• 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 Korean Society for Geospatial Information Science
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• v.19 no.3
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• pp.49-56
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• 2011

Because urban areas change rapidly, it is necessary to reflect urban changes in a digital map database in a timely manner. To address these issues, LiDAR data was used to detect changes in urban area buildings. The purpose of this study is to detect object-based building change using LiDAR data and existing digital maps, and classify change types. In the study, we classified change type using overlay and shape comparison with building layer of the digital maps and point-based extracted building outline from the LiDAR data. When applying the overlay method, we were able to increase the accuracy and objectivity of the change detection process throughout an adaptive threshold applied to each object. In the experiments, it was demonstrated that classifying and detecting changes in urban areas using the proposed method can provide superior classification accuracy compared with the existing methodology.

• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.97-106
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• 2020

The factory information provided by the Industrial Location Information System (ILIS) is provided as raw data by the Korea Industrial Complex Corporation and registered after a filtering process, so the new factory information update is slow. In this study, to solve the problem of updating factory information of industrial location information system, using building data of road name address with relatively fast renewal cycle and building data of real estate, we compared the factory information of existing ILIS and extracted new factory information. In the process of comparison, a method was proposed to compare spatial objects of different types with point data and polygon data. Attribute information matching and object matching were performed, and attribute values of new factory information were extracted. The accuracy evaluation of the proposed spatial analysis method showed 79% accuracy, and the above matching technique was used to confirm the possibility of convergence of road name address data, real estate data and factory information of ILIS.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.223-232
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• 2023

A widely used method for monitoring land cover using high-resolution satellite images is to classify the images based on the colors of the objects of interest. In urban areas, not only major objects such as buildings and roads but also vegetation such as trees frequently appear in high-resolution satellite images. However, the colors of vegetation objects often resemble those of other objects such as buildings, roads, and shadows, making it difficult to accurately classify objects based solely on color information. In this study, we propose a method that can accurately classify not only objects with various colors such as buildings but also vegetation objects. The proposed method uses the normalized difference vegetation index (NDVI) image, which is useful for detecting vegetation objects, along with the RGB image and classifies objects into subclasses. The subclass classification results are fused, and the final classification result is generated by combining them with the image segmentation results. In experiments using Compact Advanced Satellite 500-1 imagery, the proposed method, which applies the NDVI and subclass classification together, showed an overall accuracy of 87.42%, while the overall accuracy of the subchannel classification technique without using the NDVI and the subclass classification technique alone were 73.18% and 81.79%, respectively.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.1 s.31
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• pp.19-25
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• 2005

Nowadays, it is possible to construct the DEMs of urban area effectively and economically by LiDAR system. But the data from LiDAR system has form of DSM which is included various objects as trees and buildings. So the preprocess is necessary to extract the DEMs from LiDAR DSMs for particular purpose as effects analysis of man-made objects for flood prediction. As this study is for extracting DEM from LiDAR DSM of urban area, we detected the edges of various objects using edge detecting algorithm of image process. And, we tried mean value filtering, median value filtering and minimum value filtering or detected edges instead of interpolation method which is used in the previous study and could be modified the source data. it could minimize the modification of source data, and the extracting process of DEMs from DSMs could be simplified and automated.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.1
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• pp.1-10
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• 2012

Recent spatial information technology has brought innovative improvement in both efficiency and accuracy. Especially, airborne LiDAR system(ALS) is one of the practical sensors to obtain 3D spatial information. Constructing reliable 3D spatial data infrastructure is world wide issue and most of the significant tasks involved with modeling manmade objects. This study aims to create a test data set for developing automatic building modeling methods by simulating point cloud data. The data simulates various roof types including gable, pyramid, dome, and combined polyhedron shapes. In this study, a robust bottom-up method to segment surface patches was proposed for generating building models automatically by determining model key points of the objects. The results show that building roofs composed of the segmented patches could be modeled by appropriate mathematical functions and the model key points. Thus, 3D digitizing man made objects could be automated for digital mapping purpose.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.131-134
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• 2006

LiDAR(Light Detection And Ranging) 데이터 처리에 있어서 건물, 자동차, 수목 등의 비지면 객체와 지면을 분류하는 필터링 과정은 DEM(Digital Elevation Model) 구축을 위해서 중요하다. 도심지역의 건물추출 등의 필터링에 관한 연구는 활발히 진행되고 있으나 국내의 경우 수목에 대한 필터링은 비교적 연구가 미흡하였다. 따라서 이 연구에서는 기존에 다루어진 몇 가지 알고리즘을 분석하고 산림지역에 활용해 봄으로써 각 필터링에 관한 장단점을 비교하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.77-83
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• 2020

The technologies of 3D spatial information, such as Smart City and Digital Twins, are developing rapidly for managing land and solving urban problems scientifically. In this construction of 3D spatial information, an object using aerial photo images is built as a digital DB. Realistically, the task of extracting a texturing image, which is an actual image of the object wall, and attaching an image to the object wall are important. On the other hand, occluded areas occur in the texturing image. In this study, the ResNet algorithm in deep learning technologies was tested to solve these problems. A dataset was constructed, and the street tree was detected using the ResNet algorithm. The ability of the ResNet algorithm to detect the street tree was dependent on the brightness of the image. The ResNet algorithm can detect the street tree in an image with side and inclination angles.

• 한국공간정보시스템학회:학술대회논문집
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• 2005.05a
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• pp.355-361
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• 2005

3차원 자료의 필요에 발맞추어 3차원 좌표를 직접적으로 획득할 수 있는 LIDAR 시스템이 등장하게 되었다 항공 LIDAR 시스템은 항공기, GPS, INS, Laser Scanner가 통합된 시스템으로 항공기에서 발사된 Laser의 반사파를 이용하여 거리와 그 때의 항공기의 자세, 위치를 통합하여 직접적인 3차원 포인트 자료를 획득할 수 있다. LiDAR 데이터는 지형, 건물, 식생 등의 지면위에 있는 모든 객체에 대한 3차원 자료와 영상자료를 함께 제공하고 있다. 이러한 LIDAR 자료로부터 DEM, DTM 등의 지형 정보와 식목, 건물 등 지물정보를 추출하는 연구가 활발하게 이루어지고 있다. 본 연구에서는 지형을 추출하는데 사용할 수 있는 몇 가지 필터링기법을 선정하여 국내의 다양한 지모, 지물에 적용하고 그 정확도를 평가해 보았다.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.4
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• pp.151-159
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• 2013

The building information should be up-to-date information and propagated rapidly for urban modeling, terrain analysis, life information, navigational system, and location-based services(LBS), hence the most recent and updated data of the building information have been required of researchers. This paper presents the developed system to extract the 3-dimension spatial information from aerial orthoimage and LiDAR data of HSI color model. In particular, this paper presents the image processing algorithm to extract the outline of specific buildings and generate the building polygon from the image using HIS color model, recursive backtracking algorithm and the search maze algorithm. Also, this paper shows the effectivity of the HIS color model in the image segmentation.