• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.99-104
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• 2006

The new gravity field combination models are expected to improve the knowledge of the Earth's global gravity field. This study evaluates eleven global gravity field models derived from gravimetry and altimetry surface data in a comparison with ground truth in South Korea. Geoid heights obtained from GPS and levelling in South Korea are compared with geoid heights from the models. The results show that the gravity satellites CHAMP, GRACE and LAGEOS plus gravimetry and altimetry surface data have led to an improvement in gravity field models. As expected, the new combination gravity field model which are EIGEN-CG03C and EIGEN-GL04C give better results than the predecessors widely used models(EGM96, OSU91A etc.).

• Journal of information and communication convergence engineering
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• v.16 no.2
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• pp.114-124
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• 2018

This paper illustrates a vision-based vehicle positioning method at an intersection to support the C-ITS. It removes the minor shadow that causes the merging problem by simply eliminating the fractional parts of a quotient image. In order to separate the occlusion, it firstly performs the distance transform to analyze the contents of the single foreground object to find seeds, each of which represents one vehicle. Then, it applies the watershed to find the natural border of two cars. In addition, a general vehicle model and the corresponding space estimation method are proposed. For performance evaluation, the corresponding ground truth data are read and compared with the vision-based detected data. In addition, two criteria, IOU and DEER, are defined to measure the accuracy of the extracted data. The evaluation result shows that the average value of IOU is 0.65 with the hit ratio of 97%. It also shows that the average value of DEER is 0.0467, which means the positioning error is 32.7 centimeters.

• Journal of Broadcast Engineering
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• v.24 no.2
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• pp.234-242
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• 2019

In this paper, we propose a deep neural network which extracts SIFT feature points by determining whether the center pixel of a cropped image is a SIFT feature point. The data set of this network consists of a DIV2K dataset cut into $33{\times}33$ size and uses RGB image unlike SIFT which uses black and white image. The ground truth consists of the RobHess SIFT features extracted by setting the octave (scale) to 0, the sigma to 1.6, and the intervals to 3. Based on the VGG-16, we construct an increasingly deep network of 13 to 23 and 33 convolution layers, and experiment with changing the method of increasing the image scale. The result of using the sigmoid function as the activation function of the output layer is compared with the result using the softmax function. Experimental results show that the proposed network not only has more than 99% extraction accuracy but also has high extraction repeatability for distorted images.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1567-1577
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• 2020

Urban land cover classification is role in urban planning and management. So, it's important to improve classification accuracy on urban location. In this paper, machine learning model, Support Vector Machine (SVM) and Artificial Neural Network (ANN) are proposed for urban land cover classification based on high resolution satellite imagery (KOMPSAT-3A). Satellite image was trained based on 25 m rectangle grid to create training data, and training models used for classifying test area. During the validation process, we presented confusion matrix for each result with 250 Ground Truth Points (GTP). Of the four SVM kernels and the two activation functions ANN, the SVM Polynomial kernel model had the highest accuracy of 86%. In the process of comparing the SVM and ANN using GTP, the SVM model was more effective than the ANN model for KOMPSAT-3A classification. Among the four classes (building, road, vegetation, and bare-soil), building class showed the lowest classification accuracy due to the shadow caused by the high rise building.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.376-381
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• 1998

In this study, we was measured the radiance reflectance by using multi-spectral image of low resolution camera(LRC) which will be loaded in the multi-purpose satellite(KOMPSAT) to use the data in analyzing water pollution. Also we investigated the possibility of extraction of water quality factors in rivers and water body by using high resolution remote sensing data such as Airborne MSS. Especially, we tried to extract the environmental factors related with eutrophication, and also tried to develop the process technique and the radiance feature of reflectance related with eutrophication. The results were summarized as follows: First, the spectrum of sun's rays which reaches the surface of the earth was consistent with visible rays bands of 0.4${\mu}{\textrm}{m}$~0.7${\mu}{\textrm}{m}$ and about 50% of total quantity of radiation were there. And at around 0.5${\mu}{\textrm}{m}$ of green spectral band in visible rays bands, the spectrum was highest. Second, as a result of the radiance reflectance Chlorophyll-a represented high spectral reflectance mainly around 0.52${\mu}{\textrm}{m}$ of green spectral band, and suspended sediments and turbidity represented high spectral reflectance at 0.8${\mu}{\textrm}{m}$ and at 0.57${\mu}{\textrm}{m}$ each. Third, as a result of the water quality analysis by using Airborne MSS, Chlorophyll-a could have a distribution chart when carried out ratio of B3 and BS to B7. And Band 7 was useful for making the distribution chart of suspended sediments. And when we carried out PCA, suspended sediments and turbidity had distributions at PC 1 , PC 4 each similarly to ground truth data. Above results can be changed according to the change of season and time. Therefore, in order to analyze more exactly the environmental factors of water quality by using LRC data, we need to investigate constantly the ground truth data and the radiance feature of reflectance of water body. Afterward in this study, we will constantly analyze the radiance feature of the surface of water in water body by measuring the on-the-spot radiance reflectance and using low resolution satellite image(SeaWiFs). Besides, we will gather the data of water quality analysis in water body and analyze the pattern of water pollution.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.23-31
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• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.

