• Proceedings of the Korea Contents Association Conference
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• 2006.05a
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• pp.84-87
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• 2006

It was investigated what is the reasonable landcover classification system for the nonpoint pollution models. According to the parameters of the nonpoint pollution models, runoff curve number, crop management factor and Manning's roughness coefficient, the landcover classification system was proposed to manage the drainage basin of the Woopo wetland. Also, the rule-based classification method was adopted to extract the landcover information for this study area.

• Aerospace Engineering and Technology
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• v.8 no.1
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• pp.132-137
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• 2009

This study has regard to classification by using multi-temporal SAR data. Multi-temporal JERS-1 SAR images are used for extract the land cover information and possibility. So far, land cover information extracted by high resolution aerial photo, satellite images, and field survey. This study developed on multi-temporal land cover status monitoring and coherence information mapping can be processing by L band SAR image. From July, 1997 to October, 1998 JERS SAR images (9 scenes) coherence values are analyzed and then extracted land cover information factors, so on. This technique which forms the basis of what is called SAR Interferometry or InSAR for short has also been employed in spaceborne systems. In such systems the separation of the antennas, called the baseline is obtained by utilizing a single antenna in a repeat pass.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.1
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• pp.15-22
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• 2015

To create a wind resource map, we need a contour map, a roughness map and wind data. We need a land cover map for the roughness map of these data. A land cover map represents the area showing similar characteristics after color indexing based on the scientific method. The features of land cover is classified by Remote sensing technique. In this study, we verified the application of the NDVI technique is reasonable after we created the wind resource map using roughness maps by unsupervised classification and NDVI technique. As a result, the wind resource map using the NDVI technique showed a 60% accordance rate and difference in class less than one. From the results, The NDVI technique is found alternative to create roughness maps by the unsupervised classification.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1663-1676
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• 2022

Recently, a number of deep-learning based land cover segmentation studies have been introduced. Some studies denoted that the performance of land cover segmentation deteriorated due to insufficient training data. In this study, we verified the improvement of land cover segmentation performance through data augmentation. U-Net was implemented for the segmentation model. And 2020 satellite-derived landcover dataset was utilized for the study data. The pixel accuracies were 0.905 and 0.923 for U-Net trained by original and augmented data respectively. And the mean F1 scores of those models were 0.720 and 0.775 respectively, indicating the better performance of data augmentation. In addition, F1 scores for building, road, paddy field, upland field, forest, and unclassified area class were 0.770, 0.568, 0.433, 0.455, 0.964, and 0.830 for the U-Net trained by original data. It is verified that data augmentation is effective in that the F1 scores of every class were improved to 0.838, 0.660, 0.791, 0.530, 0.969, and 0.860 respectively. Although, we applied data augmentation without considering class balances, we find that data augmentation can mitigate biased segmentation performance caused by data imbalance problems from the comparisons between the performances of two models. It is expected that this study would help to prove the importance and effectiveness of data augmentation in various image processing fields.

• 한국지구과학회:학술대회논문집
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• 2005.09a
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• pp.79-84
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• 2005

이 논문에서는 구름과 같은 기상 상태의 제약 없이 자료 획득이 가능한 SAR 자료를 이용하여 토지 피복 특성을 구분하고자 하였다. 기존 단일 주파수, 편광 상태의 자료만을 제공하는 SAR 자료를 이용한 분류에서의 낮은 분류 정확도를 향상시키고자 이 논문에서는 다중 시기 C 밴드 자료이면서 서로 다른 편광 상태의 자료를 제공하는 Radarsat-1(HH)와 ENVISAT(VV) 자료를 분류에 이용하였다. 분류 기법으로 Random Forests를 적용한 결과, 단일 편광 상태의 자료만을 이용하였을 때에 비해서 보다 향상된 분류 정확도를 얻을 수 있었다.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.5
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• pp.77-83
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• 2008

In this study, land cover classification and NDVI evaluation for hazard mitigation precaution are carried out in surrounding areas of Yeongi-gun, Chungcheongnam-do ($132\;km^2$) where a project for multi-functional administrative city is promoted by government. Image acquired from KOMPSAT 2, LANDSAT and ASTER is utilized and comparative evaluation on limitation in classification based on resolution was carried out. The area mainly consists of arable land including mountains, rice fields, ordinary fields, etc thus special attention was paid to the classification of rice fields and ordinary fields. For the classification of image acquired from KOMPSAT 2, segmentation technique for classification of high-resolution image was applied. To evaluate the accuracy of the classification, field investigation was conducted to examine the sample and it was compared with the land usage and classification of land category in land ledger of Korea. Acquired results were made into theme map in shape file format and it would be of great help in decision making of policy for the future-oriented development plan of multi-functional administrative city.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.4
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• pp.100-118
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• 2015

