• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.744-744
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• 2012

불투수층은 자연적인 침투를 허용하지 않는 인위적인 토지피복상태로 도시화율 추정 및 유역의 환경변화 정도를 분석하기 위한 척도로 사용되어 왔다. 특히, 수문학적 관점에서 불투수층은 단기 유출현상에 큰 영향을 끼치는 요소로 불투수율이 증가할수록 침투량이 감소하여 첨두유출량은 증가하고 도달시간은 짧아진다. 최근에는 급속한 도시화로 인해 불투수층의 영향이 더욱 커짐에 따라 불투수율의 추정에 대한 필요성이 증가하고 있다. 현재까지 위성영상을 이용한 불투수층의 추정은 고해상도 영상을 이용하여 피복분류를 수행하였다. 즉, 분류된 토지피복에 근거하여 불투수율을 산술적으로 계산하거나 분광혼합기법 및 회귀 트리기법 등 다양한 방법에 적용되어 왔다. 본 연구에서는 Sub-pixel 분류기법을 위성영상에 적용하여 섬강유역의 불투수율을 추정하고자 한다. Sub-pixel 분류는 기존 분류기법들이 다양한 토지피복이 혼합된 화소에 대해서도 가장 비중이 높은 토지피복 하나로 분류하던 것을 개선한 방법으로 fuzzy 이론을 적용하여 최소 20% 이상의 비율을 점유하는 항목 모두를 구분하여 분류하는 기법이다. 이를 위해 섬강유역의 Landsat TM 영상을 수집하고 환경부의 토지피복도와 지질도를 참조하여 트레이닝 자료를 수집하였다. 또한 결과에 영향을 미칠 수 있는 구름은 전처리를 통하여 제거하고 수집된 트레이닝 자료에 Sub-pixel 분류기법을 적용하여 섬강유역의 불투수율을 공간분포도로 작성하였다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.343-348
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• 1998

In this study, we extracted several terrain information using satellite and aerial images. We detected change of terrain using Landsat Thematic Mapper(TM) and aerial images which are multitemporal data. In change detection processing, we first classified satellite images by ISODATA algorithm which is an unsupervised learning algorithm, then performed change detection. By this method, we could obtain good result. Also we introduce sub-pixel concept to classify road and agriculture area in inaccessible area. In summary, in chang detection processing, we can find that the used method is efficient.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.146-154
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• 2014

The local binary pattern (LBP) scheme that is one of the texture classification methods normally uses the distribution of flat, edge and corner patterns. However, it cannot examine the edge direction and the pixel difference because it is a sort of binary pattern caused by thresholding. Furthermore, since it cannot consider the pixel distribution, it shows lower performance as the image size becomes larger. In order to solve this problem, we propose a sub-classification method using the edge direction distribution and eigen-matrix. The proposed sub-classification is applied to the particular texture patches which cannot be classified by LBP. First, we quantize the edge direction and compute its distribution. Second, we calculate the distribution of the largest value among eigenvalues derived from structure matrix. Simulation results show that the proposed method provides a higher classification performance of about 8 % than the existing method.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.2
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• pp.121-135
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• 2014

Object-based classification methods provide a valid alternative to traditional pixel-based methods. This study reports the results of an object-based classification to examine land cover in the demilitarized zones(DMZs) of Inje-gun. We used land cover classes(7 classes for main category and 13 classes for sub-category) selected from the criteria by Korea Ministry of Environment. The average and standard deviation of the spectrum values, and homogeneity of GLCM were chosen to map land cover types in an hierarchical approach using the nearest neighborhood method. We then identified the distributional characteristics of land cover by considering 3 topographic characteristics (altitude, slope gradient, distance from the Southern Limited Line(SLL)) within the DMZs. The results showed that scale 72, shape 0.2, color 0.8, compactness 0.5 and smoothness 0.5 were the optimum weight values while scale, shape and color were most influenced parameters in image segmentation. The forests (92%) were main land cover type in the DMZs; the grassland(5%), the urban area (2%) and the forests (broadleaf forest: 44%, mixed forest: 42%, coniferous forest: 6%) also occupied mostly in land cover classes for sub-category. The results also showed that facilities and roads had higher density within 2 km from the SLL, while paddy, field and bare land were distributed largely outside 6 km from the SLL. In addition, there was apparent distinction in land cover by topographic characteristics. The forest had higher density at above altitude 600m and above slope gradient $30^{\circ}$ while agriculture, bare land and grass land were distributed mainly at below altitude 600m and below slope gradient $30^{\circ}$.

