• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.4
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• pp.589-593
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• 2013

Remote sensing of SAR images is suitable for sea ice observations to obtain the sea ice data if clouds or weather conditions change. There are various types of sea ice, classification results can be seen more easily to detect the change by types of sea ice. In this study, we classified the image by supervised classification method, which is minimum distance was used. Also, we compared the overall accuracy when compared to the results with classification result of SAR images and the result of texture images. When using Radarsat-2 texture images, the overall accuracy was the highest, generally, when using the SAR images had higher overall accuracy.

• Journal of the Society of Naval Architects of Korea
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• v.48 no.5
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• pp.457-464
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• 2011

This paper deal with the validation of correction method of ice strength and thickness to the sea trial condition based on the ice model test results. It is very difficult to conduct the model test corresponding to the sea trial condition exactly. In addition, the available sea trial data is not sufficient for the validation of correction method. In the present study, the model test results of Terry-Fox ice breker have been used to compare the corrected results of sea trial test by varying its thickness and strength of model ice. The HSVA and ITTC methods have been applied to the present comparisions and the required power has been also validated by using the HSVA method. There are rather good agreement between the sea trial result and model test corrected by the HSVA and ITTC method. The more comparisons are expected to be carried out in near future.

• Ocean and Polar Research
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• v.44 no.1
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• pp.69-82
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• 2022

The Ross Sea, Antarctica plays an important role in the formation of Antarctic Bottom Water (AABW) which is the densest water mass in global thermohaline circulation. Of the AABW, 25% is formed in the Ross Sea, and sea ice formation at the polynya (ice-free area) developed in front of ice shelves of the Ross Sea is considered as a pivotal mechanism for AABW production. For this reason, monitoring the Ross Sea variations is very important to understand changes of global thermohaline circulation influenced by climate change. In addition, the Ross Sea is also regarded as a natural laboratory in investigating ice-ocean interactions owing to the development of the polynya. In this article, I introduce characteristics of the Ross Sea described in previous observational studies, and investigate variations that have occurred in the Ross Sea in the past and those taking place in the present. Furthermore, based on these observational results, I outline variations or changes that can be anticipated in the Ross Sea in the future, and make an appeal to researchers regarding the importance and necessity of continuous observations in the Ross Sea.

• Korean Journal of Remote Sensing
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• v.4 no.1
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• pp.1-16
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• 1988

Multiyear ice is quite thick in general, and it needs to be distinguished from thinner types of ice because it represents a severe navigational hazard. Here, models are described for the radar backscatter from multiyear sea ice, based on simple scattering layers. Under cold conditions, the radiative transfer volume-scatter model can describe the backscattering from multiyear ice for frequencies higher than about X-band, while the surface scattering contribution has to be included for lower frequencies. A simple semi-empirical model is shown to be a good approximation to the radiative transfer model in describing the volume scattering from multiyear ice.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1251-1260
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• 2018

High-quality sea-ice surface models generated from aerial images can be used effectively as field data for developing satellite-based remote sensing methods but also as analysis data for understanding geometric variations of Arctic sea-ice. However, the lack of texture information on sea-ice surfaces can reduce the accuracy of image matching. In this paper, we analyze the performance of matching cost functions for homogeneous sea-ice surfaces as a part of high-quality sea-ice surface model generation. The matching cost functions include sum of squared differences (SSD), normalized cross-correlation (NCC), and zero-mean normalized cross-correlation (ZNCC) in image domain and phase correlation (PC), orientation correlation (OC), and gradient correlation (GC) in frequency domain. In order to analyze the matching performance for texture changes clearly and objectively, a new evaluation methodology based on the principle of object-space matching technique was introduced. Experimental results showed that it is possible to secure reliability and accuracy of image matching only when optimal search windows are variably applied to each matching point in textureless regions such as sea-ice surfaces. Among the matching cost functions, NCC and ZNCC showed the best performance for texture changes.

• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.3
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• pp.490-499
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• 2015

In this study, a local ice pressure prediction has been conducted by using measured data from two ice breaking tests that was conducted for a relatively big ice floe at Amundsen Sea in the Antarctica from January 31 to March 30 2012. The symmetry of load was considered by attaching strain gauges on the same sites inside the shell plating of ship at the port and the starboard sides in the bow thrust room. Using measured strain data, after the ice pressure was converted by the influence coefficient method and the direct method, the two values were found to be similar.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1239-1249
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• 2018

Optical remote sensing sensors provide visually more familiar images than radar images. However, it is difficult to discriminate sea ice types in optical images using spectral information based machine learning algorithms. This study addresses two topics. First, we propose a semantic segmentation which is a part of the state-of-the-art deep learning algorithms to identify ice types by learning hierarchical and spatial features of sea ice. Second, we propose a new approach by combining of semi-supervised and active learning to obtain accurate and meaningful labels from unlabeled or unseen images to improve the performance of supervised classification for multiple images. Therefore, we successfully added new labels from unlabeled data to automatically update the semantic segmentation model. This should be noted that an operational system to generate ice type products from optical remote sensing data may be possible in the near future.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.301-304
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• 2008

Since longer wavelength microwave radiation can penetrate clouds, satellite passive microwave sensors can observe sea ice of the entire polar region on a daily basis. Thus, it is becoming popular to derive sea ice motion vectors from a pair of satellite passive microwave sensor images observed at one or few day interval. Usually, the accuracies of derived vectors are validated by comparing with the position data of drifting buoys. However, the number of buoys for validation is always quite limited compared to a large number of vectors derived from satellite images. In this study, the sea ice motion vectors automatically derived from pairs of AMSR-E 89GHz images (IFOV = 3.5 ${\times}$ 5.9km) by an image-to-image cross correlation were validated by comparing with sea ice motion vectors manually derived from pairs of cloudless MODIS images (IFOV=250 ${\times}$ 250m). Since AMSR-E and MODIS are both on the same Aqua satellite of NASA, the observation time of both sensors are the same. The relative errors of AMSR-E vectors against MODIS vectors were calculated. The accuracy validation has been conducted for 5 scenes. If we accept relative error of less than 30% as correct vectors, 75% to 92% of AMSR-E vectors derived from one scene were correct. On the other hand, the percentage of correct sea ice vectors derived from a pair of SSM/I 85GHz images (IFOV = 15 ${\times}$ 13km) observed nearly simultaneously with one of the AMSR-E images was 46%. The difference of the accuracy between AMSR-E and SSM/I is reflecting the difference of IFOV. The accuracies of H and V polarization were different from scene to scene, which may reflect the difference of sea ice distributions and their snow cover of each scene.

• International Journal of Naval Architecture and Ocean Engineering
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• v.5 no.2
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• pp.210-226
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• 2013

Ever since the Arctic region has opened its mysterious passage to mankind, continuous attempts to take advantage of its fastest route across the region has been made. The Arctic region is still covered by thick ice and thus finding a feasible navigating route is essential for an economical voyage. To find the optimal route, it is necessary to establish an efficient transit model that enables us to simulate every possible route in advance. In this work, an enhanced algorithm to determine the optimal route in the Arctic region is introduced. A transit model based on the simulated sea ice and environmental data numerically modeled in the Arctic is developed. By integrating the simulated data into a transit model, further applications such as route simulation, cost estimation or hindcast can be easily performed. An interactive simulation system that determines the optimal Arctic route using the transit model is developed. The simulation of optimal routes is carried out and the validity of the results is discussed.

• Atmosphere
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• v.32 no.4
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• pp.297-306
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• 2022

We examined potential seasonal prediction of the Korean surface temperature using the relationships between the Arctic Sea Ice Area (SIA) in autumn and the temperature in the following July and February at 850 hPa in East Asia (EA). The Surface Air Temperature (SAT) over Korea shows a similar relationship to that for EA. Since 2007, reduction of autumn SIA has been followed by warming in Korea in July. The regional distribution shows strong correlations in the southern and eastern coastal areas of Korea. The correlations in the sea surface temperature shows the maximum values in July around the Korean Peninsula, consistent with the coastal regions in which the maximum correlations in the Korean SAT are seen. In February, the response of the SAT to the SIA is the opposite of that for the July temperature. The autumn sea ice reduction is followed by cooling over Korea in February, although the magnitude is small. Cooling in the Korean Peninsula in February may be related to planetary wave-like features. Examining the autumn Arctic sea ice variation would be helpful for seasonal prediction of the Korean surface temperature, mostly in July and somewhat in February. Particularly in July, the regression line would be useful as supplementary information for seasonal temperature prediction.