• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.252-262
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• 2020

In Remote Sensing, a machine learning based SVM model is typically utilized for land cover classification. And study using neural network models is also being carried out continuously. But study using high-resolution imagery of KOMPSAT is insufficient. Therefore, the purpose of this study is to assess the accuracy of land cover classification by neural network models using high-resolution KOMPSAT-3 satellite imagery. After acquiring satellite imagery of coastal areas near Gyeongju City, training data were produced. And land cover was classified with the SVM, ANN and DNN models for the three items of water, vegetation and land. Then, the accuracy of the classification results was quantitatively assessed through error matrix: the result using DNN model showed the best with 92.0% accuracy. It is necessary to supplement the training data through future multi-temporal satellite imagery, and to carry out classifications for various items.

• Journal of Korea Multimedia Society
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• v.6 no.5
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• pp.769-777
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• 2003

We present a striping technique that MPEG-1 video streams ell a disk array can be efficiently played back at different resolution levels. For the MPEG-1 compression algorithm, the proposed multi-level encoding technique first partitions the parent video stream in the temporal dimension. Each frame in the sub-stream is then Partitioned in the chroma dimension yielding a low resolution and a residual component. The multimedia server stores blocks of different components on consecutive disks in a round robin manner. As a result, the lower the resolution level being maintained, the smaller is the number of disks accessed by each client. To effectively utilize a disk array and to maximize the number of clients that can be serviced simultaneously, the proposed technique interleaves the storage of the component of sub-streams among the disks in the array We empirically validate and evaluate this striping technique through simulation in order to show the improvement of its performance on the server.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.3
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• pp.41-48
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• 2012

Using multi-sensor or multi-temporal high resolution satellite images together is essential for efficient applications in remote sensing area. The purpose of this paper is to estimate geometric difference of translations between high-resolution optical and SAR images automatically. The geometric and radiometric pre-processing steps were fulfilled to calculate the similarity between optical and SAR images by using Mutual Information method. The coarsest-level pyramid images of each sensor constructed by gaussian pyramid method were generated to estimate the initial translation difference of the x, y directions for calculation efficiency. The precise geometric difference of translations was able to be estimated by applying this method from coarsest-level pyramid image to original image in order. Yet even when considered only translation between optical and SAR images, the proposed method showed RMSE lower than 5m in all study sites.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.24 no.5
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• pp.333-342
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• 2012

We studied the characteristics of spatial distribution of global wave height and carried out the modelsensitivity test by changing the input field, model resolution and physical factor (effective wind factor) since the spatial and temporal resolution in wind wave forecasting is one of most important factors. Comparisons among the different cases, and also between model, buoy and satellite data have been made. As a results of the wind-wave model run using the high resolution wind field, the bias of significant wave height showed the positive tendency and the Root-Mean Square Error(RMSE) was a bit decreased based on the comparison with buoy data. When the model resolution was changed to higher, the bias and RMSE was increased, and as the effective wind factor was smaller than default value(= 1.4) the bias and RMSE showed also decreasing pattern.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1779-1790
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• 2023

Satellite-based fog detection algorithms are being developed to detect fog in real-time over a wide area, with a focus on the Korean Peninsula (KorPen). The GEO-KOMPSAT-2A/Advanced Meteorological Imager (GK2A/AMI, GK2A) satellite offers an excellent temporal resolution (10 min) and a spatial resolution (500 m), while GEO-KOMPSAT-2B/Geostationary Ocean Color Imager-II (GK2B/GOCI-II, GK2B) provides an excellent spatial resolution (250 m) but poor temporal resolution (1 h) with only visible channels. To enhance the fog detection level (10 min, 250 m), we developed a fused GK2AB fog detection algorithm (FDA) of GK2A and GK2B. The GK2AB FDA comprises three main steps. First, the Korea Meteorological Satellite Center's GK2A daytime fog detection algorithm is utilized to detect fog, considering various optical and physical characteristics. In the second step, GK2B data is extrapolated to 10-min intervals by matching GK2A pixels based on the closest time and location when GK2B observes the KorPen. For reflectance, GK2B normalized visible (NVIS) is corrected using GK2A NVIS of the same time, considering the difference in wavelength range and observation geometry. GK2B NVIS is extrapolated at 10-min intervals using the 10-min changes in GK2A NVIS. In the final step, the extrapolated GK2B NVIS, solar zenith angle, and outputs of GK2A FDA are utilized as input data for machine learning (decision tree) to develop the GK2AB FDA, which detects fog at a resolution of 250 m and a 10-min interval based on geographical locations. Six and four cases were used for the training and validation of GK2AB FDA, respectively. Quantitative verification of GK2AB FDA utilized ground observation data on visibility, wind speed, and relative humidity. Compared to GK2A FDA, GK2AB FDA exhibited a fourfold increase in spatial resolution, resulting in more detailed discrimination between fog and non-fog pixels. In general, irrespective of the validation method, the probability of detection (POD) and the Hanssen-Kuiper Skill score (KSS) are high or similar, indicating that it better detects previously undetected fog pixels. However, GK2AB FDA, compared to GK2A FDA, tends to over-detect fog with a higher false alarm ratio and bias.

