• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.453-454
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• 2003

Most research materials (data), which are used for the study of digital mapping and digital elevation model (DEM) in the field of Remote Sensing and Aerial Photogrammetry are aerial photographs and satellite images. Additionally, they are also used for National land mapping, National land management, environment management, military purposes, resource exploration and Earth surface analysis etc. Although aerial photographs have high resolution, the data, which they contain, are not used for environment exploration that requires continuous observation because of problems caused by its coastline, as well as single - spectral and long-term periodic image. In addition to this, they are difficult to interpret precisely because Satellite Images are influenced by atmospheric phenomena at the time of photographing, and have by far much lower resolution than existing aerial photographs, while they have a great practical usability because they are mulitispectral images. The PKNU 2 is an aerial photographing system that is made to compensate with the weak points of existing aerial photograph and satellite images. It is able to take pictures of very high resolution using a color digital camera with 6 million pixels and a color infrared camera, and can take perpendicular photographs because PKNU 2 system has equipment that makes the cameras stay level. Moreover, it is very cheap to take pictures by using super light aircraft as a platform. It has much higher resolution than exiting aerial photographs and satellite images because it flies at a low altitude about 800m. The PKNU 2 can obtain multispectral images of visible to near infrared band so that it is good to manage environment and to make a classified diagram of vegetation.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2003년도 추계학술발표회 논문집
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• pp.147-152
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• 2003

Aerial Images are usually used to extract 3-D coordinates of various urban features. In this process, the stereo matching of images should be performed precisely to extract these information from aerial Images. In this research, we proposed a matching technique based on geometric features of lanes. We extracted lanes from aerial images and grouped into 4 lane's types. They are lane lines, dotted lines, arrow lane, safety zone. After preprocessing, We will match them by spatial relationships, for example, the distance and orientation between the extracted features. In the future, we will obtain lane coordinates and reconstruct 3-d coordinates of roads.

• Journal of Ecology and Environment
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• 제41권4호
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• pp.114-119
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• 2017

This study tried to analyze error range and resolution of drone images using a rotary wing by comparing them with field measurement results and to analyze stands patterns in actual vegetation map preparation by comparing drone images with aerial images provided by National Geographic Information Institute of Korea. A total of 11 ground control points (GCPs) were selected in the area, and coordinates of the points were identified. In the analysis of aerial images taken by a drone, error per pixel was analyzed to be 0.284 cm. Also, digital elevation model (DEM), digital surface model (DSM), and orthomosaic image were abstracted. When drone images were comparatively analyzed with coordinates of ground control points (GCPs), root mean square error (RMSE) was analyzed as 2.36, 1.37, and 5.15 m in the direction of X, Y, and Z. Because of this error, there were some differences in locations between images edited after field measurement and images edited without field measurement. Also, drone images taken in the stream and the forest and 51 and 25 cm resolution aerial images provided by the National Geographic Information Institute of Korea were compared to identify stands patterns. To have a standard to classify polygons according to each aerial image, image analysis software (eCognition) was used. As a result, it was analyzed that drone images made more precise polygons than 51 and 25 cm resolution images provided by the National Geographic Information Institute of Korea. Therefore, if we utilize drones appropriately according to characteristics of subject, we can have advantages in vegetation change survey and general monitoring survey as it can acquire detailed information and can take images continuously.

• 한국멀티미디어학회논문지
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• 제25권5호
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• pp.685-694
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• 2022

The goal of few-shot semantic segmentation is to build a network that quickly adapts to novel classes with extreme data shortage regimes. Most existing few-shot segmentation methods leverage single or multiple prototypes from extracted support features. Although there have been promising results for natural images, these methods are not directly applicable to the aerial image domain. A key factor in few-shot segmentation on aerial images is to effectively exploit information that is robust against extreme changes in background and object scales. In this paper, we propose a Mask-Guided Attention module to extract more comprehensive support features for few-shot segmentation in aerial images. Taking advantage of the support ground-truth masks, the area correlated to the foreground object is highlighted and enables the support encoder to extract comprehensive support features with contextual information. To facilitate reproducible studies of the task of few-shot semantic segmentation in aerial images, we further present the few-shot segmentation benchmark iSAID-, which is constructed from a large-scale iSAID dataset. Extensive experimental results including comparisons with the state-of-the-art methods and ablation studies demonstrate the effectiveness of the proposed method.

• 전기전자학회논문지
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• 제12권1호
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• pp.51-58
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• 2008

This paper presents a new method for building detection and reconstruction from aerial images. In our approach, we extract the useful building location information from the generated disparity map to obtain the segmentation of interested objects and thus reduce significantly unnecessary line segment extracted in low level feature extraction step. Hypothesis selection is carried out by using undirected graph in which close cycles represent complete rooftops hypotheses, and hypothesis are finally tested to contruct building model. We test the proposed method with synthetic images generated from Avenches dataset of Ascona aerial images. The experiment result shows that the extracted 3D line segments of the buildings can be efficiently used for the task of building detection and reconstruction from aerial images.

