• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2006.05a
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• pp.199-204
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• 2006

도시의 변화에 대하여 보다 체계적으로 계획하고 관리하기 위해서는 도시지역에 대한 정확한 지리 정보의 획득이 필요하며 이와 더불어 정보의 신속한 갱신이 필요하다. 도시 변화를 판단하기 위한 지리정보는 여러 가지 정책과 연구에 사용될 수 있을 뿐만 아니라 그 자체만으로도 도시의 성장을 기록하는 중요한 자료로 이용될 수 있다. 지리 정보의 획득 방법 중 하나인 영상분류 방법은 여러 가지가 있으나, 그 중 건물, 도로, 수목, 논, 밭 등 지상의 물체들의 분광특성을 이용한 방법이 가장 효율적이라고 할 수 있다. 따라서 본 연구에서는 도심지의 토지피복분류 현황을 기존의 방법보다 더욱 정확히 분석하기 위해서 IKONOS 영상을 이용하여 분석방법에 따른 정확도를 비교 분석하고 GIS를 이용하여 토지피목 현황을 분류기법별로 나타내며, 대상지역의 분류 정확도와 정보를 제시하였다. 연구 결과 도심지에서는 최대우도법을 이용한 감독 분류의 정확도가 가장 높은 정확도를 나타내었으며, 주관성을 배제한 분류 방법에는 신경망을 이용한 분류 방법이 높은 정확도를 나타내었다. 또한 분류 기법 별로 분류된 토지피복도를 이용하여 분류 정확도와 분류항목에 대한 속성 자료를 GIS데이터베이스로 구축하여 사용자가 요구하는 정확도에 따라 분류 방법별 토지피복도를 제공함으로써 보다 신뢰성 있고 다양한 정보를 제공할 수 있었다.

• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.105-118
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• 2016

The purpose of this study is to improve the classification system of sub-divided land cover map among the land cover maps provided by the Ministry of Environment. To accomplish the purpose, first, the overseas country land cover map classification items were examined in priority. Second, the area ratio of each item established by applying the previous sub-divided classification system was analyzed. Third, the survey on the improvement of classification system targeting the users (experts and general public) who actually used the sub-divided land cover map was carried out. Fourth, a new classification system which improved the previous system by reclassifying 41 classification items into 33 items was finally established. Fifth, the established land cover classification items were applied on study area, and the land cover classification result according to the improvement method was compared with the previous classification system. Ilsan area in Goyang city where there are diverse geographic features with various land surface characteristics such as the urbanization area and agricultural land were distributed evenly were selected as the study area. The basic images used in this study were 0.25 m aerial ortho-photographs captured by the National Geographic Information Institute (NGII), and digital topographic map, detailed stock map plan, land registration map and administrative area map were used as the relevant reference data. As a result of applying the improved classification system into the study area, the area of culture-sports, leisure facilities was $1.84km^2$ which was approximately more than twice larger in comparison to the previous classification system. Other areas such as transportation and communication system and educational administration facilities were not classified. The result of this study has meaningful significance that it reflects the efficiency for the establishment and renewal of sub-divided land cover map in the future and actual users' needs.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.1
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• pp.139-145
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• 2006

The advantage of the lidar data is in fast acquisition and process time as well as in high accuracy and high point density. However lidar data itself is difficult to classify the earth surface because lidar data is in the form of irregularly distributed point clouds. In this study, we investigated land cover classification using both lidar data and optical image through a supervised classification method. Firstly, we generated 1m grid DSM and DEM image and then nDSM was produced by using DSM and DEM. In addition, we had made intensity image using the intensity value of lidar data. As for optical images, the red, blue, green band of CCD image are used. Moreover, a NDVI image using a red band of the CCD image and infrared band of IKONOS image is generated. The experimental results showed that land cover classification with lidar data and optical image together could reach to the accuracy of 74.0%. To improve classification accuracy, we further performed re-classification of shadow area and water body as well as forest and building area. The final classification accuracy was 81.8%.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2006.05a
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• pp.169-174
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• 2006

