• Journal of Digital Contents Society
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• v.19 no.7
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• pp.1333-1339
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• 2018

Recently, as disasters caused by weather such as heavy rains have increased, interests in forecasting weather and disasters using radars have been increasing, and related studies have also been actively performed. As the Ministry of Environment(ME) has established and operated a radar network on a national scale, utilization of radars has been emphasized. However, persons in charge and researchers, who want to use the data from radars need to understand characteristics of the radar data and are also experiencing a lot of trials and errors when converting and calibrating the radar data from Universal Format(UF) files. Hence, this study developed a Radar Display and Analysis Program(RaDAP) based on Graphic User Interface(GUI) using the Java Programming Language in order for UF-type radar data to be generated in an ASCII-formatted image file and text file. The developed program can derive desired radar rainfall data and minimize the time required to perform its analysis. Therefore, it is expected that this program will contribute to enhancing the utilization of radar data in various fields.

• The Journal of Korea Robotics Society
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• v.18 no.3
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• pp.271-280
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• 2023

FMCW (Frequency Modulated Continuous Wave) radar system is widely used in autonomous driving and navigation applications due to its high detection capabilities independent of weather conditions and environments. However, radar signals can be easily contaminated by various noises such as speckle noise, receiver saturation, and multipath reflection, which can worsen sensing performance. To handle this problem, we propose a learning-free noise removal technique for radar to enhance detection performance. The proposed method leverages adaptive thresholding to remove speckle noise and receiver saturation, and wavelet transform to detect multipath reflection. After noise removal, the radar image is reconstructed with the geometric structure of the surrounding environments. We verify that our method effectively eliminated noise and can be applied to autonomous driving by improving the accuracy of odometry and place recognition.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.4
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• pp.392-400
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• 2021

Unlike optical equipment, SAR(Synthetic Aperture Radar) has the advantage of obtaining images in all weather, and object detection in SAR images is an important issue. Generally, deep learning-based object detection was mainly performed in real-valued network using only amplitude of SAR image. Since the SAR image is complex data consist of amplitude and phase data, a complex-valued network is required. In this paper, a complex-valued ResNet network is proposed. SAR image object detection was performed by combining the ROI transformer detector specialized for aerial image detection and the proposed complex-valued ResNet. It was confirmed that higher accuracy was obtained in complex-valued network than in existing real-valued network.

• Journal of Satellite, Information and Communications
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• v.3 no.1
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• pp.48-54
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• 2008

With the increasing events of natural disasters caused by unpredictable atmospheric movements, the importance of weather forecasting is increasingly emphasized. In this paper, we adopt satellite radar imageries to deal with unusual weather events over Korean region including yellow sand that swept over Korea in spring 2007 and typhoon EWNIAR in 2006. Korea has suffered from these natural events with increasing frequencies over last decades and the satellite radar imaging is considered the most appropriate method to track and analyze the characteristics of the events spanning from mainland China to Japan. The yellow sand mostly comes from Manju area in China and consists of tiny particles so that they move with high speed resulting in difficulty in predicting their moving paths. With the use of various radar images taken at regular time intervals, we could possibly derive the expected movement of the yellow sand particles. In the future, with the help of radar images taken at very short intervals, satellite radar image analysis will become a very useful tool to predict and prepare for the natural disastrous events caused by abrupt change in the atmosphere and deserts around Korea.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.3
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• pp.475-482
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• 2023

Synthetic Aperture Radar (SAR), which provides images of targets using radio signals, enables monitoring at all times regardless of weather conditions. In this paper, the SAR system was installed on the test aircraft to collect SAR raw data on the ground and the sea, and the results of image formation using the backprojection algorithm were presented.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.12
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• pp.1813-1819
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• 2013

