• 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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.6
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• pp.667-675
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• 2011

In this paper, we propose an automatic image-to-image registration between high resolution multi-sensor images. To do this, TerraSAR-X image was shifted according to the initial translation differences of the x and y directions between images estimated using Mutual Information method. After that, the Canny edge operator was applied to both images to extract linear features. These features were used to design a cost function that finds matching points based on the similarities of their locations and gradient orientations. For extracting large number of evenly distributed matching points, only one point within each regular grid constructed throughout the image was extracted to the final matching point pair. The model, which combined the piecewise linear function with the global affine transformation, was applied to increase the accuracy of the geometric correction, and the proposed method showed RMSE lower than 5m in all study sites.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.6
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• pp.485-494
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• 2017

This study focuses on automatic image registration between multiple IR images using simple preprocessing method and modified local feature extraction algorithm. The input images were preprocessed by using the median and absolute value after histogram equalization, and it could be effectively applied to reduce the brightness difference value between images by applying the similarity of extracted features to the concept of angle instead of distance. The results were evaluated using visual and inverse RMSE methods. The features that could not be achieved by the existing local feature extraction technique showed high image matching reliability and application convenience. It is expected that this method can be used as one of the automatic registration methods between multi-sensor images under specific conditions.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.3
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• pp.41-48
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• 2012

Using multi-sensor or multi-temporal high resolution satellite images together is essential for efficient applications in remote sensing area. The purpose of this paper is to estimate geometric difference of translations between high-resolution optical and SAR images automatically. The geometric and radiometric pre-processing steps were fulfilled to calculate the similarity between optical and SAR images by using Mutual Information method. The coarsest-level pyramid images of each sensor constructed by gaussian pyramid method were generated to estimate the initial translation difference of the x, y directions for calculation efficiency. The precise geometric difference of translations was able to be estimated by applying this method from coarsest-level pyramid image to original image in order. Yet even when considered only translation between optical and SAR images, the proposed method showed RMSE lower than 5m in all study sites.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.11a
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• pp.159-160
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• 2017

본 논문에서는 무인기를 통해 수집한 영상을 과학적 분석 및 매핑이 가능한 영상으로 산출하는 자동 기하보정 시스템을 제안한다. 해당 시스템은 무인기를 활용하여 상시적으로 재난 상황을 촬영하여 감시 및 분석을 하며, 무인기에 탑재된 다중복합 센서 데이터의 실시간 처리 분석을 통해 국지적 홍수 재난의 감지 예측 및 상황대응을 지원하고, 통합경보 시스템과 연동하여 대국민 재난 정보를 제공하는 서비스를 위한 요소 기술이다. 현재 본 서비스를 제공할 수 있는 Front to End 시스템이 개발 완료되어 실제 필드에서의 재난 감시 및 예측 성능을 검증하기 위한 필드 테스트를 준비 중에 있다. 이에 본 논문에서는 현재 구축하고 있는 홍수 재난 관리 플랫폼에 대한 내용을 간단히 소개하고, 중요한 기능중 하나인 무인기 촬영 영상의 자동기하보정 시스템에 대해서 논한다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.5
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• pp.409-416
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• 2006

To use image data obtained from different sensors and different techniques, the preprocessing step that registers them in a common coordinate system is needed. For this purpose, we developed the methodology to register middle- and low-resolution satellite images automatically. Firstly, candidate matching points were extracted using the Harris and Harris-affine algorithm. Secondly, we used the correlation coefficient, normalized correlation coefficient and SIFT algorithm to detect conjugate matching points from candidates. Then, to test the feasibility of approaches, we applied the developed methodology to various kinds of satellite images and compared results. The results clearly demonstrate that the methology using the SIFT is appropriate to register these multi-resolution satellite images automatically, compared with the classical cross-correlation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.209-219
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• 2019

