• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.1
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• pp.45-52
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• 2015

The interoperability of multi-source data has become more important due to various digital maps, produced from public institutions and enterprises. In this study, the automatic matching algorithm of multi-source building data using hierarchical matching was proposed. At first, we divide digital maps into blocks and perform the primary geometric registration of buildings with the ICP algorithm. Then, corresponding building pairs were determined by evaluating the similarity of overlap area, and the matching threshold value of similarity was automatically derived by the Otsu binary thresholding. After the first matching, we extracted error matching candidates buildings which are similar with threshold value to conduct the secondary ICP matching and to make a matching decision using turning angle function analysis. For the evaluation, the proposed method was applied to representative public digital maps, road name address map and digital topographic map 2.0. As a result, the F measures of matching and non-matching buildings increased by 2% and 17%, respectively. Therefore, the proposed method is efficient for the matching of building polygons from multi-source digital maps.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.248-250
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• 2003

An automatic approach and strategy for extracting building information from aerial images using combined image analysis and interpretation techniques is described in this paper. A dense DSM is obtained by stereo image matching. Multi-band classification, DSM, texture segmentation and Normalised Difference Vegetation Index (NDVI) are used to reveal building interest areas. Then, based on the derived approximate building areas, a shape modelling algorithm based on the level set formulation of curve and surface motion has been used to precisely delineate the building boundaries. Data fusion, based on the Dempster-Shafer technique, is used to interpret simultaneously knowledge from several data sources of the same region, to find the intersection of propositions on extracted information derived from several datasets, together with their associated probabilities. A number of test areas, which include buildings with different sizes, shape and roof colour have been investigated. The tests are encouraging and demonstrate that the system is effective for building extraction, and the determination of more accurate elevations of the terrain surface.

• ETRI Journal
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• v.33 no.4
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• pp.537-546
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• 2011

This paper introduces an automated method for building height recovery through the integration of high-resolution satellite images and digital vector maps. A cross-correlation matching method along the vertical line locus on the Ikonos images was deployed to recover building heights. The rational function models composed of rational polynomial coefficients were utilized to create a stereopair of the epipolar resampled Ikonos images. Building footprints from the digital maps were used for locating the vertical guideline along the building edges. The digital terrain model (DTM) was generated from the contour layer in the digital maps. The terrain height derived from the DTM at each foot of the buildings was used as the starting location for image matching. At a preset incremental value of height along the vertical guidelines derived from vertical line loci, an evaluation process that is based on the cross-correlation matching of the images was carried out to test if the top of the building has reached where maximum correlation occurs. The accuracy of the reconstructed buildings was evaluated by the comparison with manually digitized 3D building data derived from aerial photographs.

• Korean Journal of Remote Sensing
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• v.26 no.1
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• pp.39-45
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• 2010

This study used the keypoint observed simultaneously on two images and on twodimensional intensity image data, which was obtained along with the two point clouds data that were approached for automatic focus among points on terrestrial LiDAR data, and selected matching point through SIFT algorithm. Also, for matching error diploid, RANSAC algorithm was applied to improve the accuracy of focus. As calculating the degree of three-dimensional rotating transformation, which is the transformation-type parameters between two points, and also the moving amounts of vertical/horizontal, the result was compared with the existing result by hand. As testing the building of College of Science at Konkuk University, the difference of the transformation parameters between the one through automatic matching and the one by hand showed 0.011m, 0.008m, and 0.052m in X, Y, Z directions, which concluded to be used as the data for automatic focus.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.5
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• pp.477-486
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• 2020

Drone has been widely used for many applications ranging from amateur and leisure to professionals to get fast and accurate 3-D information of the surface of the interest. Most of commercial softwares developed for this purpose are performing automatic matching based on SIFT (Scale Invariant Feature Transform) or SURF (Speeded-Up Robust Features) using nadir-looking stereo image sets. Since, there are some situations where not only nadir and nadir-looking matching, but also nadir and oblique-looking matching is needed, the existing software for the latter case could not get good results. In this study, a matching experiment was performed to utilize images with differences in geometry. Nadir and oblique-looking images were acquired through drone for a total of 2 times. SIFT, SURF, which are feature point-based, and IMAS (Image Matching by Affine Simulation) matching techniques based on affine transformation were applied. The experiment was classified according to the identity of the geometry, and the presence or absence of a building was considered. Images with the same geometry could be matched through three matching techniques. However, for image sets with different geometry, only the IMAS method was successful with and without building areas. It was found that when performing matching for use of images with different geometry, the affine transformation-based matching technique should be applied.

