• 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.

• ETRI Journal
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• v.25 no.2
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• pp.109-120
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• 2003

Though the airborne laser scanning (ALS) technique is becoming more popular in many applications, horizontal accuracy of points scanned by the ALS is not yet satisfactory when compared with the accuracy achieved for vertical positions. One of the major reasons is the drift that occurs in the inertial measurement unit (IMU) during the scanning. This paper presents an algorithm that adjusts for the error that is introduced mainly by the drift of the IMU that renders systematic differences between strips on the same area. For this, we set up an observation equation for strip-wise adjustments and completed it with tie point and control point coordinates derived from the scanned strips and information from aerial photos. To effectively capture the tie points, we developed a set of procedures that constructs a digital surface model (DSM) with breaklines and then performed feature-based matching on strips resulting in a set of reliable tie points. Solving the observation equations by the least squares method produced a set of affine transformation equations with 6 parameters that we used to transform the strips for adjusting the horizontal error. Experimental results after evaluation of the accuracy showed a root mean squared error (RMSE) of the adjusted strip points of 0.27 m, which is significant considering the RMSE before adjustment was 0.77 m.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.393-398
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• 2006

Currently, 54 kinds of digital maps are provided by National Geographic Information Clearinghouse and the majority of those maps are based on aerial photographs or satellite image. The digital maps which symbolize and simplifies the topography and objects from ortho-photos does not reflect the objects 'shapes and facilities' changes. Especially, underground structures and complex building shapes are not correctly identifies by ortho-photos. Furthermore, the 1/1,000 and 1/500-1/2,500 maps for urban area produced by some local government or public organizations have detailed information with high precision, it is not easy to update the information due to the frequent changes of structures in the city. Although some efforts to solve this problem such as conducting field survey and shorten the survey period were tried, it is not the fundamental solution due to the high cost. Therefore, in this study, a realtime renewal and update of digital map using general survey are suggested. By assigning absolute coordinates to the general survey products and matching with digital maps, it is possible to update the digital map economically and rapidly. In addition, it is suggested that the construction of DB for general survey and sharing among survey companies to solve the duplicated survey.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.474-478
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• 1999

SAR interferometry (InSAR) using the space-borne Synthetic Aperture Radar (SAR) have recently become one of the most effective tools monitoring surface changes caused by landslides, earthquakes, subsidences or volcanic eruption. This study focuses on examining the feasibility of InSAR using the RADARSAT data. Although the RABARSAT SAR with its high resolution and variable incidence angle has several advantages for repeat-pass InSAR, it has two key limitations: first, the orbit is not precisely known; and second, RADARSAT's 24-day repeat pass interval is not very favourable for retaining useful coherence. In this study, two pairs of RADARSAT data in the Nahanni area, NWT, Canada have been tested. We will discuss about the special consideration required on the interferometric processing steps specifically for RADARSAT data including image co-registration, spectral filtering in both azimuth and range, estimation of the interferometric baseline, and correction of the interferogram with respect to the "flat earth" phase contribution. Preliminary results can be summarized as: i) the properly designed azimuth filter based upon the antenna characteristic improves coherence considerably if difference in Doppler centroid of the two images is relatively large; ii) the co-registration process combined by fringe spectrum and amplitude cross-correlation techniques results in optimal matching; iii) the baseline is not always possible to be estimated from the definitive orbit information.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.10
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• pp.1516-1522
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• 2017

We propose a novel monitoring system for diagnosing crack faults of the wind turbine using image information. The proposed method classifies a normal state and a abnormal state for the blade parts of the wind turbine. Specifically, the images are input to the proposed system in various states of wind turbine rotation. according to the blade condition. Then, the video of rotating blades on the wind turbine is divided into several image frames. Motion vectors are estimated using the previous and current images using the motion estimation, and the change of the motion vectors is analyzed according to the blade state. Finally, we determine the final blade state using the Support Vector Machine (SVM) classifier. In SVM, features are constructed using the area information of the blades and the motion vector values. The experimental results showed that the proposed method had high classification performance and its $F_1$ score was 0.9790.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.54-61
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• 2008

