• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.4
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• pp.14-23
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• 2005

This paper proposes a method for matching two different images, especially satellite images. In the general image matching fields, when an image is compared to other image, they may have different properties on the size, contents, brightness, etc. If there is no noise in each image, in other words, they have identical pixel level and unchanged edges, the image matching method will be simple comparison between two images with pixel by pixel. However, in many applications, most of images to be matched should have much different properties. This paper proposes an efficient method for matching satellite images. This method is to match a raw satellite image with GCP chips. From this we can make a geometrically corrected image. The proposed method is based on wavelet transformation, not required any pre-processing such as histogram equalization, analysis of raw image like the traditional methods.

• Korean Journal of Environment and Ecology
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• v.32 no.6
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• pp.686-697
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• 2018

Due to the advance in remote sensing technology, it has become easier to more frequently obtain high resolution imagery to detect delicate changes in an extensive area, particularly including forest which is not readily sub-classified. Time-series analysis on high resolution images requires to collect extensive amount of ground truth data. In this study, the potential of land coverage mapas ground truth data was tested in classifying high-resolution imagery. The study site was Wonju-si at Gangwon-do, South Korea, having a mix of urban and natural areas. KOMPSAT-3A imagery taken on March 2015 and land coverage map published in 2017 were used as source data. Two pixel-based classification algorithms, Support Vector Machine (SVM) and Random Forest (RF), were selected for the analysis. Forest only classification was compared with that of the whole study area except wetland. Confusion matrixes from the classification presented that overall accuracies for both the targets were higher in RF algorithm than in SVM. While the overall accuracy in the forest only analysis by RF algorithm was higher by 18.3% than SVM, in the case of the whole region analysis, the difference was relatively smaller by 5.5%. For the SVM algorithm, adding the Majority analysis process indicated a marginal improvement of about 1% than the normal SVM analysis. It was found that the RF algorithm was more effective to identify the broad-leaved forest within the forest, but for the other classes the SVM algorithm was more effective. As the two pixel-based classification algorithms were tested here, it is expected that future classification will improve the overall accuracy and the reliability by introducing a time-series analysis and an object-based algorithm. It is considered that this approach will contribute to improving a large-scale land planning by providing an effective land classification method on higher spatial and temporal scales.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.417-419
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• 1998

캘리브레이션이란 회전요소, 이동요소, 크기요소 값을 이용하여 한 영상을 변형시킴으로서 두 영상간의 부분적으로 동일한 영역이 오버랩 되도록 하는 작업을 말한다. 본 논문에서는 두 영상의 캘리브레이션을 위한 하나의 방법으로서 히스토그램을 작성하고, 이를 토대로 설정된 임의의 그레이 레벨에 해당하는 화소들은 seed로 하여 영역확장을 수행한 후, 대응하는 영역사이에 매칭을 행함으로서 회전요소 값, 이동요소 값, 크기요소 값을 구한다. 또한 실험을 텅해 얻은 각 요소값과 실제값을 비교함으로서 본 알고리즘의 성능을 분석한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.11a
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• pp.363-366
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• 2009

변이영상은 두 스테레오 영상의 시차에 의해 발생하는 각 화소의 변위를 수록한 영상이다. 이 영상은 깊이영상을 생성하여 시점 간 가상영상을 생성하는데 사용된다. 따라서 변이영상은 다시점 비디오 서비스와 직접적인 연관이 있다. 본 논문에서는 유일성(uniqueness)제약과 순차성(ordering) 제약을 사용하여 기준영상과 참조영상 사이의 관계를 이용하여 생성한 변이 공간 영상(DSI : disparity space image)으로부터 비용 행렬을 계산하여 최적의 변이 경로를 찾아가는 다이내믹 프로그래밍을 분석 하였다. 다이내믹 프로그래밍은 정밀한 변이 맵을 얻을 수 있고, 다른 방식들에 비해 연산 속도가 빠르다는 장점을 가지고 있지만, 영상의 화소값의 변화가 없는 영역에서 이전의 경로를 계속 유지하려는 성질에 때문에 발생 하는 오류확산과 가려진 (occluded) 영역에 의한 오차로 인해 정확한 경로를 찾을 수 없는 경우가 빈번히 발생 하여 에러율이 높아지는 단점을 가지고 있다. 이러한 이론을 토대로 기존의 기법들에 비하여 정확도가 우수한 기법들을 제안하였다. 개선된 역 추적 과정을 이용하여 기존의 다이내믹 프로그래밍 기반의 스테레오 정합 기법들보다 우수성이 뛰어난 결과들을 나타내었다.

