacity evaluated with theoretical methods, unless there are severe damages, defects or material deterioration phenomena that can have a great impact on the behavior of bridges. However, errors have been already included in the current processes of loading tests and structural analysis for measuring load carrying capacity, thus devaluing the reliability of response adjustment factor. Therefore, this study found out the problems of existing electric resistance strain and displacement sensors in sensor suite to solve the problems with sensors and the errors in the appropriateness of structural analysis model, thereby leading to the changes into an optical fiber smart sensor with excellent performance. Besides, the study attempted to ensure the accuracy of response adjustment factor by selecting the optimal models through the interpretation of various structural analysis models.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.3D
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• pp.437-444
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• 2008

In the paper, a methodology is verified to integrate IKONOS-2 satellite imagery and ALS dataset by compensating biases of RPC models. To achieve this, conjugate features from both data should be extracted in advance. For this purpose, linear features are chosen as conjugate features because they can be accurately extracted from man-made structures in urban area and more easily extracted than point features from ALS data. Then, observation equations are established from similarity measurements of the extracted features. During the process, several kinds of transformation functions were selected and used to register them. In addition, it was also analyzed how the number of linear features used as control features affects the accuracy of registration results. Finally, the results were evaluated by using check-points obtained from DGPS surveying techniques and it was clearly demonstrated that the proposed algorithms are appropriate to integrate these data.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.4
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• pp.177-186
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• 2015

For data fusion of laser range finder (LRF) and vision camera, accurate calibration of external parameters which describe relative pose between two sensors is necessary. This paper proposes a new calibration method which can acquires more accurate external parameters between a LRF and a vision camera compared to other existing methods. The main motivation of the proposed method is that any corner data of a known 3D structure which is acquired by the LRF should be projected on a straight line in the camera image. To satisfy such constraint, we propose a 3D geometric model and a numerical solution to minimize the energy function of the model. In addition, we describe the implementation steps of the data acquisition of LRF and camera images which are necessary in accurate calibration results. In the experiment results, it is shown that the performance of the proposed method are better in terms of accuracy compared to other conventional methods.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1541-1551
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• 2023

Radiometric calibration is a fundamental step in ocean color remote sensing since the step to derive solar radiance spectrum in visible to near-infrared wavelengths from the sensor-observed electromagnetic signals. Generally, satellite sensor suffers from degradation over the mission period, which results in biases/uncertainties in radiometric calibration and the final ocean products such as water-leaving radiance, chlorophyll-a concentration, and colored dissolved organic matter. Therefore, the importance of radiometric calibration for the continuity of ocean color satellites has been emphasized internationally. This study introduces an approach to improve the radiometric calibration algorithm for the visible bands of the Geostationary Ocean Color Imager-II (GOCI-II) satellite with a focus on stability. Solar Diffuser (SD) measurements were employed as an on-orbit radiometric calibration reference, to obtain the continuous monitoring of absolute gain values. Time series analysis of GOCI-II absolute gains revealed seasonal variations depending on the azimuth angle, as well as long-term trends by possible sensor degradation effects. To resolve the complexities in gain variability, an azimuth angle correction model was developed to eliminate seasonal periodicity, and a sensor degradation correction model was applied to estimate nonlinear trends in the absolute gain parameters. The results demonstrate the effects of the azimuth angle correction and sensor degradation correction model on the spectrum of Top of Atmosphere (TOA) radiance, confirming the capability for improving the long-term stability of GOCI-II data.