Satellite imagery occurs geometric and radiometric errors due to external environmental factors at the acquired time, which in turn causes false-alarm in change detection. These errors should be eliminated by geometric and radiometric corrections. In this study, we propose a methodology that automatically and simultaneously performs geometric and radiometric corrections by using the SURF (Speeded-Up Robust Feature) algorithm and the mask filter. The MPs (Matching Points), which show invariant properties between multi-temporal imagery, extracted through the SURF algorithm are used for automatic geometric correction. Using the properties of the extracted MPs, PIFs (Pseudo Invariant Features) used for relative radiometric correction are selected. Subsequently, secondary PIFs are extracted by generated mask filters around the selected PIFs. After performing automatic using the extracted MPs, we could confirm that geometric and radiometric errors are eliminated as the result of performing the relative radiometric correction using PIFs in geo-rectified images.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.431-447
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• 2021

Recently, the use of high-resolution satellites is increasing in many areas. In order to supply useful satellite images stably, it is necessary to establish automatic precision geometric correction technic. Geometric correction is the process that corrected geometric errors of satellite imagery based on the GCP (Ground Control Point), which is correspondence point between accurate ground coordinates and image coordinates. Therefore, in the automatic geometric correction process, it is the key to acquire high-quality GCPs automatically. In this paper, we proposed iterative precision geometry correction method. we constructed an image pyramid and repeatedly performed GCP chip matching, outlier detection, and precision sensor modeling in each layer of the image pyramid. Through this method, we were able to acquire high-quality GCPs automatically. we then improved the performance of geometric correction of high-resolution satellite images. To analyze the performance of the proposed method, we used KOMPSAT-3 and 3A Level 1R 8 scenes. As a result of the experiment, the proposed method showed the geometric correction accuracy of 1.5 pixels on average and a maximum of 2 pixels.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.1
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• pp.19-28
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• 2019

Images captured by drone have the advantage of quickly constructing spatial information in small areas and are applied to fields that require quick decision making. If an image registration technique that can automatically register the drone image on the ortho-image with the ground coordinate system is applied, it can be used for various analyses. In this study, a methodology for geo-referencing of a single image and sequential images using drones was proposed even if they differ in spatio-temporal resolution using linear features and distinct points. Through the method using linear features, projective transformation parameters for the initial geo-referencing between images were determined, and then finally the geo-referencing of the image was performed through the template matching for distinct points that can be extracted from the images. Experimental results showed that the accuracy of the geo-referencing was high in an area where relief displacement of the terrain was not large. On the other hand, there were some errors in the quantitative aspect of the area where the change of the terrain was large. However, it was considered that the results of geo-referencing of the sequential images could be fully utilized for the qualitative analysis.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.345-347
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• 2012

본 논문에서는 4-조각 전립선 병리조직 영상을 기하 보정 및 강체 정합하여 자동 스티칭하는 방법을 제안한다. 4-조각 병리영상에 대하여 총 3번의 스티칭을 수행하는데, 좌, 우 조각영상의 상, 하 영상 간 스티칭을 각각 수행 한 후 좌, 우 조각 영상 간 스티칭을 수행한다. 강체 정합 전 병리 조각의 코너를 이용해 x-축, y-축 방향의 위치보정과 회전보정을 수행하는 기하보정 단계를 거침으로서 지역적 최적해로의 수렴을 방지하며, 강체 정합의 정확성을 높인다. 병리영상은 전체적으로 밝기값이 유사하므로 밝기값이 아닌 외곽선 정보를 이용하여 조각 영상간의 거리를 최소화시켜 강체 정합한다. 실험 결과, 4-조각 전립선 병리조직 영상이 지역적 최적해에 수렴하지 않고, 조각 영상 간 거리를 최소화 하며, 하나로 스티칭 됨을 확인할 수 있었다. 제안 방법의 총 수행 시간은 평균 10.32초로 측정되었다. 본 논문의 제안 방법은 4조각으로 나뉜 전립선 병리조직 영상을 하나로 스티칭 함으로서 해당 조직의 전체 구조 파악 및 조직 내에서의 암의 위치 파악에 사용 될 수 있으며 이를 통한 전립선암의 확진에도 사용 될 수 있다.