• Korean Journal of Remote Sensing
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• v.12 no.2
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• pp.169-198
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• 1996

The objectives of this investigation are : 1. To analyze spectral signature and the associated vegetation index for geometric illumination conditions inf1uenced by low solar elevation and high slope orientations in mountainous forest. 2. To assess the accuracy of the spectral angle mapper classification for the a winter land cover in comparison with the maximum likelihood classification. 3. To produce the image of water quality and water properties that could be used to estimate the water pollution sources and the tide-included by turbid water in estuarine and coastal areas. These objectives are to characterize environmental and ecological monitoring applications of the Nak-Dong River Basin by using Fuyo-1 OPS VNIR data acquired on December 26, 1992. The results of this paper are as follows : 1. The spectral digital numbers and vegetation indexes (NDVI and TVI) of mountainous forest are higher on the slope facing the sun than on the slope hidden the sun under low sun elevation condition. 2. The spectral angle mapper algorithm produces a more accurate land cover classification of areas with steep slope, various aspects and low solar elevation than the maximum likelihood classifier. 3. The maximum likelihood classification images can be used for identifying the location and movement of both freshwater and salt water, regardless of geometric illumination conditions. 4. The color-coded density sliced image of selected water bodies by using the near-infrared band 3 can provide distribution of the water quality of the Lower Nak-Dong River. 5. The color-coded normalized difference vegetation index image of the selected mountain forest is suitable to classify winter vegetation cover types, i.e., forest canopy densities for slope orientations.

• Korean Journal of Remote Sensing
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• v.23 no.4
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• pp.257-272
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• 2007

This paper analyzed relationships between various features from SAR data with multiple acquisition dates and mode (frequency, polarization and incidence angles), and land-cover classes. Two typical types of features were extracted by considering acquisition conditions of currently available SAR data. First, coherence, temporal variability and principal component transform-based features were extracted from multi-temporal and single mode SAR data. C-band ERS-1/2, ENVISAT ASAR and Radarsat-1, and L-band JERS-1 SAR data were used for those features and different characteristics of different SAR sensor data were discussed in terms of land-cover discrimination capability. Overall, tandem coherence showed the best discrimination capability among various features. Long-term coherence from C-band SAR data provided a useful information on the discrimination of urban areas from other classes. Paddy fields showed the highest temporal variability values in all SAR sensor data. Features from principal component transform contained particular information relevant to specific land-cover class. As features for multiple mode SAR data acquired at similar dates, polarization ratio and multi-channel variability were also considered. VH/VV polarization ratio was a useful feature for the discrimination of forest and dry fields in which the distributions of coherence and temporal variability were significantly overlapped. It would be expected that the case study results could be useful information on improvement of classification accuracy in land-cover classification with SAR data, provided that the main findings of this paper would be confirmed by extensive case studies based on multi-temporal SAR data with various modes and ground-based SAR experiments.

• Korean Journal of Remote Sensing
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• v.24 no.4
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• pp.273-287
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• 2008

Characteristics of skin-bulk sea surface temperature (SST) differences in the Northeast Asia seas were analyzed by utilizing 845 collocated matchup data between NOAA/AVHRR data and oceanic in-situ temperature measurements for selected months from 1994 to 2003. In order to understand diurnal variation of SST within a few meters of the upper ocean, the matchup database were classified into four categories according to day-night and drifter-shipboard measurements. Temperature measurements from daytime drifters showed a good agreement with satellite MCSST (Multi-Channel Sea Surface Temperature) with an RMS error of about $0.56^{\circ}C$. Poor accuracy of SST with an rrns error of $1.12^{\circ}C$ was found in the case of daytime shipboard CTD (Conductivity, Temperature, Depth) measurements. SST differences between MCSST and in-situ measurements are caused by various errors coming from atmospheric moist effect, coastal effect, and others. Most of the remarkable errors were resulted from the diurnal variation of vertical temperature structure within a few meters as well as in-situ oceanic temperatures at different depth, about 20 cm for a satellite-tracked drifting buoy and a few meters for shipboard CTD or moored buoy. This study suggests that satellite-derived SST shows significant errors of about ${\pm}3^{\circ}C$ in some cases and therefore it should be carefully used for one's purpose on the base of in-depth understanding of skin-bulk SST difference and vertical temperature structure in regional sea.