• Publications of The Korean Astronomical Society
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• v.15 no.spc2
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• pp.1-13
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• 2000

Through the coupling between the near-earth space environment and the polar ionosphere via geomagnetic field lines, the variations occurred in the magnetosphere are transferred to the polar region. According to recent studies, however, the polar ionosphere reacts not only passively to such variations, but also plays active roles in modifying the near-earth space environment. So the study of the polar ionosphere in terms of geomagnetic disturbance becomes one of the major elements in space weather research. Although it is an indirect method, ground magnetic disturbance data can be used in estimating the ionospheric current distribution. By employing a realistic ionospheric conductivity model, it is further possible to obtain the distributions of electric potential, field-aligned current, Joule heating rate and energy injection rate associated with precipitating auroral particles and their energy spectra in a global scale with a high time resolution. Considering that the ground magnetic disturbances are recorded simultaneously over the entire polar region wherever magnetic station is located, we are able to separate temporal disturbances from spatial ones. On the other hand, satellite measurements are indispensible in the space weather research, since they provide us with in situ measurements. Unfortunately it is not easy to separate temporal variations from spatial ones specifically measured by a single satellite. To demonstrate the usefulness of ground magnetic disturbance data in space weather research, various ionospheric quantities are calculated through the KRM method, one of the magneto gram inversion methods. In particular, we attempt to show how these quantities depend on the ionospheric conductivity model employed.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.2
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• pp.90.1-90.1
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• 2013

To better understand the physics underlying the eruption of prominences in solar active regions, we studied eruption processes of two active prominences located in the active region NOAA 11261 using multi-wavelength observational data with high temporal and spatial resolution. Specifically, we examined (1) the temporal variation of morphology and plasma properties of the two active prominences, (2) magnetic fields and their evolution on the photospheric surface underneath the prominences, and (3) the time profiles and locations of radio, EUV, and soft/hard X-ray emissions produced by the M9.3 flare related to the prominence eruption. As a result, we found that: (1) a prominence F1 began to erupt and expand as the abrupt and intense EUV brightening occurred in the localized region underneath the western part of F1 at 03:45 UT prior to the peak time of the M9.3 flare, (2) F1 split into two parts: i.e., the western part asymmetrically erupted by producing the M9.3 flare with microwave source motions along the magnetic polarity inversion line between the two flare ribbons, while the eastern part coalesced into a pre-existing prominence F2, (3) F2 became unstable due to the coalescence with the eastern part of F1, and then it partially erupted with clockwise untwisting motions.

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.3
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• pp.282-292
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• 2007

The present paper describes the results of high speed photography, acoustic emission (AE) detection and plasma light emission (LE) measurement during $CO_2$ laser welding of 304 stainless steel in different processing conditions. Video images with high spatial and temporal resolution allowed to observe the melt dynamics and keyhole evolution. The existence of keyhole was confirmed by the slag motion on the weld pool. The characteristic frequencies of flow instability and keyhole fluctuations at different welding speed were measured and compared with the results of Fourier analyses of temporal AE and LE spectra. The experimental results were compared with the newly developed numerical model of keyhole dynamics. The model is based on the assumption that the propagation of front part of keyhole into material is due to the melt ejection driven by laser induced surface evaporation. The calculations predict that a high speed melt flow is induced at the front part of keyhole when the sample travel speed exceeds several 10 mm/s. The numerical analysis also shows the hump formation on the front keyhole wall surface. Experimentally observed melt behavior and transformation of the AE and LE spectra with variation of welding speed are qualitatively in good agreement with the model predictions.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.2
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• pp.179-189
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• 2003

The concentration of gaseous elemental mercury (Hg) was measured concurrently with relevant environmental parameters from Yang-Jae monitoring station in Seoul during Sept. 1997 to June 2002. Although data collection was disrupted for certain periods, the grand mean concentration of Hg for this five year period was found at 5.32 $\pm$ 3.53 ng m$^{-3}$ (N = 27,170). Because of short resolution of data acquisition, we were able to examine the temporal variability of Hg at varying time scale. The diurnal variability of Hg, when investigated for each of those five years, indicated consistently the dominance of nighttime over daytime. If examined at seasonal scale, Hg level was systematically higher during winter/spring than summer/fall period. The results of this short-term variability were best explained by the combined effects of such factors as meteorological conditions (formation of inversion layer and seasonal changes) and anthropogenic source processes. However, examination of long-term variation Pattern was much more complicated to explain. Thus, extension of our study is needed to diagnose the future direction in long-term trend of Hg behavior.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.5
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• pp.735-747
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• 2018

Rainfall depth is an important meteorological information. Generally, high spatial resolution rainfall data such as road-level rainfall data are more beneficial. However, it is expensive to set up sufficient Automatic Weather Systems to get the road-level rainfall data. In this paper, we propose to use deep learning to recognize rainfall depth from road surveillance videos. To achieve this goal, we collect a new video dataset and propose a procedure to calculate refined rainfall depth from the original meteorological data. We also propose to utilize the differential frame as well as the optical flow image for better recognition of rainfall depth. Under the Temporal Segment Networks framework, the experimental results show that the combination of the video frame and the differential frame is a superior solution for the rainfall depth recognition. The final model is able to achieve high performance in the single-location low sensitivity classification task and reasonable accuracy in the higher sensitivity classification task for both the single-location and the multi-location case.