• Spatial Information Research
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• v.7 no.1
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• pp.89-101
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• 1999

This paper represents methods to calculate heights of buildings by estimating their shadow lengths in a single and panchromatic image of the KVR-1000 camera system Shadows are identified Com brightness intensity of each pixel, and their lengths are measured. Two methods are implemented to estimate heights from shadows. One method is to use a ratio of shadow s lengths with respect to heights of reference buildings measured on site. The other method uses sun elevation angles calculated from various camera s parameters at the exposure time. The estimated heights of 20 buildings are compared with heights measured on site, and the RMS errors for each method are 1.70m and 1.75m, respectively. When a resampling method to enhance identification of shadows is used and their lengths are accordingly re-calculated, the estimated errors for each method are significantly reduced to 1.17m and 1.16m, respectively. Meanwhile, effects of land slope on shadows can be hardly obtained unless detailed DTM(digital terrain model) are available, and they introduce additional errors up to 25m.

• Solar Energy
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• v.13 no.2_3
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• pp.37-44
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• 1993

건물의 외부에 차양을 설치하는 것은 건물이 갖는 냉방부하를 줄일 수 있는 중요한 요인이 된다. 외부차양의 효율성은 차양의 형태, 크기, 태양의 경로, 건물의 방향등과 같은 요소에 의해 결정 지어진다. 그러나, 이러한 요소들이 서로 동적으로 상호 관련 되어 있기 때문에 외부차양에 의한 그림자 투영은 예측하기 힘들고, 따라서 냉방부하가 얼마만큼 감소되는지는 더욱더 예측이 어려워진다. 이 논문은 디자이너가 직접 외부차양을 컴퓨터 그래픽으로 디자인 함과 동시에 그림자 투영을 시각화 할 수 있는 프로그램 개발을 위한 연구이다.

• 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.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.621-626
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• 2006

Today's commercial high resolution satellite imagery such as IKONOS and QuickBird, offers the potential to extract useful spatial information for geographical database construction and GIS applications. Extraction of 3D building information from high resolution satellite imagery is one of the most active research topics. There have been many previous works to extract 3D information based on stereo analysis, including sensor modelling. Practically, it is not easy to obtain stereo high resolution satellite images. On single image performance, most studies applied the roof-bottom points or shadow length extracted manually to sensor models with DEM. It is not suitable to apply these algorithms for dense buildings. We aim to extract 3D building information from a single satellite image in a simple and practical way. To measure as many buildings as possible, in this paper, we suggested a new way to extract building height by triangular vector structure that consists of a building bottom point, its corresponding roof point and a shadow end point. The proposed method could increase the number of measurable building, and decrease the digitizing error and the computation efficiency.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.2
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• pp.47-54
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• 2012

Additional high resolution satellite images, other period or site, are essential for efficient city modeling and analysis. However, the same ground objects have a radiometric inconsistency in different satellite images and it debase the quality of image processing and analysis. Moreover, in an urban area, buildings, trees, bridges, and other artificial objects cause shadow effects, which lower the performance of relative radiometric normalization. Therefore, in this study, we exclude shadow areas and suggest the selective histogram matching methods for image based application without supplementary digital elevation model or geometric informations of sun and sensor. We extract the shadow objects first using adjacency informations with the building edge buffer and spatial and spectral attributes derived from the image segmentation. And, Outlier objects like a asphalt roads are removed. Finally, selective histogram matching is performed from the shadow masked multi-temporal Quickbird-2 images.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.643-645
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• 2003

위성사진에서 그림자 영역을 추출하고 그 화질을 개선하는 것은 이 분야에서 생길 수 있는 다양한 응용 때문에 관심있는 연구분야로 떠오르고 있다. 이 논문은 한국의 위성사진에서 흔히 볼 수 있는 아파트와 같은 건물이 있는 도시의 화상에서 그림자 영역을 자동 추출하는 방법을 제안하고 있다. 제안된 알고리듬은 공간영역에서 히스토그램의 특성을 이용하고 주파수영역에서 고속푸리에변환과 코릴레이션을 이용한다. 실험결과는 이 방법이 효과적임을 보여준다.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.303-306
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• 2002

