• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.400-406
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• 2002

Recently, the enormous increase in the volume of remotely sensed data is being acquired by an ever-growing number of earth observation satellites. The combining of diversely sourced imagery together is an important requirement in many applications such as data fusion, city modeling and object recognition. Aerial triangulation is a procedure to reconstruct object space from imagery. However, since the different kinds of imagery have their own sensor model, characteristics, and resolution, the previous approach in aerial triangulation (or georeferencing) is performed on a sensor model separately. This study evaluated the advantages of aerial triangulation of large number of images from multi-sensors simultaneously. The incorporated multi-sensors are frame, push broom, and whisky broom cameras. The limits and problems of push-broom or whisky broom sensor models can be compensated by combined triangulation with frame imagery and vise versa. The reconstructed object space from multi-sensor triangulation is more accurate than that from a single model. Experiments conducted in this study show the more accurately reconstructed object space from multi-sensor triangulation.

• Proceedings of the KSRS Conference
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• v.1
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• pp.409-412
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• 2006

Today's commercial high resolution satellite imagery such as IKONOS and QuickBird, offers the potential to extract useful spatial information for geographical database construction and GIS applications. Recognizing this potential use of high resolution satellite imagery, KARI is performing a project for developing Korea multipurpose satellite 3(KOMPSAT-3). Therefore, it is necessary to develop techniques for various GIS applications of KOMPSAT-3, using similar high resolution satellite imagery. As fundamental studies for this purpose, we focused on the extraction of 3D spatial information and the update of existing GIS data from QuickBird imagery. This paper examines the scheme for rectification of high resolution image, and suggests the convenient semi-automatic algorithm for extraction of 3D building information from a single image. The algorithm is based on triangular vector structure that consists of a building bottom point, its corresponding roof point and a shadow end point. The proposed method could increase the number of measurable building, and enhance the digitizing accuracy and the computation efficiency.

• Korean Journal of Adult Nursing
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• v.15 no.4
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• pp.639-649
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• 2003

Purpose: The purpose of this study was to identify the effect of dynamic imagery therapy on "anger-in" and immune function in patients with breast cancer. Method: Dynamic imagery therapy was given for 10 sessions with 2 times informative education. Data collection was from June, 24th to August, 12th 2002. The participants were experimental group 15, control group 15. This study was designed using a quasi-experimental approach with non-equivalent Pre test-Post test Design. The instruments used in this study include the anger scale developed by Spielberger(1972) and modified by Chon, Kyum Koo (1996), The data was analysed with $\chi^2$-test, t-test, ANOVA and ANCOVA. Result: 1) That the level of "Anger-in" in patients will be significantly lower than that of the control group was supported (p<.001). 2) That the level of T3 lymphocyte and NK Cells, IgG. Cortisol in patients will be significantly lower than that of the control group was supported (T3, IgG: p<.05, Cortisol p<.001). Conclusion: The dynamic imagery therapy is suggested as an effective nursing intervention to reduce anger level and increase immune function for patients with breast cancer.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.155-158
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• 2008

The needs for digital models of real environment such as 3D terrain or cyber city model are increasing. Most of applications related with modeling and simulation require virtual environment constructed from geospatial information of real world in order to guarantee reliability and accuracy of the simulation. The most fundamental data for building virtual environment, terrain elevation and orthogonal imagery is acquired from optical sensor of satellite or airplane. Providing interoperable and reusable digital model is important to promote practical application of high-resolution satellite imagery. This paper presents the new research regarding representation of geospatial information, especially for 3D shape and appearance of virtual terrain, and describe framework for constructing real-time 3D model of large terrain based on high-resolution satellite imagery. It provides infrastructure of 3D simulation with geographical context. Details of standard-based approach for providing infrastructure of real-time 3D simulation using high-resolution satellite imagery are also presented. This work would facilitate interchange and interoperability across diverse systems and be usable by governments, industry scientists and general public.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.130-137
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• 2013

This study tries to investigate the relationship between physical perception and creative thinking of dancers by dance imagery training programs. The inner imagery and the external expression of dancers are a divergent expression process of autosuggestion, and substantiate the relationship between physical perception and creative thinking. The key point consists in the active thinking process of problem recognition and problem solving by imagination, and means a new perceptivity and the communication capability. Dance imagery is a perception training based on the integration principle of body and soul, and so dancers should create a new approach of communication through the diversity of wide inner imagination and the active thinking of external expression.

