• Korean Journal of Remote Sensing
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• v.27 no.5
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• pp.521-534
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• 2011

The high-resolution X-band SAR systems such as COSMO-SkyMED and TerraSAR-X have been launched recently. In addition KOMPSAT-5 will be launched in the early of 2012. In this study we developed the new method for persistent scatterer candidate (PSC) selection and network construction, which is more suitable for PSInSAR analysis using multi-temporal X-band SAR data. PSC selection consists in two main steps: first, selection of initial PSCs based on amplitude dispersion index, mean amplitude, mean coherence. second, selection of final PSCs based on temporal coherence directly estimated from network analysis of initial PSCs. To increase the stability of network the Multi- TIN and complex network for non-urban area were addressed as well. The proposed algorithm was applied to twenty-one TerraSAR-X SAR of New Orleans. As a result many PSs were successfully extracted even in non-urban area. This research can be used as the practical application of KOMPSAT-5 for surface displacement monitoring using X-band PSInSAR.

• Journal of the Korean Society for Marine Environment & Energy
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• v.4 no.4
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• pp.12-20
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• 2001

The temporal variability in near-field mixing characteristics of discharging plumes in oceanic environment was investigated using the time series data of the buoyant jet parameters. Based on the currents and density profiles observed in Masan outfall site and effluent discharge flowrates for 63days of summer season, the temporal variabilities and those occurrence frequency were obtained by line plume equations. The results show that wide range of variability in Masan outfall's mixing characteristics was found due to the temporal changes of effluent flowrates and ambient oceanic conditions. The near-field dilution was in the range of 30~71 with the averaged dilution of 34, which was a good agreement with field measurements of salinity deficit. The length of mixing zone was in the range of 5.4~36.2 m with the average of 9.5 m, and the plume rise height was in the range of 8.1~10.2 m with the average of 8.9 m. However, only the 30~44% of the whole results are higher than the averages, which indicates the necessity of this frequency analysis with the continuously measured data for designing and managing the ocean outfall system.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.2
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• pp.135-145
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• 2020

Wave observation is widely used to direct observation method for observing the water surface elevation using wave buoy or pressure gauge and remote-sensing wave observation method. The wave buoy and pressure gauge can produce high-quality wave data but have disadvantages of the high risk of damage and loss of the instrument, and high maintenance cost in the offshore area. On the other hand, remote observation method such as radar is easy to maintain by installing the equipment on the land, but the accuracy is somewhat lower than the direct observation method. This study investigates the data quality of MIROS Wave and Current Radar (MWR) installed at Dokdo and improve the data quality of remote wave observation data using the wave buoy (CWB) observation data operated by the Korea Meteorological Administration. We applied and developed the three types of wave data quality control; 1) the combined use (Optimal Filter) of the filter designed by MIROS (Reduce Noise Frequency, Phillips Check, Energy Level Check), 2) Spike Test Algorithm (Spike Test) developed by OOI (Ocean Observatories Initiative) and 3) a new filter (H-Ts QC) using the significant wave height-period relationship. As a result, the wave observation data of MWR using three quality control have some reliability about the significant wave height. On the other hand, there are still some errors in the significant wave period, so improvements are required. Also, since the wave observation data of MWR is different somewhat from the CWB data in high waves of over 3 m, further research such as collection and analysis of long-term remote wave observation data and filter development is necessary.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.199-208
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• 2019

As the number of available satellites increases and technology advances, image information outputs are becoming increasingly diverse and a large amount of data is accumulating. In this study, we propose a change detection method for high-resolution satellite images that uses transfer learning and a deep learning network to overcome the limit caused by insufficient training data via the use of pre-trained information. The deep learning network used in this study comprises convolutional layers to extract the spatial and spectral information and convolutional long-short term memory layers to analyze the time series information. To use the learned information, the two initial convolutional layers of the change detection network are designed to use learned values from 40,000 patches of the ISPRS (International Society for Photogrammertry and Remote Sensing) dataset as initial values. In addition, 2D (2-Dimensional) and 3D (3-dimensional) kernels were used to find the optimized structure for the high-resolution satellite images. The experimental results for the KOMPSAT-3A (KOrean Multi-Purpose SATllite-3A) satellite images show that this change detection method can effectively extract changed/unchanged pixels but is less sensitive to changes due to shadow and relief displacements. In addition, the change detection accuracy of two sites was improved by using 3D kernels. This is because a 3D kernel can consider not only the spatial information but also the spectral information. This study indicates that we can effectively detect changes in high-resolution satellite images using the constructed image information and deep learning network. In future work, a pre-trained change detection network will be applied to newly obtained images to extend the scope of the application.