인공위성영상을 이용하여 벡터 지도 생성은 지형에 따른 건물, 도로, 농지 등에 관한 벡터를 추출하는 작업이 필요하다. 이 작업의 정확도는 지도의 정확도와 상관관계가 있기 때문에 건물 추출의 정확성이 달라진다. 따라서 건물추출의 정확성을 향상시키기 위해 본 연구에서는 위성영상에서 건물 추출 알고리즘을 제안하였다. 이 알고리즘은 인공신경망을 이용하여 건물의 그림자를 추적하고 이를 중심으로 건물위치와 외형을 추정하는 알고리즘을 제안하고 실험하였으며, 양호한 결과를 얻었다.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.93-98
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• 2013

Multi-temporal high resolution satellite images are essential data for efficient city analysis and monitoring. Yet even when acquired from the same location, identical sensors as well as different sensors, these multi-temporal images have a geometric inconsistency. Matching points between images, therefore, must be extracted to match the images. With images of an urban area, however, it is difficult to extract matching points accurately because buildings, trees, bridges, and other artificial objects cause shadows over a wide area, which have different intensities and directions in multi-temporal images. In this study, we analyze a shadow effect on image matching of high resolution satellite images in urban area using Scale-Invariant Feature Transform(SIFT), the representative matching points extraction method, and automatic shadow extraction method. The shadow segments are extracted using spatial and spectral attributes derived from the image segmentation. Also, we consider information of shadow adjacency with the building edge buffer. SIFT matching points extracted from shadow segments are eliminated from matching point pairs and then image matching is performed. Finally, we evaluate the quality of matching points and image matching results, visually and quantitatively, for the analysis of shadow effect on image matching of high resolution satellite image.

• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.499-508
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• 2013

This article presents a new method for semi-automatic extraction of building information (height, shape, and footprint location) from monoscopic urban scenes. The proposed method is to expand Semi-automatic Volumetric Shadow Analysis (SVSA), which can handle occluded building footprints or shadows semi-automatically. SVSA can extract wrong building information from a single high resolution satellite image because SVSA is influenced by extracted shadow area, image noise and objects around a building. The proposed method can reduce the disadvantage of SVSA by using multi-spectral images. The proposed method applies SVSA to panchromatic and multi-spectral images. Results of SVSA are used as parameters of a cost function. A building height with maximum value of the cost function is determined as actual building height. For performance evaluation, building heights extracted by SVSA and the proposed method from Kompsat-2 images were compared with reference heights extracted from stereo IKONOS. The result of performance evaluation shows the proposed method is a more accurate and stable method than SVSA.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.3
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• pp.556-563
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• 2020

Most of a BIPVS (Building Integrated Photovoltaic System) is installed on the rooftop or wall of a building. Therefore, the main factor to consider for selecting the installation site is the shadow effects produced by the surrounding buildings. On the other hand, when the photovoltaic was installed on soundproof walls, shadow effects were produced by not only surrounding buildings but also the surrounding trees. Therefore, a different data model and algorithm with the BIPVS case are essential for proper installation sites selection of a SIPVS (Soundproof wall Integrated Photovoltaic System). This paper deals with the DSM (Digital Surface Model)-based proper installation site analysis for SIPVS. First, the solar incident and altitude angles of the installation candidate sites (solar panel) during the year were calculated. Second, the shadow effects (shadowed or unshadowed) were determined for the candidate sites at each time with the DSM. Third, the amount of solar radiation was calculated with the incident angle for the candidate sites at an unshadowed period. The proper installation site of the SIPVS could then be selected by comparing the accumulated annual solar radiation for each candidate. The proposed algorithm was implemented as a prototype (Java program). From the experiment, the order of the installation suitability was determined among the nine candidates. The proposed algorithm could be used for proper BIPVS installation site analysis aimed at the lower part of a building and calculation of the expected power production.