• 한국지형공간정보학회:학술대회논문집
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• 2002.11a
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• pp.117-122
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• 2002

Change detection is a useful technology that can be applied to various fields, taking temporal change information with the comparison and analysis among multi-temporal satellite images. Especially, change detection that utilizes high-resolution satellite imagery can be implemented to extract useful change information for many purposes, such as the environmental inspection, the circumstantial analysis of disaster damage, the inspection of illegal building, and the military use, which cannot be achieved by lower middle-resolution satellite imagery. However, because of the special characteristics that result from high-resolution satellite imagery, it cannot use a pixel-based method that is used for low-resolution satellite imagery. Therefore, it must be used a feature-based algorithm based on the geographical and morphological feature. This paper presents the system that builds the change map by digitizing the boundary of the changed object. In this system, we can make the change map using manual or semi-automatic digitizing through the user interface implemented with a floating window that enables to detect the sign of the change, such as the construction or dismantlement, more efficiently.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.3
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• pp.83-94
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• 2003

All the urban planning process require land use informations, which should be obtained after through intensive investigation and accurate analysis about the past and current situations and conditions of a city. Until now, the generation of land use informations from remotely sensed imagery has had many limitation because of its spatial resolution. It is now expected that the availability of high resolution satellite imagery whose spatial resolution less than 10m will reduce these limitations. For the purpose of urban growth monitoring we must first establish a urban land use classification nomenclature. In this study, we would like to establish a land use nomenclature for land use classification using remotely sensed data, especially using KOMPSAT EOC imagery.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.641-642
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• 2013

This research implemented Geostationary Ocean Color Imager (GOCI) observation satellite imagery visualization system on a large high-resolution tiled display. This system is designed to help users observe or analyze satellite imagery more effectively on the tiled display using multi-touch and Kinect motion gesture recognition interaction. We developed the multi-scale image loading and rendering technique for the massive amount of memory management and smooth rendering for GOCI satellite imagery on the tiled display. In this system, the unit of time corresponding to the selected date of the satellite images are sequentially displayed on the screen. Users can zoom-in, zoom-out, move the imagery and select some buttons to trigger functions using both multi-touch or Kinect gesture interaction.

• Korean Journal of Remote Sensing
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• v.25 no.2
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• pp.133-143
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• 2009

This paper describes differences of performance when the orbit attitude model is applied to the respective images obtained from two different types of satellite. The one is Spot that rotates its pointing mirror and the other is Kompsat-2 that rotates its whole body when they obtain imagery for target. Our research scope is limited to the orbit-attitude model only as its good performance was proved in prior investigation. Model performances between two images were compared with sensor model accuracy and 3D coordinates calculation. The results show performances of the orbit-attitude model for each image type were different. For Spot imagery, the model required attitude angle to be included as adjustment parameters. For Kompsat-2 imagery, the model required high-order parameter for adjustment. This implies that satellite sensor model may be applied differently in accordance with platform's attitude control scheme and accuracy. Understanding of this information can be a base for improvement and development of model and application for new satellite images.

• Korean Journal of Remote Sensing
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• v.23 no.6
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• pp.547-552
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• 2007

This study presents an approach to reconstruct high-resolution imagery for multispectral imagery of low-resolution using panchromatic imagery of high-resolution. The proposed scheme reconstructs a high-resolution image which agrees with original spectral values. It uses a linear model of high-and low- resolution images and consists of two stages. The first one is to perform a global estimation of the least square error on the basis of a linear model of low-resolution image associated with high-resolution feature, and next local correction then makes the reconstructed image locally fit to the original spectral values. In this study, the new method was applied to KOMPSAT-1 EOC image of 6m and LANDSAT ETM+ of 30m, and an 1m RGB image was also generated from 4m IKONOS multispectral data. The results show its capability to reconstruct high-resolution imagery from multispectral data of low-resolution.

• Psychiatry investigation
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• v.15 no.11
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• pp.1071-1078
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• 2018

Objective We developed easily accessible imagery-based treatment program for patients with post-traumatic stress disorder (PTSD) related to workplace accidents and investigated the effects of the program on various PTSD related symptoms. Methods The program was based on an online platform and consisted of eight 15-min sessions that included script-guided imagery and supportive music. Thirty-five patients with workplace-related PTSD participated in this program 4 days per week for 4 weeks. Its effects were examined using self-report questionnaires before and after the take-home online treatment sessions. Results After completing the 4-week treatment program, patients showed significant improvements in depressed mood (t=3.642, p=0.001) based on the Patient Health Questionnaire-9 (PHQ-9), anxiety (t=3.198, p=0.003) based on the Generalized Anxiety Disorder seven-item (GAD-7) scale, and PTSD symptoms (t=5.363, p<0.001) based on the Posttraumatic Stress Disorder Check List (PCL). In particular, patients with adverse childhood experiences exhibited a greater degree of relief related to anxiety and PTSD symptoms than those without adverse childhood experiences. Conclusion The present results demonstrated that the relatively short online imagery-based treatment program developed for this study had beneficial effects for patients with workplace-related PTSD.

