• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.351-361
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• 2024

Satellite imagery contains various elements such as clouds, cloud shadows, and terrain shadows. Accurately identifying and eliminating these factors that complicate satellite image analysis is essential for maintaining the reliability of remote sensing imagery. For this reason, satellites such as Landsat-8, Sentinel-2, and Compact Advanced Satellite 500-1 (CAS500-1) provide Usable Data Masks(UDMs)with images as part of their Analysis Ready Data (ARD) product. Precise detection of clouds and their shadows is crucial for the accurate construction of these UDMs. Existing cloud and their shadow detection methods are categorized into threshold-based methods and Artificial Intelligence (AI)-based methods. Recently, AI-based methods, particularly deep learning networks, have been preferred due to their advantage in handling large datasets. This study aims to analyze the applicability of constructing UDMs for high-resolution satellite images through deep learning-based cloud and their shadow detection using open-source datasets. To validate the performance of the deep learning network, we compared the detection results generated by the network with pre-existing UDMs from Landsat-8, Sentinel-2, and CAS500-1 satellite images. The results demonstrated that high accuracy in the detection outcomes produced by the deep learning network. Additionally, we applied the network to detect cloud and their shadow in KOMPSAT-3/3A images, which do not provide UDMs. The experiment confirmed that the deep learning network effectively detected cloud and their shadow in high-resolution satellite images. Through this, we could demonstrate the applicability that UDM data for high-resolution satellite imagery can be constructed using the deep learning network.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.989-998
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• 2021

In the utilization of optical satellite imagery, which is greatly affected by clouds, periodic composite technique is a useful method to minimize the influence of clouds. Recently, a technique for selecting the optimal pixel that is least affected by the cloud and shadow during a certain period by directly inputting cloud and cloud shadow information during period compositing has been proposed. Accurate extraction of clouds and cloud shadowsis essential in order to derive optimal composite results. Also, in the case of an surface targets where spectral information is important, such as crops, the loss of spectral information should be minimized during cloud-free compositing. In thisstudy, clouds using two spectral indicators (Haze Optimized Tranformation and MeanVis) were used to derive a detection technique with low loss ofspectral information while maintaining high detection accuracy of clouds and cloud shadowsfor cabbage fieldsin the highlands of Gangwon-do. These detection results were compared and analyzed with cloud and cloud shadow information provided by Sentinel-2A/B. As a result of analyzing data from 2019 to 2021, cloud information from Sentinel-2A/B satellites showed detection accuracy with an F1 value of 0.91, but bright artifacts were falsely detected as clouds. On the other hand, the cloud detection result obtained by applying the threshold (=0.05) to the HOT showed relatively low detection accuracy (F1=0.72), but the loss ofspectral information was minimized due to the small number of false positives. In the case of cloud shadows, only minimal shadows were detected in the Sentinel-2A/B additional layer, but when a threshold (= 0.015) was applied to MeanVis, cloud shadowsthat could be distinguished from the topographically generated shadows could be detected. By inputting spectral indicators-based cloud and shadow information,stable monthly cloud-free composited vegetation index results were obtained, and in the future, high-accuracy cloud information of Sentinel-2A/B will be input to periodic cloud-free composite for comparison.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.199-201
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• 2004

모바일 장치에서 많이 사용되는 플래시 메모리는 작고, 저전력을 사용하며 내구성을 지니는 비휘발성 저장장치이다. 플래시 메모리의 읽기 속도는 램과 비슷하며, 대용량화 되어가고 있지만 쓰기 속도가 램에 비해 느리고, 블록에 대한 쓰기가 제한되어 있다. 현재의 디스크 기반의 DBMS 와는 달리 플래시 메모리용 저장장치를 설계함에 있어 트랜잭션 실패시의 회복기법이 같은 블록에 다시 쓰기가 불가능한 플래시 메모리의 특성을 고려하는 것이 중요하다. 본 연구에서 LFS에 Shadow Paging을 응용하여 플래시 메모리의 블록에 대한 쓰기 횟수를 줄이고 플래시 메모리의 특성에 맞추어 트랜잭션 실패시 효율적인 데이터 복구를 가능하게 하는 회복기법을 제안한다.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.635-638
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• 1999

도로 위에 설치된 카메라에서 검지 영역의 데이터를 입력받아 통과 차량수, 도로 점유율, 차량속도, 차간 거리 등의 교통정보를 실시간으로 산출하는 기법은 지능형 교통정보 시스템(ITS)의 핵심 분야이다. 본 논문에서는 검지영역의 시공간 영상 분석에 의해 다양한 기상 조건과 그림자 등의 환경의 변화에 민감하지 않은 교통정보 산출기법을 제안한다.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.115-117
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• 2002

