• Korean Journal of Remote Sensing
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• v.34 no.2_2
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• pp.313-326
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• 2018

This paper introduces core techniques on ship detection and tracking based on a compact HF radar platform which is necessary to establish a wide-area surveillance network. Currently, most HF radar sites are primarily optimized for observing sea surface radial velocities and bearings. Therefore, many ship detection systems are vulnerable to error sources such as environmental noise and clutter when they are applied to these practical surface current observation purpose systems. In addition, due to Korea's geographical features, only compact HF radars which generates non-uniform antenna response and has no information on target information are applicable. The ship detection and tracking techniques discussed in this paper considers these practical conditions and were evaluated by real data collected from the Yellow Sea, Korea. The proposed method is composed of two parts. In the first part, ship detection, a constant false alarm rate based detector was applied and was enhanced by a PCA subspace decomposition method which reduces noise. To merge multiple detections originated from a single target due to the Doppler effect during long CPIs, a clustering method was applied. Finally, data association framework eliminates false detections by considering ship maneuvering over time. According to evaluation results, it is claimed that the proposed method produces satisfactory results within certain ranges.

• Journal of Navigation and Port Research
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• v.36 no.8
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• pp.625-630
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• 2012

The DGNSS Central Office operates 17 DGNSS reference stations. Compared to the other DGNSS sites, the TEQC data quality of some sites is poorer. In this study, we tried to find out the causes that degrade the quality of GPS data for the purpose of improving the signal quality of the DGNSS stations. We selected the Chungju station that is the one of those stations with bad data quality. Through the on-site visit, we found that there is no signal-blocking obstacles. In addition to site surveys, we conducted two experiments; simultaneous observation considering environmental factors and comparison test through equipment replacements to check the malfunctioning of GPS equipments. In the simultaneous test results, we realized that environmental factors do not induce any bad effects on the data quality. In equipment replacement experiments, we confirmed that the data quality is of excellent quality when the test receiver was used instead of the original one installed at the site. When we replaced the antenna instead of the receiver, the data quality was bad. Through those two experiments, we concluded that the receiver is the main factor that degrades the signal quality.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1415-1425
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• 2018

Kompsat-5 is the first Earth Observation Satellite which is equipped with an SAR in Korea. SAR images are generated by receiving signals reflected from an object by microwaves emitted from a SAR antenna. Because the wavelengths of microwaves are longer than the size of particles in the atmosphere, it can penetrate clouds and fog, and high-resolution images can be obtained without distinction between day and night. However, there is no color information in SAR images. To overcome these limitations of SAR images, colorization of SAR images using Cycle GAN, a deep learning model developed for domain translation, was conducted. Training of Cycle GAN is unstable due to the unsupervised learning based on unpaired dataset. Therefore, we proposed MS Cycle GAN applying multi-scale discriminator to solve the training instability of Cycle GAN and to improve the performance of colorization in this paper. To compare colorization performance of MS Cycle GAN and Cycle GAN, generated images by both models were compared qualitatively and quantitatively. Training Cycle GAN with multi-scale discriminator shows the losses of generators and discriminators are significantly reduced compared to the conventional Cycle GAN, and we identified that generated images by MS Cycle GAN are well-matched with the characteristics of regions such as leaves, rivers, and land.