• Korean Journal of Remote Sensing
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• v.14 no.2
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• pp.117-128
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• 1998

Topex/Poseidon satellite, launched in Auguest 1992, has provided more 5 years of very good quality data. Efficient improvements, either about instrumental accuracy or about sea level data correction, have been made so that Topex/Poseidon has become presently a wonderful tool for many researchers. The first mission data of 73 cycles, September 1992 - August 1994, was used to our study in order to know characteristics of environmental correction factors in the Amsterdam-Crozet-Kerguelen region of the South Indian Ocean. According to standard procedures as defined under user handbook for sea surface height data processes, then we have chosen cycles 43 as the cycle of reference because this cycle has provided the completed data for measurement points and has presented the exacted position of ground track compared to another cycles. It was computed variations of various factors for correction in ascending ground track 103(Amsterdam-Kerguelen continental plateau) and descending ground track170 (Crozet basin). Here the variations of ionosphere, dry troposphere, humid troposphere, electromagnetic bias, elastic tide and loading tide were generally very smaller as a few of cm, but the variations of oceanic tide(30-35cm) and inverted barometer(15-30cm) were higher than another factors. For the correction of ocean tide, our model(CEFMO: Code d' Elements Finis pour la Maree Oceanique) - This is hydrodynamic model that is very well applicated in all oceanic situations - was used because this model has especially good solution in the coastal and island area as the open sea area. Conclusionally, it should be understood that the variation of ocean free surface is mainly under the influence of tides(>80-90%) in the Amsterdam - Crozet- Kerguelen region of the South Indian Ocean.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.565-574
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• 2006

Satellite images have been used to obtain land cover information that is one of important factors for hydrological analysis over a large area. In urban area, more detailed land cover data are often required for hydrological analysis because of the relatively complex land cover types. The number of land cover classes that can be classified with traditional multispectral data is usually less than the ones required by most hydrological uses. In this study, we present the capabilities of hyperspectral data (Hyperion) for the classification of hydrological land cover types in urban area. To obtain 17 classes of urban land cover defined by the USDA SCS, spectral mixture analysis was applied using eight endmembers representing both impervious and pervious surfaces. Fractional values from the spectral mixture analysis were then reclassified into 17 cover types according to the ratio of impervious and pervious materials. The classification accuracy was then assessed by aerial photo interpretation over 10 sample plots.

• (The) Korean Journal of Advertising
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• v.29 no.7
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• pp.29-53
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• 2018

In our study, we investigated the effects of position of endorser and endorser's gaze direction(direct/averted_image/averted_text) on advertising attitude, purchase intent and brand recognition using eye-tracking method. Focusing on the printed cosmetic ads which the role of endorser is important and indirect persuade route is relatively is emphasized, we conducted experiment on 36 participants in 20s. As prior studies, our results shows that participants paid attention to more and faster on specific element which the endorser is gazing at. But it was not reflected to ad attitude and purchase intent directly. When the endorser is positioned in left the side, the highest purchase intent was shown in direct gaze condition, while when the endorser is on the right side, the highest ad attitude was shown in gazing image condition. Additionally, the brand recognition task following eye-tracking experiment shows that recognition accuracy was higher only in condition which the endorser is in the left side looking at the product image. These results demonstrated that the gaze direction of endorser plays a role as attentional guidance, which means it can lead customer's attention to particular region in the printed ad, but the effect can be varied depending on the position of endorser and which type of information the endorser is gazing at. Therefore, ultimately, to increase customer's ad attitude and purchase intent, complex consideration of not only the gazing direction of the endorser, but the position of endorser and other diverse elements is necessary.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.681-692
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• 2019

