• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.1003-1017
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• 2017

Multispectral image data of high spatial resolution is required to obtain correct information on the ground surface. The multispectral image data has lower resolution compared to panchromatic data. PAN-sharpening fusion technique produces the multispectral data with higher resolution of panchromatic image. Recently the object-based approach is more applied to the high spatial resolution data than the conventional pixel-based one. For the object-based image analysis, it is necessary to perform image segmentation that produces the objects of pixel group. Image segmentation can be effectively achieved by the process merging step-by-step two neighboring regions in RAG (Regional Adjacency Graph). In the satellite remote sensing, the operational environment of the satellite sensor causes image degradation during the image acquisition. This degradation increases variation of pixel values in same area, and results in deteriorating the accuracy of image segmentation. An iterative approach that reduces the difference of pixel values in two neighboring pixels of same area is employed to alleviate variation of pixel values in same area. The size of segmented regions is associated with the quality of image segmentation and is decided by a stopping rue in the merging process. In this study, the image restoration and segmentation was quantitatively evaluated using simulation data and was also applied to the three PAN-sharpened multispectral images of high resolution: Dubaisat-2 data of 1m panchromatic resolution from LA, USA and KOMPSAT3 data of 0.7m panchromatic resolution from Daejeon and Chungcheongnam-do in the Korean peninsula. The experimental results imply that the proposed method can improve analytical accuracy in the application of remote sensing high resolution PAN-sharpened multispectral imagery.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.46-56
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• 2004

As economic cooperation and exchanges between North and South Korea have been glowing much more than before, the demand for geographic information on North Korea is recently increasing. In fact, there is no specific method to be provided with geographic information on North Korea. In this regard, the study searched a method to collect geographic information on North Korea by using the high spatial resolution satellite image. In order to produce its best result, the study collected the geographic information on the case study area and ensured the location accuracy. This study produced total 52 items of geographic information on North Korea. Horizontal and vertical errors of stereo image, which are 4.6m and 0.9m respectively, showed high accuracy. In addition, even though the horizontal error of single image is 9m, which is bigger than that of stereo image, there is no doubt that it can be used as basic data for North Korean studies and related projects.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.2027-2034
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• 2021

Using multi-temporal KOMPSAT-3/3A high-resolution satellite images, the Normalized Difference Vegetation Index (NDVI) for the area around the Fukushima daiichi nuclear power plant was determined, and the pattern of vegetation changes was analyzed. To calculate the NDVI, surface reflectance from the KOMPSAT-3/3A satellite image was used. Satellite images from four years were used, and the zones where the images overlap was designated as the area of interest (AOI) for the study, and by setting a profile passing through highly vegetated area as a data analysis method, the changes by year were examined. In addition, random points were extracted within the AOI and displayed as a box plot to quantitatively indicate change of NDVI distribution pattern. The main results of this study showed that the NDVI in 2014 was low within AOI in the vicinity of the nuclear power plant, but vegetated area continued to expand until 2021. These results were also confirmed in the change monitoring results shown in a profile or box plot. In disaster areas where access is restricted, such as the Fukushima nuclear power plant area, where it is difficult to collect field data, obtaining land cover classification products with high accuracy using satellite images is challenging, so it is appropriate to analyze them using primary outputs such as vegetation indices obtained from high-resolution satellite imagery. It is necessary to establish an international cooperation system for jointly utilizing satellite images. Meanwhile, to periodically monitor environmental changes in neighboring countries that may affect the Korean peninsula, it is necessary to establish utilization models and systems using high-resolution satellite images.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2006.05a
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• pp.73-77
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• 2006

제 3차 국가 GIS 기본 계획의 목적은 국가지리정보체계의 원활한 구축 및 지리정보 활용촉진으로 기존의 구가 GIS 구축의 양적 확산에서 질적 심화를 도모하고 있다. 고해상도 위성영상이 다양한 분야에서 활발하게 이용하게 됨에 따라 지형자료의 더 정확한 경계검출에 대한 필요성이 대두되고 있다. 위성영상을 이용한 도로 경계 검출은 교통정보시스템을 포함한 도로계획, 도시계획 등의 GIS 응용을 위한 필수 연구로 인식하고 있다. 본 연구는 IKONOS 영상에서 도로 경계 검출을 위한 고주파 와 저주파 필터링 비교분석에 관한 연구이다. 분석결과 저주파 필터링과 고주파 필터링은 입력영상의 경계부분에서 영상을 선택적으로 강조할 수 있었다. 저주파 필터링과 같은 영상강화 기법에서는 추출 가능한 경계부의 위치를 변화시키거나 영상의 화소값이 전체영상을 대상으로 변화시켜 비교적 도로 폭이 넓은 경우 효과적이었다. 고주파 필터링은 세부적인 영상정보를 선택적으로 강조할 수 있었다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3D
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• pp.535-540
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• 2006

This paper presented an effective strategy to extract the buildings and to reconstruct 3-D buildings using high-resolution multispectral stereo satellite images. Proposed scheme contained three major steps: building enhancement and segmentation using both BDT (Background Discriminant Transformation) and ISODATA algorithm, conjugate building identification using the object matching with Hausdorff distance and color indexing, and 3-D building reconstruction using photogrammetric techniques. IKONOS multispectral stereo images were used to evaluate the scheme. As a result, the BDT technique was verified as an effective tool for enhancing building areas since BDT suppressed the dominance of background to enhance the building as a non-background. In building recognition, color information itself was not enough to identify the conjugate building pairs since most buildings are composed of similar materials such as concrete. When both Hausdorff distance for edge information and color indexing for color information were combined, most segmented buildings in the stereo images were correctly identified. Finally, 3-D building models were successfully generated using the space intersection by the forward RFM (Rational Function Model).

