• Journal of Korean Society for Geospatial Information Science
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• v.5 no.2 s.10
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• pp.111-119
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• 1997

The precise measurement and the construction of analysis system for human body is very important in human engineering fields. As this study is about to the accuracy improvement of digital image for human body monitoring, we apply the optimum exposure condition which is decided through the accuracy analysis of digital images those are acquired at the various exposure station to the human body model. We acquired digital data in 1mm accuracy and carried out various spatial analyses. We expect tile results of this study to offer the fundamental source data for the human engineering fields.

• Journal of Korean Society for Geospatial Information Science
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• v.3 no.2 s.6
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• pp.151-158
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• 1995

Change detection, image differencing technique, is the most widely used in a variety of image environments. The digital terrain model and digital images have the same data structure. This study applied digital terrain model and change detection technique for inspecting the deflection of the structure. Authors make digital terrain model from triangular irregular network(TIN) by leveling data and suggest to possibility recognize modification part and volumes by digital terrain model and change detection technique. Authors can reduce testing materials and man power, and displayed his modification part.

• Journal of Korean Society for Geospatial Information Science
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• v.3 no.2 s.6
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• pp.91-100
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• 1995

The aim of this study is to classify mixed information in a pixel of a remote sensing image data (in the case of SPOT HRV's band $1{\sim}3,\;20m{\times}20m$). First, the loss of information and the uncertainty of mixed pixel are examined. To solve the problems, methods by fuzzy sigmoid function and back-propagation neural network are suggested. Then. the study simulates and comparatively analyzes the two methods.

• Journal of Korean Society for Geospatial Information Science
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• v.12 no.3 s.30
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• pp.61-67
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• 2004

Satellite and aerial images are effectively used to monitor ecological and environmental situation. More and more officials in the Ministry of Environment thus need to utilize these image data for various administrative affairs. However, it is difficult not only to deliver to the officials these image data mostly of large capacity through network but also for them to actively use the delivered data without specialized knowledge in remote sensing and image processing. Therefore, we established a large rapacity image data service system employing image compressive transmission and web-based image processing techniques. This system allows the officials to rapidly access all the associated image data and conveniently utilize the data using various functions implemented for remote sensing, image processing, GIS operations. Consequently, this system have been actively utilized for the decision making processes of the officials and hence accomplished a great reduction in the resources required for the data analysis for various administrative affairs.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.4
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• pp.81-88
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• 2007

The geometry of satellite image captured by linear CCD sensor is different from that of frame camera image. The fact that the exterior orientation parameters for satellite image with linear CCD sensor varies from scan line by scan line, causes the difference of image geometry between frame and linear CCD sensor. Therefore, we need the epipolar geometry for linear CCD image which differs from that of frame camera image. In this paper, we proposed a method of resampling linear CCD satellite image in epipolar geometry under the assumption that image is not formed in perspective projection but in parallel projection, and the sensor model is a 2D affine sensor model based on parallel projection. For the experiment, IKONOS stereo images, which are high resolution linear CCD images, were used and tested. As results, the spatial accuracy of 2D affine sensor model is investigated and the accuracy of epipolar resampled image with RFM was presented.

• Journal of Korean Society for Geospatial Information Science
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• v.12 no.2 s.29
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• pp.43-52
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• 2004

This study presents the application of aerial photographs and the Korea Multi-Purpose Satellite, KOMPSAT-1 Electro-Optical Camera(EOC) imagery in detecting change in an urban area that has been rapidly growing. For the study, we used multi-temporal images which were acquired by two different sensors. Image registration and resampling were rallied out before performing change detection in a common reference system with the same spatial resolution. for all of the images. Results from image differencing and image ratioing techniques show that panchromatic aerial photographs and KOMPSAT-1 EOC images collected by different sensors have potential to detect changes of urban features such as building, road and other man-made structure. And the optimal threshold values were suggested in applying image differencing and image ratioing techniques for change detection.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.2
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• pp.49-56
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• 2008

Image registration is one of the critical techniques of image mosaic which has many applications such as generating panoramas, video monitoring, image rendering and reconstruction, etc. The fundamental tasks of image registration are point features extraction and tracking which take much computation time. KLT(Kanade-Lucas-Tomasi) feature tracker has proposed for extracting and tracking features through image sequences. The aim of this study is to demonstrate the usage of effective and robust KLT feature detector and tracker for an image registration using the sequence image frames captured by UAV video camera. In result, by using iterative implementation of the KLT tracker, the features extracted from the first frame of image sequences could be successfully tracked through all frames. The process of feature tracking in the various frames with rotation, translation and small scaling could be improved by a careful choice of the process condition and KLT pyramid implementation.

• Journal of Cadastre & Land InformatiX
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• v.47 no.1
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• pp.213-223
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• 2017

Recently, the air quality of South Korea has deteriorated and public interest has been increasing. Various observation means are used for the monitoring of the atmospheric environment, but it relies on the experience and judgment of the observer in the absence of spatial information on the emission facilities. The purpose of this study was to determine the availability of using drones for monitoring air pollutant emission facilities. A texture transformation method was applied to the drone ortho image to detect the small gas emission facility and the slope data calculated by the digital surface model (DSM) was used to reduce the false alarm ratio. As a result, it shows the possibility of using drones in the detection of small gas emission facilities by showing about 80% of positive detection ratio and 40% of false alarm ratio. In the future, various researches are required to the improve positive detection ratio and the reduction of the false alarm ratio. Based on these results, it is necessary to construct a database including 3D spatial information of air pollutant emission facilities.

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.195-202
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• 2022

Underground Geospatial Information Map Management System(UGIMMS) integrates various underground facilities in the underground space into 3D mesh data, and supports to check the 3D image and location of the underground facilities in the mobile app. However, there is a problem that it takes a long time to run in the app because various underground facilities can exist in some areas executed by the app and can be seen layer by layer. In this paper, we propose a deep learning-based K-means vertex clustering algorithm as a method to reduce the execution time in the app by reducing the size of the data by reducing the number of vertices in the 3D mesh data within the range that does not cause a problem in visibility. First, our proposed method obtains refined vertex feature information through a deep learning encoder-decoder based model. And second, the method was simplified by grouping similar vertices through K-means vertex clustering using feature information. As a result of the experiment, when the vertices of various underground facilities were reduced by 30% with the proposed method, the 3D image model was slightly deformed, but there was no missing part, so there was no problem in checking it in the app.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.121-128
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• 2016

Many studies are focused on image data and classifier for comparison or improvement of classification accuracy. Therefore studies are needed aspect of the training samples on supervised classification which depend on reference data or skill of analyst. This study tries to assess usability of field spectra as training samples on supervised classification. Classification accuracies of hyperspectral and multispectral images were assessed using training samples from image itself and field spectra, respectively. The results shown about 90% accuracy with training sample collected from image. Using field spectra as training sample, accuracy was decreased 10%p for hyperspectral image, and 20%p for multispectral image. Especially, some classes shown very low accuracies due to similar spectral characteristics on multispectral image. Therefore, field spectra might be used as training samples on classification of hyperspectral image, although it has limitation for multispectral image.