• Korean Journal of Geomatics
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• 제5권1호
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• pp.35-42
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• 2005

Precise geometric registration is required in multi-source data fusion process to obtain synergistic results successfully. However, most of the previous studies focus on the assumption of perfect registration or registration in a limited local area with intuitively derived simple geometric model. In this study, therefore, we developed a robust method for geometric registration based on a systematic model that is derived from the geometry associated with the data acquisition processes. The key concept of the proposed approach is to utilize smooth planar patches extracted from LIDAR data as control surfaces to adjust exterior orientation parameters of the aerial images. Registration of the simulated LIDAR data and aerial images was performed. The experimental results show that the RMS value of the geometric discrepancies between two data sets is decreased to less than ${\pm}0.30\;m$ after applying suggested registration method.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2003년도 춘계학술발표회 논문집
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• pp.187-190
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• 2003

Due to complexity of diverse features in urban area, accurate feature extraction is laborious task in aerial and satellite imagery. Especially occlusion by buildings, and image distortion of shadow effects make processing more difficult work. In this study, algorithm was presented to correct of shadow effects in aerial color images. This algorithm enables user to accurately interpretate urban information by correction of shadow effects in aerial color images

• 한국측량학회지
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• 제29권5호
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• pp.527-534
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• 2011

본 연구에서는 낙동강하구 을숙도 지역에 대하여 1983년부터 2007년까지 장기간의 고해상도 항공영상을 이용한 지형변화 분석을 최초로 실시하였다. 먼저, 과거 항공사진의 사진기준점 측량을 위해서는 과거부터 존재하고 있는 특정점에 대하여 GPS를 이용하여 지상기준점(GCP : Ground Control Point) 측량을 실시, 이후 이를 이용한 과거 항공영상의 사진기준점 작업 수행 시 1m 정도의 정확도를 얻을 수 있었다. 이후 사진기준점측량 결과를 바탕으로 정사영상을 제작하여 과거부터 최근까지의 지형변화에 대한 정량적 분석을 수행하였다. 이로부터 연구 대상지에 대한 각 연대별 변화요인에 따른 전체면적, 건설현장, 식생, 건물과 도로의 면적 변화량을 알 수 있었다. 전체 면적의 경우 1983년부터 1992년까지 감소하다가 이후 변화가 거의 일어나지 않았다. 개발이 진행됨에 따라 식생은 지속적으로 감소하였고, 건물과 도로의 경우 그 면적이 전반적으로 증가함을 알 수 있었다. 본 연구의 결과는 인위적, 자연적 영향으로 인하여 변화가 예상되는 을숙도와 낙동강 하구의 지형변화 모니터링을 위한 기초자료로 활용될 것이다.

• 한국측량학회지
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• 제19권2호
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• pp.109-115
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• 2001

최근 수치사진측량시스템의 도입으로 수치정사영상의 활용도가 높아지고 있으며, 이에 따라 수치정사영상으로부터 지형지물의 반자동 또는 자동 추출에 관한 연구가 대두되고 있지만, 최근 많은 연구보고서에 의하면 흑백항공사진에서의 지형지물을 판단하거나 속성인식에 한계가 있음을 알 수 있다. 본 연구에서는 천연색 항공사진 영상을 흑백항공사진 영상과 비교, 분석하여 천연색 항공사진 영상의 특성을 분석하였으며, 반자동으로 지형요소를 추출하는 알고리즘을 개발하였고, 추출한 지형요소를 국립지리원에서 제작하는 1:5,000 수치지형도와 비교하여 반자동으로 추출가능한 지형요소를 분석하였다. 이러한 결과를 토대로 천연색 항공사진영상을 이용한 지형요소 추출 가능성과 지형요소 추출시 문제점을 분석하였고, 향후 연구에 대한 방안을 제시하였다.

• 한국측량학회지
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• 제39권1호
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• pp.41-46
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• 2021

무인항공기를 이용한 데이터 취득이 널리 활용되면서 무인항공기를 이용한 사진측량의 정확도를 높일 수 있는 방안의 하나로 항공삼각측량의 번들 조정에 연직사진과 경사사진을 같이 사용하는 방법이 제시되고 있다. 본 연구에서는 사진측량의 정확도를 높이는 데 적합한 방법을 찾기 위해 촬영 각도를 달리하여 촬영한 경사사진을 조정하는 경우와 촬영 각도가 다른 경사사진을 연직사진과 동시에 조정하는 경우의 정확도를 비교하였다. 연구결과 입력되는 경사사진의 경사가 커질수록 검사점의 오차가 줄어드는 것으로 나타났으며, 특히 연직사진과 경사사진을 같이 사용할 때, 경사사진의 경사가 클수록 높이 오차가 크게 줄어드는 것으로 나타났다. 현행 『항공사진측량 작업규정』에서는 연직사진의 GSD (Ground Spatial Distance)와 동일한 RMSE (Root Mean Square Error)를 요구하고 있다. 촬영각도 50°의 경사사진을 이용할 때 이 기준에 거의 근접한 결과를 얻을 수 있었고, 연직사진과 50°의 경사사진을 동시에 조정한 경우 작업규정을 만족시킬 수 있었다. 본 연구 결과를 활용하면 무인항공기에 탑재된 저가의 사진기를 이용하는 사진측량이 더욱 활발해 질 수 있을 것으로 기대된다.