Existing level crossing obstacle detecting system was installed using a laser beam. Level crossing obstacle detecting system using a laser beam that has been a problem in relation to safety and maintainability failure according to weather conditions. We proposed laser radar level crossing obstacle detecting system as a way to overcome problem, and we developed an algorithm for this. Level crossing obstacle detecting system using a laser radar sensor algorithm is robust to external environment and a shadow zone does not exist. Sensor part of the laser radar level crossing obstacle detecting system of these is made up by the image processing unit and laser radar sensor, it operations by receiving train entering information from the control unit. In this paper, we proposed a detecting algorithm with calculation of the size of the laser radar sensor. Based on this, we were performance test on the basis of the scenario by making a prototype. In the future, laser radar level crossing obstacle detecting system to ensure the safety and reliability through the field test.

• Journal of Navigation and Port Research
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• v.26 no.1
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• pp.28-34
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• 2002

Irregular reflected signals on a sea surface make clutters to a ship's radar image. Clutters are similar to Gaussian white noises which are very harmful for detecting objecting at sea by a ship's radar. To remove the clutter effects, many papers show the algorithms by antenna, filters, and so on. This paper shows a new algorithm which uwes Wavelet and Morphology median filter conceps for removing clutter and enhancing image in order to detect well a distressed of being rescued ship in a rough weather condition at sea.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.6_1
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• pp.517-528
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• 2007

Extraction of the coastal boundary is important because the boundary serves as a reference in the demarcation of maritime zones such as territorial sea, contiguous zone, and exclusive economic zone. Accurate nautical charts also depend on well established, accurate, consistent, and current coastline delineation. However, to identify the precise location of the coastal boundary is a difficult task due to tidal and wave motions. This paper presents an efficient way to extract coastlines by applying digital image processing techniques to Synthetic Aperture Radar (SAR) imagery. Over the past few years, satellite-based SAR and high resolution airborne SAR images have become available, and SAR has been evaluated as a new mapping technology. Using remotely sensed data gives benefits in several aspects, especially SAR is largely unaffected by weather constraints, is operational at night time over a large area, and provides high contrast between water and land areas. Various image processing techniques including region growing, texture-based image segmentation, local entropy method, and refinement with image pyramid were implemented to extract the coastline in this study. Finally, the results were compared with existing coastline data derived from aerial photographs.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.4
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• pp.319-326
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• 2017

SAR (Synthetic aperture radar) images are less affected by the weather compared to optical images and can be obtained at any time of the day. Therefore, SAR images are being actively utilized for military applications and natural disasters. However, because SAR data are in grayscale, it is difficult to perform visual analysis and to decipher details. In this study, we propose a color mapping method using RF (random forest) regression for enhancing the visual decipherability of SAR images. COSMO-SkyMed 2 and WorldView-3 images were obtained for the same area and RF regression was used to establish color configurations for performing color mapping. The results were compared with image fusion, a traditional color mapping method. The UIQI (universal image quality index), the SSIM (structural similarity) index, and CC (correlation coefficients) were used to evaluate the image quality. The color-mapped image based on the RF regression had a significantly higher quality than the images derived from the other methods. From the experimental result, the use of color mapping based on the RF regression for SAR images was confirmed.

• Proceedings of KOSOMES biannual meeting
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• 2006.05a
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• pp.41-45
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• 2006

From the 1978 Seasat synthetic aperture radar(SAR) to present systems, spaceborne SAR has demonstrated the capability to image the Earth's ocean and land features over broad areas, day and night, and under most weather conditions. The application of SAR for surveillance of commercial fishing grounds can did in the detection of illegal fishing activities and provides more efficient use cf limited aircraft or patron craft resources. In the area of vessel traffic monitoring for commercial vessels, Vessel Traffic Service (VTS) which uses the ground-based radar system has some difficulties in detecting moving ships due to the limited detection range cf about 10 miles. This paper introduces the field testing results of ship detection by RADARSAT SAR imagery, and proposes a new approach for a Vessel Monitoring System(VMS), including VTS, and SAR combination service.