• Journal of the Korea Computer Graphics Society
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• v.25 no.4
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• pp.9-16
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• 2019

In this paper, we propose a hydrodynamic scene separation method for wave propagation from video imagery using autoencoder. In the coastal area, image analysis methods such as particle tracking and optical flow with video imagery are usually applied to measure ocean waves owing to some difficulties of direct wave observation using sensors. However, external factors such as ambient light and weather conditions considerably hamper accurate wave analysis in coastal video imagery. The proposed method extracts hydrodynamic scenes by separating only the wave motions through minimizing the effect of ambient light during wave propagation. We have visually confirmed that the separation of hydrodynamic scenes is reasonably well extracted from the ambient light and backgrounds in the two videos datasets acquired from real beach and wave flume experiments. In addition, the latent representation of the original video imagery obtained through the latent representation learning by the variational autoencoder was dominantly determined by ambient light and backgrounds, while the hydrodynamic scenes of wave propagation independently expressed well regardless of the external factors.

• Journal of Biomedical Engineering Research
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• v.43 no.6
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• pp.375-381
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• 2022

Brain-computer interface (BCI) is a technology that allows users with motor disturbance to control machines by brainwaves without a physical controller. Motor imagery (MI)-BCI is one of the popular BCI techniques, but it needs a long calibration time for users to perform a mental task that causes high fatigue to the users. MI is reported as showing a similar neural mechanism as motor execution (ME) and motor observation (MO). However, integrative investigations of these three tasks are rarely conducted. In this study, we propose a new paradigm that incorporates three tasks (MI, ME, and MO) and conducted a comparative analysis. For this study, we collected Electroencephalograms (EEG) of motor imagery/execution/observation from 28 healthy subjects and investigated alpha event-related (de)synchronization (ERD/ERS) and classification accuracy (left vs. right motor tasks). As result, we observed ERD and ERS in MI, MO and ME although the timing is different across tasks. In addition, the MI showed strong ERD on the contralateral hemisphere, while the MO showed strong ERD on the ipsilateral side. In the classification analysis using a Riemannian geometry-based classifier, we obtained classification accuracies as MO (66.34%), MI (60.06%) and ME (58.57%). We conclude that there are similarities and differences in fundamental neural mechanisms across the three motor tasks and that these results could be used to advance the current MI-BCI further by incorporating data from ME and MO.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.27-39
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• 2023

Detecting greenhouses from the remote sensing datasets is useful in identifying the illegal agricultural facilities and predicting the agricultural output of the greenhouses. This research proposed a methodology for automatically detecting greenhouses from a given Planetscope satellite imagery acquired in the areas of Gimje City using the deep learning technique through a series of steps. First, multiple training images with a fixed size that contain the greenhouse features were generated from the five training Planetscope satellite imagery. Next, the YOLO(You Only Look Once) model was trained using the generated training images. Finally, the greenhouse features were detected from the input Planetscope satellite image. Statistical results showed that the 76.4% of the greenhouse features were detected from the input Planetscope satellite imagery by using the trained YOLO model. In future research, the high-resolution satellite imagery with a spatial resolution less than 1m should be used to detect more greenhouse features.

• Journal of Sensor Science and Technology
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• v.25 no.4
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• pp.257-263
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• 2016

Among several technologies related to human brain, Brain Computer Interface (BCI) system is one of the most notable technologies recently. Conventional BCI for direct communication between human brain and machine are discomfort because normally electroencephalograghy(EEG) signal is measured by using multichannel EEG sensor. In this study, we propose 2-channel EEG sensor-based application control system which is more convenience and low complexity to wear to get EEG signal. EEG sensor module and system algorithm used in this study are developed and designed and one of the BCI methods, Motor Imagery (MI) is implemented in the system. Experiments are consisted of accuracy measurement of MI classification and driving control test. The results show that our simple wearable system has comparable performance with studies using multi-channel EEG sensor-based system, even better performance than other studies.