This paper proposes a new algorithm for detecting and recognizing overlapped objects among a stack of arbitrarily located objects using a signature representation scheme. The proposed algorithm consists of two processes of detecting overlap of objects and of determining the boundary between overlapping objects. To determine overlap of objects, in the first step, the edge image of object region is extracted and those areas in the object region are considered as the object areas if an area is surrounded by a closed edge. For each object, its signature image is constructed by measuring the distances of those edge points from the center of the object, along the angle axis, which are located at every angle with reference to the center of the object. When an object is not overlapped, its features which consist of the positions and angles of outstanding points in the signature are searched in the database to find its corresponding model. When an object is overlapped, its features are partially matched with those object models among which the best matching model is selected as the corresponding model. The boundary among the overlapping objects is determined by projecting the signature to the original image. The performance of the proposed algorithm has been tested with the task of picking the top or non-overlapped object from a stack of arbitrarily located objects. In the experiment, a recognition rate of 98% has been achieved.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.47-49
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• 2003

Automatic land use and land cover change (LUCC) detection via remotely sensed imagery has a wide application in the area of LUCC research, nature resource and environment monitoring and protection. Under the condition that one time (T1) data is existed land use and land cover maps, and another time (T2) data is remotely sensed imagery, how to detect change automatically is still an unresolved issue. This paper developed a land use and land cover class knowledge guided method for automatic change detection under this situation. Firstly, the land use and land cover map in T1 and remote sensing images in T2 were registered and superimposed precisely. Secondly, the remotely sensed knowledge database of all land use and land cover classes was constructed based on the unchanged parcels in T1 map. Thirdly, guided by T1 land use and land cover map, feature statistics for each parcel or pixel in RS images were extracted. Finally, land use and land cover changes were found and the change class was recognized through the automatic matching between the knowledge database of remote sensing information of land use & land cover classes and the extracted statistics in that parcel or pixel. Experimental results and some actual applications show the efficiency of this method.

• Korean Journal of Remote Sensing
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• v.9 no.2
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• pp.7-19
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• 1993

In stereo vision, when we use two parallel axis images, small portion of object is contained and B/H(Base-line to Height) ratio is limited due to the size of object and depth information is inaccurate. To overcome these difficulities we take a non-parallel axis image which is rotated $\theta$ about y-axis and match other parallel-axis image. Epipolar lines of non-parallel axis image are not same as those of parallel-axis image and we can't match these two images directly. In this paper, we transform the non-parallel axis image geometrically with camera parameters, whose epipolar lines are alingned parallel. NCC(Normalized Cross Correlation) is used as match measure, area-based matching technique is used find correspondence and 9$\times$9 window size is used, which is chosen experimentally. Focal length which is necessary to get depth information of given object is calculated with least-squares method by CCD camera characteristics and lenz property. Finally, we select 30 test points from given object whose elevation is varied to 150 mm, calculate heights and know that height RMS error is 7.9 mm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.389-395
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• 2019

Unlike aerial images covering small region, satellite data show high potential to detect urban scale geospatial changes. The change detection using satellite images can be carried out using single image or stereo images. The single image approach is based on radiometric differences between two images of different times. It has limitations to detect building level changes when the significant occlusion and relief displacement appear in the images. In contrast, stereo satellite data can be used to generate DSM (Digital Surface Model) that contain information of relief-corrected objects. Therefore, they have high potential for the object change detection. Therefore, we carried out a study for the change detection over an urban area using stereo satellite data of two different times. First, the RPC correction was performed for two DSMs generation via stereo image matching. Then, D-DSM (Differential DSM) was generated by differentiating two DSMs. The D-DSM was used for the topographic change detection and the performance was checked by applying different height thresholds to D-DSM.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.125-133
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• 2011

For any pattern matching based algorithm in WLAN environment, the characteristics of signal to noise ratio(SNR) to multiple access points(APs) are utilized to establish database in the training phase, and in the estimation phase, the actual two dimensional coordinates of mobile unit(MU) are estimated based on the comparison between the new recorded SNR and fingerprints stored in database. As fingerprinting method, k-nearest neighbor(KNN) has been widely applied for indoor location in wireless location area networks(WLAN), but its performance is sensitive to number of neighbors k and positions of reference points(RPs). So intuitive fuzzy c-means(IFCM) clustering algorithm is applied to improve KNN, which is the KNN/IFCM hybrid algorithm presented in this paper. In the proposed algorithm, through KNN, k RPs are firstly chosen as the data samples of IFCM based on signal to noise ratio(SNR). Then, the k RPs are classified into different clusters through IFCM based on SNR. Experimental results indicate that the proposed KNN/IFCM hybrid algorithm generally outperforms KNN, KNN/FCM, KNN/PFCM algorithm when the locations error is less than 2m.