This study established a land-cover classification method on objects using satellite images, and figured out distributional patterns of land cover according to categories through spatial statistics techniques. Object-based classification generated each land cover classification map by spectral information, texture information, and the combination of the two. Through assessment of accuracy, we selected optimum land cover classification map. Also, to figure out spatial distribution pattern of land cover according to categories, we analyzed hot spots and quantified them. Optimal weight for an object-based classification has been selected as the Scale 52, Shape 0.4, Color 0.6, Compactness 0.5, Smoothness 0.5. In case of using the combination of spectral information and texture information, the land cover classification map showed the best overall classification accuracy. Particularly in case of dry fields, protected cultivation, and bare lands, the accuracy has increased about 12 percent more than when we used only spectral information. Forest, paddy fields, transportation facilities, grasslands, dry fields, bare lands, buildings, water and protected cultivation in order of the higher area ratio of DMZ according to categories. Particularly, dry field sand transportation facilities in Yanggu occurred mainly in north areas of the civilian control line. dry fields in Cheorwon, forest and transportation facilities in Inje fulfilled actively in south areas of the civilian control line. In case of distributional patterns according to categories, hot spot of paddy fields, dry fields and protected cultivation, which is related to agriculture, was distributed intensively in plains of Yanggu and in basin areas of Cheorwon. Hot spot areas of bare lands, waters, buildings and roads have similar distribution patterns with hot spot areas related to agriculture, while hot spot areas of bare lands, water, buildings and roads have different distributional patterns with hot spot areas of forest and grasslands.

• Journal of Korean Society for Geospatial Information Science
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• v.4 no.2 s.8
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• pp.181-193
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• 1996

A new technique for land cover classification which applies digital image pre-classified by unsupervised classification technique, clustering, to Canonical Correlation Analysis(CCA) was proposed in this paper. Compared with maximum likelihood classification, the proposed technique had a good flexibility in selecting training areas. This implies that any selected position of training areas has few effects on classification results. Land cover of each cluster designated by CCA after clustering is able to be used as prior information for maximum likelihood classification. In case that the same training areas are used, accuracy of classification using Canonical Correlation Analysis after cluster analysis is better than that of maximum likelihood classification. Therefore, a new technique proposed in this study will be able to be put to practical use. Moreover this will play an important role in the construction of GIS database

• Ecology and Resilient Infrastructure
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• v.9 no.4
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• pp.218-227
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• 2022

This study attempted classification through deep learning-based image training for land cover classification in river spaces which is one of the important data for efficient river management. For this purpose, land cover classification analysis with the RGB image of the target section based on the category classification index of major land cover map was conducted by using the learning outcomes from the result of labeling. In addition, land cover classification of the river spaces was performed by unsupervised and supervised classification from Sentinel-2 satellite images provided in an open format, and this was compared with the results of deep learning-based image classification. As a result of the analysis, it showed more accurate prediction results compared to unsupervised classification results, and it presented significantly improved classification results in the case of high-resolution images. The result of this study showed the possibility of classifying water areas and wetlands in the river spaces, and if additional research is performed in the future, the deep learning based image train method for the land cover classification could be used for river management.

• Journal of Environmental Impact Assessment
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• v.10 no.1
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• pp.39-47
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• 2001

본 연구는 지리정보시스템 및 원격탐사기법을 응용하여 북한의 자연환경을 조사하기 위한 기초연구로서 수행되었으며, 과거 약 20년 동안의 평양 및 남포지역의 산림면적의 변화 및 경관구조 변화측면에서의 산림 파편화 양상을 조사하였다. 조사자료로는 Landsat MSS 및 TM 영상의 NDVI값을 이용하였으며, 보다 정확한 피복분류를 위하여 변형된 Cluster-Busting 알고리즘을 활용하여 산림과 비산림지역으로 단순화시켜 분석하였다. 경관구조의 변화를 살피기 위해서 조각밀도, 형태 및 핵심내부지역의 면적 등의 경관지수(Landscape Indices)를 활용하였다. 분석과정을 거쳐서 도출된 결론은 다음과 같다. 첫째, Cluster-busting 방법을 활용한 토지피복 분류결과 87.3%의 총 분류 정확도를 얻었으며, Binary Map을 이용한 변화감지(Change Detection)기법 또한 그 결과가 정확한 것으로 판단되었다. 둘째, '79년에서 '98년에 이르는 기간동안, 평양의 경우 '79년 산림면적의 15%, 그리고 남포지역의 경우 14%가 감소하였다. 셋째, 경관지수를 이용하여 북한 산림의 파편화 변화를 조사한 결과 산림조각의 개수는 늘어나고 조각의 평균면적 및 핵심내부면적은 감소하였으며 조각크기의 다양성 또한 낮아졌다. 산림조각 형태지수 또한 매우 증가하였는데 이러한 결과들은 평양 및 남포지역의 산림조각이 파편화되고 그 형태 또한 불규칙적이며 복잡하게 변화하였음을 보여주고 있다.