• Journal of KIBIM
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• v.13 no.4
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• pp.96-105
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• 2023

This study focuses on developing a building object recognition technology for efficient use in the remodeling of buildings constructed without drawings. In the era of the 4th industrial revolution, smart technologies are being developed. This research contributes to the architectural field by introducing a deep learning-based method for automatic object classification and recognition, utilizing point cloud data. We use a TD3D network with voxels, optimizing its performance through adjustments in voxel size and number of blocks. This technology enables the classification of building objects such as walls, floors, and roofs from 3D scanning data, labeling them in polygonal forms to minimize boundary ambiguities. However, challenges in object boundary classifications were observed. The model facilitates the automatic classification of non-building objects, thereby reducing manual effort in data matching processes. It also distinguishes between elements to be demolished or retained during remodeling. The study minimized data set loss space by labeling using the extremities of the x, y, and z coordinates. The research aims to enhance the efficiency of building object classification and improve the quality of architectural plans by reducing manpower and time during remodeling. The study aligns with its goal of developing an efficient classification technology. Future work can extend to creating classified objects using parametric tools with polygon-labeled datasets, offering meaningful numerical analysis for remodeling processes. Continued research in this direction is anticipated to significantly advance the efficiency of building remodeling techniques.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.1
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• pp.125-132
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• 2010

With recent advancement of remote sensing technology, a variety of data acquisition over the same area is achievable. An automated co-registration of heterogeneous airborne images is a critical step for change detection. This paper describes an automatic method for co-registration between digital image and existing building model. Optimal building models for co-registration purpose are extracted as primitives from existing building model database. A set of homologous features between straight lines extracted from aerial digital image and model primitive are computed based on geometric similarity function. With obtained homologous features, EO parameter is recomputed using least square method. The result shows that die suggested method automatically co-register two data set in a reliable manner.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.1
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• pp.37-45
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• 1990

In this paper, a knowledge base editor is presented as a supporting environment for an expert system building tool, OPS5. The knowledge base editor is especially useful for the fast and easy development of a knowledge base when the OPS5 production language is used. This knowledge base editor has some special facilities such as syntax and type checking, rule browsing and automatic bokkeeping. The syntax and type checking provides the facilities to find syntax and type errors in an edited knowledge base, respectively. The rule browsing facility offers various pattern matching schemes to see the causes and effects of a concerned rule. Automatic bookkeeping keeps the updated date and user name of a rule for the later reference whenever a user adds or changes a rule.

• Journal of Power System Engineering
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• v.10 no.4
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• pp.139-146
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• 2006

In this paper vector code correlation(VCC) method and an algorithm to promote the image processing performance in building an effective measurement system using cameras are described for automatically berthing and controlling the ship equipped with side thrusters. In order to realize automatic ship berthing, it is indispensable that the berthing assistant system on the ship should continuously trace a target in the berth to measure the distance to the target and the ship attitude, such that we can make the ship move to the specified location. The considered system is made up of 4 apparatuses compounded from a CCD camera, a camera direction controller, a popular PC with a built in image processing board and a signal conversion unit connected to parallel port of the PC. The object of this paper is to reduce the image processing time so that the berthing system is able to ensure the safety schedule against risks during approaching to the berth. It could be achieved by composing the vector code image to utilize the gradient of an approximated plane found with the brightness of pixels forming a certain region in an image and verifying the effectiveness on a commonly used PC. From experimental results, it is clear that the proposed method can be applied to the measurement system for automatic ship berthing and has the image processing time of fourfold as compared with the typical template matching method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.17 no.4
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• pp.421-429
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• 1999

It is estimated that, in the twenty-first century, 70％ of global citizens will live in urban areas. This accelerated urbanization will require a greater need for the building DEM and orthoimagery along with Geographic Information System for urban management. The building DEM requires the detection of outlines showing building shapes. To do this, automatic and semiautomatic building extractions are usually used. However, in cases where automatic extraction is performed directly from the aerial images, accurate building outline extraction is very difficult because of shadow, roof color, and neighboring trees making it hard to discern building roofs. To overcome this problem semiautomatic building extraction was suggested in this paper. When a roof texture was homogeneous, building outline detection was performed by mouse-clicking on a part of the roof. To construct the building outlines when the texture was not homogeneous, a computer program was developed to search out corner points by clicking spots near corner points. The building DEM was generated by taking into account building outlines and heights calculated by image matching.