메모리 기술의 발전은 주기억장치 데이터베이스가 널리 사용될 수 있는 토대를 마련하였다. 주기억장치 데이터베이스에서는 주기억장치의 휘발성으로 인해 시스템이 파손될 경우 모든 데이터들을 잃어버리게 되므로 데이터베이스를 일관된 상태로 복구시켜주는 회복기법이 매우 중요하다. 본 논문에서는 비휘발성 메모리이면서도 RAM과 같이 빠른 접근속도를 가지는 FRAM과 같은 고성능 비휘발성 메모리를 이용하여 그림자 갱신과 로그 기반 백업을 실시함으로써 시스템 파손 후, 마른 복구를 제공하면서도 기존의 로그 기반 백업이 가지는 문제점을 해결하는 회복기법을 제안한다.

• Journal of the Korea Computer Graphics Society
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• v.6 no.4
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• pp.9-14
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• 2000

Non-Photorealistic rendering techniques, such as toon rendering, can enhance the quality of hand-drawn cell-animation images greatly with less effort. For this reason, to on rendering is one of the popular techniques used in the cell-animation image production field. The existing toon rendering techniques, however, have not been effective enough for the real-time image processing that the techniques have not been adequate for some processes that needs immediate responses such as virtual-realities, or video games. This paper will suggest the real-time toon rendering to overcome the limits through real-time outline detection and phong shading. In addition, a effective result-image is created as adding a shadow and a execution time remains by real-time through fast shadow generation algorithm.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.989-1006
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• 2020

Building change monitoring based on building detection is one of the most important fields in terms of monitoring artificial structures using high-resolution multi-temporal images such as CAS500-1 and 2, which are scheduled to be launched. However, not only the various shapes and sizes of buildings located on the surface of the Earth, but also the shadows or trees around them make it difficult to detect the buildings accurately. Also, a large number of misdetection are caused by relief displacement according to the azimuth and elevation angles of the platform. In this study, object-based building detection was performed using the azimuth angle of the Sun and the corresponding main direction of shadows to improve the results of building change detection. After that, the platform's azimuth and elevation angles were used to detect changed buildings. The object-based segmentation was performed on a high-resolution imagery, and then shadow objects were classified through the shadow intensity, and feature information such as rectangular fit, Gray-Level Co-occurrence Matrix (GLCM) homogeneity and area of each object were calculated for building candidate detection. Then, the final buildings were detected using the direction and distance relationship between the center of building candidate object and its shadow according to the azimuth angle of the Sun. A total of three methods were proposed for the building change detection between building objects detected in each image: simple overlay between objects, comparison of the object sizes according to the elevation angle of the platform, and consideration of direction between objects according to the azimuth angle of the platform. In this study, residential area was selected as study area using high-resolution imagery acquired from KOMPSAT-3 and Unmanned Aerial Vehicle (UAV). Experimental results have shown that F1-scores of building detection results detected using feature information were 0.488 and 0.696 respectively in KOMPSAT-3 image and UAV image, whereas F1-scores of building detection results considering shadows were 0.876 and 0.867, respectively, indicating that the accuracy of building detection method considering shadows is higher. Also among the three proposed building change detection methods, the F1-score of the consideration of direction between objects according to the azimuth angles was the highest at 0.891.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.1
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• pp.127-136
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• 2006

Shaded relief is used in the analysis of digital terrain model, but accurate shadow zone has not been affirmed on account of idea only shadow of terrain that would be in shadow are shaded. This study is to analyze each display difference of a digital terrain model by grasping the shadow characteristics of terrain and comparing shaded relief function used terrain display of GIS with a rendering technique. After terrain with road in subject area is selected and created to digital terrain model of TIN, shaded relief and rendering according to altitude and azimuth of the sun at 9:00 am and 3:00 pm is applied. As the results, only backward portions of the terrain that is in shadow from the sunlight are shaded in case of shaded relief. The rendering created the shadow, which is cast by terrain features. By these mutual comparison, this study represented data for understanding of shaded relief. And it is expected that the rendering method could be used to analyze sunshine influence.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1892-1897
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• 2017

This paper proposes a finger counting algorithm for a hand with stuck fingers. The proposed algorithm is based on the fact that straight line type shadows are inevitably generated between fingers. It divides the hand region into the thumb region and the four fingers region for effective shadow detection, and generates an edge image in each region. Projection curves are generated by appling a line detection and a projection technique to each edge image, and the peaks of the curves are detected as candidates for finger shadows. And then peaks due to finger shadows are extracted from them and counted. In the finger counting experiment on hand images expressing various shapes with stuck fingers, the counting success rate is from 83.3% to 100% according to the number of fingers, and 93.1% on the whole. It also shows that if hand images are generated under controlled conditions, the failure cases can be sufficiently improved.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06a
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• pp.277-278
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• 2008