In this paper, a hybrid deep learning model, called 2D convolution with bidirectional long short-term memory (2DCBLSTM), is presented that can effectively combine both spatial and temporal features for crop classification. In the proposed model, 2D convolution operators are first applied to extract spatial features of crops and the extracted spatial features are then used as inputs for a bidirectional LSTM model that can effectively process temporal features. To evaluate the classification performance of the proposed model, a case study of crop classification was carried out using multi-temporal unmanned aerial vehicle images acquired in Anbandegi, Korea. For comparison purposes, we applied conventional deep learning models including two-dimensional convolutional neural network (CNN) using spatial features, LSTM using temporal features, and three-dimensional CNN using spatio-temporal features. Through the impact analysis of hyper-parameters on the classification performance, the use of both spatial and temporal features greatly reduced misclassification patterns of crops and the proposed hybrid model showed the best classification accuracy, compared to the conventional deep learning models that considered either spatial features or temporal features. Therefore, it is expected that the proposed model can be effectively applied to crop classification owing to its ability to consider spatio-temporal features of crops.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.737-750
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• 2019

Information of target changes in inaccessible areas is very important in terms of national security. Fast and accurate change detection of targets is very important to respond quickly. Spaceborne synthetic aperture radar can acquire images with high accuracy regardless of weather conditions and solar altitude. With the recent increase in the number of SAR satellites, it is possible to acquire images with less than one day temporal resolution for the same area. This advantage greatly increases the availability of change detection for inaccessible areas. Commonly available information in satellite SAR is amplitude and phase information, and change detection techniques have been developed based on each technology. Those are amplitude Change Detection (ACD), Coherence Change Detection (CCD). Each algorithm differs in the preprocessing process for accurate automatic classification technique according to the difference of information characteristics and the final detection result of each algorithm. Therefore, by analyzing the academic research trends for ACD and CCD, each technologies can be complemented. The goal of this paper is identifying current issues of SAR change detection techniques by collecting research papers. This study would help to find the prerequisites for SAR change detection and use it to conduct periodic detection research on inaccessible areas.

• Korean Journal of Construction Engineering and Management
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• v.20 no.6
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• pp.34-43
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• 2019

The maintenance cost for road pavement is gradually increasing due to the continuous increase in road extension as well as increase in the number of old routes that have passed the public period. As a result, there is a need for a method of minimizing costs through preventative grievance preventive maintenance requires the establishment of a strategic plan through accurate prediction of road pavement. Hence, In this study, the deep neural network(DNN) and the recurrent neural network(RNN) were used in order to develop the expressway pavement damage prediction model. A superior model among these two network models was then suggested by comparing and analyzing their performance. In order to solve the RNN's vanishing gradient problem, the LSTM (Long short-term memory) circuits which are a more complicated form of the RNN structure were used. The learning result showed that the RMSE value of the RNN-LSTM model was 0.102 which was lower than the RMSE value of the DNN model, indicating that the performance of the RNN-LSTM model was superior. In addition, high accuracy of the RNN-LSTM model was verified through the comparison between the estimated average road pavement condition and the actually measured road pavement condition of the target section over time.

• Journal of Energy Engineering
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• v.28 no.3
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• pp.1-9
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• 2019

A major stress determining the remaining life of the tube in feedwater heater of fossil fuel power plant is hoop stress by the internal pressure. However, thermal stress due to temperature difference across the wall thickness also contributed to reduce the remaining life of the tube. Therefore, thermal loading must be considered even though the contribution of internal pressure loading to the stresses of the tube was known to be much higher than that of the thermal loading. In this study, thermal stress of the tubes in the de-superheating zone was estimated, which was generated due to the temperature difference across the tube thickness. Analytic equations were shown for determining the hoop stress and the radial stress of the tube with uniform thinning and for the temperature across the tube thickness. Accuracy and effectiveness of the analytic equations for the stresses were verified by comparing the results obtained by the analytic equations with those obtained from finite element analysis. Using finite element analysis, the stresses for eccentric thinning were also determined. The effect of heat transfer coefficient on thermal stress was investigated using series of finite element analyses with various values of heat transfer coefficient for both inner and outer surface of the tube. It was shown that the effect of heat transfer coefficient at outer surface was larger than that of heat transfer coefficient at inner surface on the thermal stress of the tube. Also, the hoop stress was larger than the radial stress for both cases of uniformly and eccentrically thinned tubes when the thermal loading was only considered without internal pressure loading.