• Journal of the Korean earth science society
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• v.40 no.5
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• pp.502-517
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• 2019

Global warming and rapid climate change have long affected the characteristics of typhoons in the Northwest Pacific, which has induced increasing devastating disasters along the coastal regions of the Korean peninsula. Synthetic Aperature Radar (SAR), as one of the microwave sensors, makes it possible to produce high-resolution sea surface wind field around the typhoon under cloudy atmospheric conditions, which has been impossible to obtain the winds from satellite optical and infrared sensors. The Geophysical Model Functions (GMFs) for sea surface wind retrieval from SAR data requires the input of wind direction, which should be based on the accurate estimation of the center of the typhoon. This study estimated the typhoon centers using Sentinel-1A images to improve the problem of typhoon center detection method and to reflect it in retrieving the sea surface wind. The results were validated by comparing with the typhoon best track data provided by the Korea Meteorological Administration (KMA) and Japan Meteorological Agency (JMA), and also by using infrared images of Himawari-8 satellite. The initial center position of the typhoon was determined by using VH polarization, thereby reducing the possibility of error. The detected center showed a difference of 23.76 km on average with the best track data of the four typhoons provided by the KMA and JMA. Compared to the typhoon center estimated by Himawari-8 satellite, the results showed an average spatial variation of 11.80 km except one typhoon located near land with a large difference of 58.73 km. This result suggests that high-resolution SAR images can be used to estimate the center and retrieve sea surface wind around typhoons.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.435-444
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• 2011

The land cover map derived from spectral features of high resolution optical images has low spectral resolution and heterogeneity in the same land cover class. For this reason, despite the same land cover class, the land cover can be classified into various land cover classes especially in vegetation area. In order to overcome these problems, detailed vegetation classification is applied to optical satellite image and SAR(Synthetic Aperture Radar) integrated data in vegetation area which is the result of pre-classification from optical image. The pre-classification and vegetation classification were performed with MLC(Maximum Likelihood Classification) method. The hierarchical land cover classification was proposed from fusion of detailed vegetation classes and non-vegetation classes of pre-classification. We can verify the facts that the proposed method has higher accuracy than not only general SAR data and GLCM(Gray Level Co-occurrence Matrix) texture integrated methods but also hierarchical GLCM integrated method. Especially the proposed method has high accuracy with respect to both vegetation and non-vegetation classification.

• Proceedings of the KSRS Conference
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• v.2
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• pp.942-942
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• 2006

• Korean Journal of Remote Sensing
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• v.19 no.5
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• pp.351-361
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• 2003

As the launches of a series of remote sensing satellites, there are various multiresolution and multi-spectral images available nowadays. This diversity in remotely sensed image data has created a need to be able to integrate data from different sources. The C-band imaging radar of ERS-2 due to its high sensitivity to coastal wetlands holds tremendous potential in mapping and monitoring coastal wetland features. This paper investigates the advantages of using ERS-2 SAR data combined with IRS-ID LISS-3 data for mapping complex coastal wetland features of Tamil Nadu, southern India. We present a methodology in this paper that highlights the mapping potential of different combinations of filtering and integration techniques. The methodology adopted here consists of three major steps as following: (i) speckle noise reduction by comparative performance of different filtering algorithms, (ii) geometric rectification and coregistration, and (iii) application of different integration techniques. The results obtained from the analysis of optical and microwave image data have proved their potential use in improving interpretability of different coastal wetland features of southern India. Based visual and statistical analyzes, this study suggests that brovey transform will perform well in terms of preserving spatial and spectral content of the original image data. It was also realized that speckle filtering is very important before fusing optical and microwave data for mapping coastal mangrove wetland ecosystem.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.327-336
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• 2019

The caldera lake "Chun-ji" is located at the summit of Baekdu volcano, which is in the border of China and North Korea. Chun-ji Lake has altitude 2,189 m above sea level. The Chun-ji is freezing in the winter when the water temperature goes down to zero for a year, and it melts in the season when the water temperature goes up again. However,since it is located at a high altitude, there are many cloudy days, and it is difficult to observe with optical images. For this reason, radar images, which are less influenced by weather than optical images, are more effective for observing the ice of heaven and earth. In this study, 75 TerraSAR-X images from chun-ji area were used for analysis from 2015 to 2017, and the calculated ice area and temperature changes were analyzed. As a result, the ice of the caldera lake formed was formed in early December and slowly melted until mid-April. During this period, temperatures in the Samjiyeon area were about $-10^{\circ}C$ when ice was produced, and the temperature was about $0^{\circ}C$ in mid-April when it was thawing. Correlation coefficients between ice surface area and temperature in winter 2015 and 2016, where global ice is produced,show a high correlation of -0.82 and -0.75. In addition to the results of this study, it can be used as an indicator to monitor the volcanic activity by comparing the result of the recent volcanic activity with the result of the increase in water temperature using various imagery.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.8
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• pp.710-717
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• 2016