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.10a
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• pp.247-248
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• 2008

본 연구는 IKONOS 입체 위성영상에서 정합시간 단축을 위한 인터벌 정합방법을 제안하였다. 그 결과, 산림지역을 제외한 나머지 지역에서 인터벌을 주지 않고 정합한 경우와 큰 차이를 보이지 않았다.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.1
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• pp.83-91
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• 2012

Crown density, which is defined as the proportion of the forest floor concealed by tree crown, is important and useful information in various fields. Previous methods of measuring crown density have estimated crown density by interpreting aerial photographs or through a ground survey. These are time-consuming, labor-intensive, expensive and inconsistent approaches, as they involve a great deal of subjectivity and rely on the experience of the interpreter. In this study, the crown density of a forest in Korea was estimated using KOMPSAT-2 high-resolution satellite images. Using the image segmentation technique and stand information of the digital forest map, the forest area was divided into zones. The crown density for each segment was determined using the discriminant analysis method and the forest ratio method. The results showed that the accuracy of the discriminant analysis method was about 60%, while the accuracy of the forest ratio method was about 85%. The probability of extraction of candidate to update was verified by comparing the result with the digital forest map.

• Korean Journal of Remote Sensing
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• v.30 no.6
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• pp.817-827
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• 2014

In the satellite remote sensing, the operational environment of the satellite sensor causes image degradation during the image acquisition. The degradation results in noise and blurring which badly affect identification and extraction of useful information in image data. This study proposes a maximum a posteriori (MAP) estimation using Point-Jacobian iteration to restore a degraded image. The proposed method assumes a Gaussian additive noise and Markov random field of spatial continuity. The proposed method employs a neighbor window of spoke type which is composed of 8 line windows at the 8 directions, and a boundary adjacency measure of Mahalanobis square distance between center and neighbor pixels. For the evaluation of the proposed method, a pixel-wise classification was used for simulation data using various patterns similar to the structure exhibited in high resolution imagery and an unsupervised segmentation for the remotely-sensed image data of 1 mspatial resolution observed over the north area of Anyang in Korean peninsula. The experimental results imply that it can improve analytical accuracy in the application of remote sensing high resolution imagery.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.11a
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• pp.192-195
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• 2010

영상 정보의 정확성을 필요로 하는 다양한 서비스 및 여러 학문 분야의 적용성으로 인해 물리적인 한계성을 극복할 수 있는 고해상도 영상처리 기법의 요구가 대두되고 있다 뿐만 아니라, 인터넷 상의 디지털 콘텐츠 중의 하나인 동영상 UCC의 제작환경이 보편화됨으로써 비전문적인 제작으로 인한 다양한 형태의 해상도 저하 및 영상의 왜곡 현상이 발생하여 고품질의 영상을 추구하는 사용자들의 요구를 충족시키지 못하고 있다. 이러한 문제점을 해결하기 위한 압축 동영상의 개선 된 영상 정보를 획득하기 위한 연구가 이루어지고 있지만 다음과 같은 문제점이 있다. 기존의 방법은 일반적으로 저역 통과 필터 기법과 정규화 영상 복원 방식으로 구분되어 연구되고 있으며, 저역 통과 기법은 계산량 측면에서 장점이 있으나, 영상과 양자화 정도에 따라 적응적이지 못한 단점이 있다. 또한 정규화 복원 방식은 압축 영상의 시각적으로 불편한 현상의 완화 정도를 결정하는 정규화 매개변수를 일괄적으로 모든 화소에 적용해, 화소의 위치에 대한 적응도가 낮고 정규화 매개 변수 선택 시 원 영상에 대한 정보를 알고 있다는 가정을 사용했으므로 실제 사용이 불가능하며, 영상들 사이의 불균일로 인해 적응적으로 처리하지 못하는 단점이 있다. 본 연구에서는 이러한 문제점을 극복할 수 있도록 압축 정보 활용을 통한 POCS 방식을 사용한 coding artifact 제거 방식에 대해 기술한다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.790-792
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• 2004

본 논문은 PCM(Possibilistic C-Means) 알고리즘과 베이시안 분류 알고리즘을 통합한 고해상도 위성영상의 효과적인 분류방법을 제안하였다. 제안된 알고리즘은 학습데이터를 참고로 하여 PCM 알고리즘을 반복적인 과정 없이 수행한다. 각 분류항목별로 분류된 데이터에서 평균내부거리 내부에 해당되는 데이터들을 선정하여 각 항목별 비율을 구한 후 베이시안 분류기법의 사전확률로 적용하여 분류를 수행한다 PCM 알고리즘은 각 데이터와 특정 클러스터와의 거리에 소속도를 부여하는 퍼지 C-Means 알고리즘과 달리 소속도를 각 데이터와 클러스터 중심간의 절대거리에 의존하는 방법으로 퍼지 C-Means 알고리즘이 가지는 상대성 문제를 해결하였다. 제안된 분류 기법을 고해상도 다중분광 데이터인 IKONOS 위성영상에 적용하여 분류를 수행한 후 최대우도 분류기법과 비교한다.