• International journal of advanced smart convergence
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• v.7 no.2
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• pp.33-37
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• 2018

In this study, we propose an iterative clustering algorithm that automatically clusters a set of voice signal data without a label into an optimal number of clusters and generates hmm model for each cluster. In the clustering process, the likelihood calculations of the clusters are performed using iterative hmm learning and testing while varying the number of clusters for given data, and the maximum likelihood estimation method is used to determine the optimal number of clusters. We tested the effectiveness of this clustering algorithm on a small-vocabulary digit clustering task by mapping the unsupervised decoded output of the optimal cluster to the ground-truth transcription, we found out that they were highly correlated.

• ETRI Journal
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• v.23 no.3
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• pp.129-137
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• 2001

A random sampling or systematic sampling method is commonly used to assess the accuracy of classification results. In remote sensing, with these sampling methods, much time and tedious work are required to acquire sufficient ground truth data. So, a more effective sampling method that can represent the characteristics of the population is required. In this study, fractal analysis is adopted as an index for reference sampling. The fractal dimensions of the whole study area and the sub-regions are calculated to select sub-regions that have the most similar dimensionality to that of the whole area. Then the whole area's classification accuracy is compared with those of sub-regions, and it is verified that the accuracies of selected sub-regions are similar to that of whole area. A new kind of reference sampling method using the above procedure is proposed. The results show that it is possible to reduce sampling area and sample size, while keeping the same level of accuracy as the existing methods.

• Korean Journal of Remote Sensing
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• v.21 no.3
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• pp.221-228
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• 2005

Baekdusan is a Cenozoic stratovolcano in which a series of micro-seismic events and gaseous emissions have been reported in 1990s. Two-pass DInSAR technique was applied to determine displacement in the volcano by using 10 ERS SAR and 41 JERS-1 SAR datasets. Most interferometric phases out of 58 JERS-1 differential interferograms showed concentric fringe patterns that correlated with elevation. From an analysis of fringe-duration relation, the fringe patterns were found to be severely distorted specifically by stratified troposphere. To estimate the tropospheric delay, we used the data in the Sobaeksan located about 20 km away from the summit of Baekdusan. The maximum and mean magnitudes of the phase delay in the Baekdusan were respectively 13.8 cm and 3.8 cm over 1200 m in altitude. After removing tropospheric effects, a mean inflation rate was estimated to be about 3 mm per year from 1992 to 1998. Although the inflation rate of the volcano is inconclusive without ground truth data, the results indicate that there exists slow upward deformation in the Baekdusan volcano.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.46.2-46.2
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• 2021

We present solar event auto detection using deep-learning-based object detection algorithms and DeepSDO event dataset. DeepSDO event dataset is a new detection dataset with bounding boxed as ground-truth for three solar event (coronal holes, sunspots and prominences) features using Solar Dynamics Observatory data. To access the reliability of DeepSDO event dataset, we compared to HEK data. We train two representative object detection models, the Single Shot MultiBox Detector (SSD) and the Faster Region-based Convolutional Neural Network (R-CNN) with DeepSDO event dataset. We compared the performance of the two models for three solar events and this study demonstrates that deep learning-based object detection can successfully detect multiple types of solar events. In addition, we provide DeepSDO event dataset for further achievements event detection in solar physics.