Satellite payload can be classified as electro-optical payload, SAR, microwave radiometer, communication payload, navigation payload and so on in accordance with the mission objective. The technology of satellite payload was tried to be obtained through development of KOMPSAT series, COMS and STSAT in Korea. In this paper, the required technology for the development and world market trend of satellite payload were studied and described. Since KOMPSAT program has been started in 1994, technology status and future prospects of satellite payload in Korea are studied and analyzed.

• Journal of Aerospace System Engineering
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• v.17 no.4
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• pp.1-9
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• 2023

Recently, as Synthetic Aperture Radar (SAR), communication, and signal surveillance missions of spacecraft have become more advanced, research has been actively conducted on the deployable large mesh antenna system with excellent storage efficiency compared to the deployment area, and light weight. Deployable Mesh antennae are characterized by an increase in the number of Openings Per Inch (OPI), which is a measure of mesh weaving density as the mission frequency band increases, and this OPI change directly affects the thermal optical properties of the mesh antenna, so research on this is required. In this paper, to verify the thermal relationship between the optical properties of the mesh and antenna reflector, thermal sensitivity analysis between the mesh and the antenna reflector is performed by in-orbit thermal analysis with various optical characteristics of the mesh based on existing overseas research cases. In addition, the temperature gradient effect of the mesh reflector is analyzed.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.253-256
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• 2005

The use of satellite remote sensing in maritime safety and security can aid in the detection of illegal fishing activities and provide more efficient use of limited aircraft or patrol craft resources. In the area of vessel traffic monitoring for commercial vessels, Vessel Traffic Service (VTS) which use the ground-based radar system have some difficulties in detecting moving ships due to the limited detection range. A virtual vessel traffic control system is introduced to contribute to prevent a marine accident such as collision and stranding from happening. Existing VTS has its limit. The virtual vessel traffic control system consists of both data acquisition by satellite remote sensing and a simulation of traffic environment stress based on the satellite data, remotely sensed data. And it could be used to provide timely and detailed information about the marine safety, including the location, speed and direction of ships, and help us operate vessels safely and efficiently. If environmental stress values are simulated for the ship information derived from satellite data, proper actions can be taken to prevent accidents. Since optical sensor has a high spatial resolution, JERS satellite data are used to track ships and extract their information. We present an algorithm of automatic identification of ship size and velocity. This paper lastly introduce the field testing results of ship detection by RADARSAT SAR imagery, and propose a new approach for a Vessel Monitoring System(VMS), including VTS, and SAR combination service.

• Journal of Korea Multimedia Society
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• v.25 no.7
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• pp.895-902
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• 2022

Building segmentation using satellite imagery such as EO (Electro-Optical) and SAR (Synthetic-Aperture Radar) images are widely used due to their various uses. EO images have the advantage of having color information, and they are noise-free. In contrast, SAR images can identify the physical characteristics and geometrical information that the EO image cannot capture. This paper proposes a learning framework for efficient building segmentation that consists of a teacher-student-based privileged knowledge distillation and deformable convolution block. The teacher network utilizes EO and SAR images simultaneously to produce richer features and provide them to the student network, while the student network only uses EO images. To do this, we present objective functions that consist of Kullback-Leibler divergence loss and knowledge distillation loss. Furthermore, we introduce deformable convolution to avoid pixel-level noise and efficiently capture hard samples such as small and thin buildings at the global level. Experimental result shows that our method outperforms other methods and efficiently captures complex samples such as a small or narrow building. Moreover, Since our method can be applied to various methods.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1084-1086
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• 2003

The aim of this study is to assess the integrated use of different features extracted from spaceborne interferometric synthetic aperture radar (InSAR) data and optical data for land cover classification. Special attention is given to the discriminatory characteristics of the features derived from the multisource data sets. For the evaluation of the features , the statistical maximum likelihood decision rule and neural network classification are used and the results are compared. The performance of each method was evaluated by measuring the overall accuracy. In all cases, the performance of the first method was better than the performance of the latter one. Overall, the research indicated that multisource data sets containing different information about backscattering and reflecting properties of the selected classes of objects can significantly improve the classification of land cover types.