• Korean Journal of Environment and Ecology
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• v.32 no.6
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• pp.686-697
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• 2018

Due to the advance in remote sensing technology, it has become easier to more frequently obtain high resolution imagery to detect delicate changes in an extensive area, particularly including forest which is not readily sub-classified. Time-series analysis on high resolution images requires to collect extensive amount of ground truth data. In this study, the potential of land coverage mapas ground truth data was tested in classifying high-resolution imagery. The study site was Wonju-si at Gangwon-do, South Korea, having a mix of urban and natural areas. KOMPSAT-3A imagery taken on March 2015 and land coverage map published in 2017 were used as source data. Two pixel-based classification algorithms, Support Vector Machine (SVM) and Random Forest (RF), were selected for the analysis. Forest only classification was compared with that of the whole study area except wetland. Confusion matrixes from the classification presented that overall accuracies for both the targets were higher in RF algorithm than in SVM. While the overall accuracy in the forest only analysis by RF algorithm was higher by 18.3% than SVM, in the case of the whole region analysis, the difference was relatively smaller by 5.5%. For the SVM algorithm, adding the Majority analysis process indicated a marginal improvement of about 1% than the normal SVM analysis. It was found that the RF algorithm was more effective to identify the broad-leaved forest within the forest, but for the other classes the SVM algorithm was more effective. As the two pixel-based classification algorithms were tested here, it is expected that future classification will improve the overall accuracy and the reliability by introducing a time-series analysis and an object-based algorithm. It is considered that this approach will contribute to improving a large-scale land planning by providing an effective land classification method on higher spatial and temporal scales.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.553-563
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• 2006

This study aims to produce land-cover maps of Korean peninsula using multi-temporal MODIS (Moderate Resolution Imaging Spectroradiometer) imagery. To solve the low spatial resolution of MODIS data and enhance classification accuracy, Linear Spectral Mixture Analysis (LSMA) was employed. LSMA allowed to determine the fraction of each surface type in a pixel and develop vegetation, soil and water fraction images. To eliminate clouds, MVC (Maximum Value Composite) was utilized for vegetation fraction and MinVC (Minimum Value Composite) for soil fraction image respectively. With these images, using ISODATA unsupervised classifier, southern part of Korean peninsula was classified to low and mid level land-cover classes. The results showed that vegetation and soil fraction images reflected phenological characteristics of Korean peninsula. Paddy fields and forest could be easily detected in spring and summer data of the entire peninsula and arable land in North Korea. Secondly, in low level land-cover classification, overall accuracy was 79.94% and Kappa value was 0.70. Classification accuracy of forest (88.12%) and paddy field (85.45%) was higher than that of barren land (60.71%) and grassland (57.14%). In midlevel classification, forest class was sub-divided into deciduous and conifers and field class was sub-divided into paddy and field classes. In mid level, overall accuracy was 82.02% and Kappa value was 0.6986. Classification accuracy of deciduous (86.96%) and paddy (85.38%) were higher than that of conifers (62.50%) and field (77.08%).

• Journal of the Korea Society of Computer and Information
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• v.20 no.4
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• pp.57-67
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• 2015

The concept of detection and classification of objects based on infrared camera is widely applied to military applications. While the object detection technology using infrared images has long been researched and the latest one can detect the object in sub-pixel, the object classification technology still needs more research. In this paper, we present object classification method based on measured radiant intensity of objects such as target, artillery, and missile using infrared camera. The suggested classification method was verified by radiant intensity measuring experiment using black body. Also, possible measuring errors were compensated by modelling-based correction for accurate radiant intensity measure. After measuring radiation of object, the model of radiant intensity is standardized based on theoretical background. Based on this research, the standardized model can be applied to the object classification by comparing with the actual measured radiant intensity of target, artillery, and missile.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.8B
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• pp.1480-1489
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• 1999

In this paper, we propose a new post-processing method which can solve a problem of MSDS(Mean Squared Difference of Slope) method. Using that method the blocking artifacts can significantly be reduced without any restriction, which is a major drawback of block-based DCT compression method. In this approach, the OSLD(Overlapped Sub-Laplacian Distribution) of dequantized block boundary pixel difference values is defined and used to categorize each block of an image into one of four types. Those types are also classified into one of two classes: an edge and a non-edge classes. A slope across the block boundary is used to quantify discontinuity of the image. If an absolute estimated quantization error value of a DCT coefficient is greater than the corresponding quantization step size, it is saturated to the step size in the edge class. The proposed post-processing method can improve not only the PSNR value up to 0.1~O.3 dB but visual quality without any constraints determined by ad-hoc manner.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.

• Spatial Information Research
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• v.22 no.3
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• pp.47-58
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• 2014

The credible measurement, reporting and verification (MRV) is among the most critical elements in UN-REDD (United Nations programme on Reducing Emissions from Deforestation and forest Degradation in Developing Countries). This study is intended to explore MRV potential in terms of UN-REDD opportunity cost estimation using satellite image for Mt. Geum-gang of North Korea. A visual interpretation were conducted to evaluate MRV conditions by sub-dividing or decomposing the images with different pixel size into a three types of hierarchical tree structure that helps dealing with spatial variability within each subarea. The permanent record of standard satellite remote sensing system demonstrated its capability of presenting area-wide visual evidences of MRV conditions in Mt. Geum-gang (such as the identification of forested area, degradation trends for forest space, three types of hierarchical land-cover and land use tree structure, carbon density in the landscape). Satellite data could be accepted as legally binding proof when it comes to REDD opportunity cost estimation since several cases exist where remote sensing has been used as legal evidence in ICJ (International Court of Justice) and UN resolution. It doesn't seem very difficult to comply with MRV requirements for UN-REDD opportunity cost calculation due to the probative value of satellite data. It is anticipated that this research output could be used as a valuable reference for Korea-based enterprises exploring REDD project sites and the carbon traders to ensure MRV potentials using satellite image in UN-REDD Opportunity Cost estimation.