• Journal of Korean Society of Forest Science
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• v.100 no.4
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• pp.540-547
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• 2011

This study was conducted to compare classification accuracy by tree species using QuickBird imagery for pixel- and segment-based classifications that have been mostly applied to classify land covers. A total of 398 points was used as training and reference data. Based on this points, the points were classified into fourteen land cover classes: four coniferous and seven deciduous tree species in forest classes, and three non-forested classes. In pixel-based classification, three images obtained by using raw spectral values, three tasseled indices, and three components from principal component analysis were produced. For the both classification processes, the maximum likelihood method was applied. In the pixel-based classification, it was resulted that the classification accuracy with raw spectral values was better than those by the other band combinations. As resulted that, the segment-based classification with a scale factor of 50% provided the most accurate classification (overall accuracy:76% and ${\hat{k}}$ value:0.74) compared to the other scale factors and pixel-based classification.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.400-407
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• 2019

In this paper, the shadow detection and reconstruction method are proposed using intrinsic image, which does not change the essential characteristics under the influence of various illuminance, and multi-scale gamma correction. The shadow detection was estimated by the pixel change information between a grayscale and an intrinsic image of the color image, and the brightness of the image were adjusted by gamma correction in the shadow restoration process. Multi-scale gamma correction is performed for each channel of a color image due to the fact that the saturation can be changed by nonlinear adjustment to individual pixel values. Multi-scale gamma values are estimated based on the information of the crossed edge between shadows and non-shadowed regions in the color image, as a result, the shadows are reconstructed by correcting different region features with multi-scale gamma values. Experimental results show that the proposed method effectively reconstructs shadows in a single natural image.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.9
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• pp.2043-2052
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• 1997

The object-oriented coding technique which is one of the coding methods in very low bit rate environment is suitable for videophone image sequence. The selection of source model affect image analysis. In this paper, an image analysis method for the object-oriented coding is presented. The process is composed of changed region detection andmotion estimateion. First, we use the standard deviation of frame difference as thrreshold to extract themoving area. If thesum of gray values in mask is greater than the threshold, the center pixel of the mask is regarded as moving region. After moving is detected in changed region by edge operator, observation point is determined from moving region. The motion is estimated by 6-parameter mapping method with determined observation point. The experimantal resutls show that the proposed method can significantly improve the image quality.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.4C
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• pp.424-432
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• 2003

In this paper, an effective method of stereo image reconstruction through the regularized adaptive disparity estimation is proposed. Althougth the conventional adaptive disparity estimation method can sharply improve the PSNR of a reconstructed stereo image, but some problems of overlapping between the matching windows and disallocation of the matching windows can be occurred, because the matching window size changes adaptively in accordance with the magnitude of feature values. Accordingly, in thia paper, a new regularized adaptive disparity estimation technique is proposed. That is, by regularizing the estimated disparity vector with the neughboring disparity vectors, problems of the conventional adaptive disparity estimated scheme might be solved, and also the predicted stereo image can be more effectively reconstructed. From some experiments using the CCETT'S stereo image pairs of 'Man' and 'Claude', it is analyzed that the proposed disparity estimation scheme can improve PSNRs of the reconstructed images to 10.89dB, 6.13dB for 'Man' and 1.41dB, 0.81dB for 'Claude' by comparing with those of the conventional pixel-based and adaptive estimation method, respectively.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.177-185
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• 2022

A method of restoring the occluded area was proposed by referring to images taken with the same types of sensors on high-resolution optical satellite images through deep learning. For the natural continuity of the simulated image with the occlusion region and the surrounding image while maintaining the pixel distribution of the original image as much as possible in the patch segmentation image, CycleGAN (Cycle Generative Adversarial Network) method with ConvNeXt block applied was used to analyze three experimental regions. In addition, We compared the experimental results of a training patch size of 512*512 pixels and a 1024*1024 pixel size that was doubled. As a result of experimenting with three regions with different characteristics,the ConvNeXt CycleGAN methodology showed an improved R² value compared to the existing CycleGAN-applied image and histogram matching image. For the experiment by patch size used for training, an R² value of about 0.98 was generated for a patch of 1024*1024 pixels. Furthermore, As a result of comparing the pixel distribution for each image band, the simulation result trained with a large patch size showed a more similar histogram distribution to the original image. Therefore, by using ConvNeXt CycleGAN, which is more advanced than the image applied with the existing CycleGAN method and the histogram-matching image, it is possible to derive simulation results similar to the original image and perform a successful simulation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.6_2
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• pp.587-597
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• 2007

This study determines matching size for digital elevation model (DEM) production according to land cover property from IKONOS Geo-level stereo image. We applied area based matching method using correlation coefficient of pixel brightness value between the two images. After matching line (where "matching line" implies straight line that is approximated to complex non-linear epipolar geometry) is established by exterior orientation parameters to minimize search area, the matching is carried out based on this line. The experiment is performed according to land cover property, which is divided off into four areas (water, urban land, forest land and agricultural land). In each of the test areas, matching size is selected using a correlation-coefficient image and parallax image. As the results, optimum matching size of the images was selected as $81{\times}81$ pixels window, $21{\times}21$ pixels window, $119{\times}119$ pixels window and $51{\times}51$ pixels window in the water area, urban land, forest land and agricultural land, respectively.