• Korean Journal of Adult Nursing
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• v.19 no.5
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• pp.115-126
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• 2007

Purpose: The present study is a quasi-experimental research for examining the effects of imagery on stress, anxiety and immune cells in acute leukemia patients receiving chemotherapy and utilizing the therapy for their self control and stress management. Methods: The subjects were 60 patients who were diagnosed with acute leukemia and scheduled to receive chemotherapy at A hospital in Seoul during the period from November 2006 to March 2007. After the start of chemotherapy, the experimental group received imagery for 4 weeks, three sessions a week, so a total of 12 sessions and 156 minutes. Results: The decrease of stress was larger in the experimental group than in the control group. Systolic blood pressure decreased significantly more in the experimental group than in the control group. In the experimental group, state anxiety decreased significantly in the 2nd week of the experiment and after the final stage of the experiment. The total number of white blood cells, and the absolute number of neutrophils and lymphocytes were showing significant differences between the time points. Conclusion: Imagery is an effective intervention for reducing stress and state anxiety and stabilizing blood pressure in acute leukemia patients receiving chemotherapy.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.3
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• pp.315-322
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• 2018

Due to the increasing demand for more rapid, precise and accurate geolocation of the targets on video frames from UAVs, an efficient and timely method for correcting sensor models of motion imagery is required. In this paper, we propose a method to adjust or correct sensor models of motion imagery frames using space resection via image matching with reference data. The proposed method adopts image matching between the motion imagery frames and the reference frames which are synthesized from reference data. Ground or reference control points are generated or selected through the matching process in near real time, and are used for space resection to get adjusted sensor models. Finally, more precise and accurate geolocation of the targets can possibly be done on the fly, and we have got the promising result on performance analysis in terms of the geolocation quality.

• Korean Journal of Remote Sensing
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• v.34 no.4
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• pp.671-680
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• 2018

Compared to the imagery produced by traditional satellites, PlanetScope satellite imagery has made it possible to easily capture remotely-sensed imagery every day through dozens or even hundreds of satellites on a relatively small budget. This study aimed to detect changed areas and update a land cover map using a PlanetScope image. To generate a classification map, pixel-based Random Forest (RF) classification was performed by using additional features, such as the Normalized Difference Water Index (NDWI) and the Normalized Difference Vegetation Index (NDVI). The classification result was converted to vector data and compared with the existing land cover map to estimate the changed area. To estimate the accuracy and trends of the changed area, the quantitative quality of the supervised classification result using the PlanetScope image was evaluated first. In addition, the patterns of the changed area that corresponded to the classification result were analyzed using the PlanetScope satellite image. Experimental results found that the PlanetScope image can be used to effectively to detect changed areas on large-scale land cover maps, and supervised classification results can update the changed areas.

• The Korean Journal of Ecology
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• v.26 no.2
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• pp.75-81
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• 2003

This study was performed to prove if the high resolution satellite imagery of IKONOS is suitable for preparing digital vegetation map which is becoming increasingly important in ecological science. Seven classes for forest area and five classes for non-forest area were taken for classification. Three methods, such as the pixel based classification, the segment based classification with majority principle, and the segment based classification with maximum likelihood, were applied to classify IKONOS imagery taken in April 2000. As a whole, the segment based classification shows better performance in classifying the high resolution satellite imagery of IKONOS. Through the comparison of accuracies and kappa values of the above 3 classification methods, the segment based classification with maximum likelihood was proved to be the best suitable for preparing the vegetation map with the help of IKONOS imagery. This is true not only from the viewpoint of accuracy, but also for the purpose of preparing a polygon based vegetation map. On the basis of the segment based classification with the maximum likelihood, a digital vegetation map in which each vegetation class is delimitated in the form of a polygon could be prepared.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.775-779
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• 2002

Currently, high-resolution satellite imagery such as KOMPSAT and IKONOS has been tentatively utilized to various types of urban engineering problems such as transportation planning, site planning, and utility management. This approach aims at software development and followed applications of remotely sensed imagery to transportation geographic analysis. At first, GDPA (Gradient Direction Profile Algorithm) and main modules in it are overviewed, and newly implemented results under MS visual programming environment are presented with main user interface, input imagery processing, and internal processing steps. Using this software, road network are automatically generated. Furthermore, this road network is used to transportation geographic analysis such as gamma index and road pattern estimation. While, this result, being produced to do-facto format of ESRI-shapefile, is used to several types of road layers to urban/transportation planning problems. In this study, road network using KOMPSAT EOC imagery and IKONOS imagery are directly compared to multiple road layers with NGI digital map with geo-coordinates, as ground truth; furthermore, accuracy evaluation is also carried out through method of computation of commission and omission error at some target area. Conclusively, the results processed in this study is thought to be one of useful cases for further researches and local government application regarding transportation geographic analysis using remotely sensed data sets.