도시지역의 급변하는 토지이용의 패턴 및 토지피복상태 등의 도시환경의 변화를 분석하는 것은 도시계획 및 개발계획을 기획, 입안하는데 중요한 자료로 활용될 수 있을 것이다. 본 연구에서는 구미시를 대상지역으로 하는 Landsat TM과 Landsat $ETM^+$ 인공위성 영상데이터로부터 토지피복/토지이용 분류를 수행함으로써 18년간의 광역적 도시변화를 탐지하였다. 또한 도시의 발전과 지표면 온도의 상관성을 알아보기 위하여 열적외선 파장영역을 이용하여 온도를 추출하였다. 시가지 확장으로 인한 지표면 온도의 상승을 확인하고 이를 통해 토지이용/토지피복의 상관관계 분석 및 식생지수분포를 비교하였다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.5
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• pp.443-450
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• 2014

To fulfill the recent increasement in the public and private demands for various spatial data, the central and local governments started to produce those data. The low-level land cover map has been produced since 2000, yet the production of high-level land covered map has started later in 2010, and recently, a few regions was completed recently. Although many studies have been carried to improve the quality of land that covered in the map, most of them have been focused on the low-level and mid-level classifications. For that reason, the study for high-level classification is still insufficient. Therefore, in this study, we suggested the automatic extraction of land readjustment for paddy land that updated in the mid-level land mapping. At the study, the RapidEye satellite images, which consider efficient to apply in the agricultural field, were used, and the high pass filtering emphasized the outline of paddy field. Also, the binary images of the paddy outlines were generated from the Otsu thresholding. The boundary information of paddy field was extracted from the image-to-map registrations and masking of paddy land cover. Lastly, the snapped edges were linked, as well as the linear features of paddy outlines were extracted by the regional Hough line extraction. The start and end points that were close to each other were linked to complete the paddy field outlines. In fact, the boundary of readjusted paddy fields was able to be extracted efficiently. We could conclude in that this study contributed to the automatic production of a high-level land cover map for paddy fields.

• Journal of The Geomorphological Association of Korea
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• v.18 no.1
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• pp.41-56
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• 2011

This study analyzes the change in land-cover based on satellite imagery to draw up land-cover map in the future, and estimates the change in land category using statistical data of the land category. To estimate land category, this study applied the double exponentially smoothing method. The result of the land cover classification according to year using satellite imagery showed that the type with the largest increase in area of land cover change in the cities of Cheonan and Asan was artificial structure, followed by water, grass field and bare land. However forest, paddy, marsh and dry field were reduced. Further, the result of the time-series analysis of the land category was found to be similar to the result of the land cover classification using satellite imagery. Especially, the result of the estimation of the land category change using the double exponentially smoothing method showed that paddy, dry field, forest and marsh are anticipated to consistently decrease in area from 2010 to 2100, whereas artificial structure, water, bare land and grass field are anticipated to consistently increase. Such results can be utilized as basic data to estimate the change in land cover according to climate change in order to prepare climate change response strategies.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1035-1046
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• 2022

This study evaluated the applicability of multi-resolution data fusion for land-cover classification. In the applicability evaluation, a spatial time-series geostatistical deconvolution/fusion model (STGDFM) was applied as a multi-resolution data fusion model. The study area was selected as some agricultural lands in Iowa State, United States. As input data for multi-resolution data fusion, Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat satellite images were used considering the landscape of study area. Based on this, synthetic Landsat images were generated at the missing date of Landsat images by applying STGDFM. Then, land-cover classification was performed using both the acquired Landsat images and the STGDFM fusion results as input data. In particular, to evaluate the applicability of multi-resolution data fusion, two classification results using only Landsat images and using both Landsat images and fusion results were compared and evaluated. As a result, in the classification result using only Landsat images, the mixed patterns were prominent in the corn and soybean cultivation areas, which are the main land-cover type in study area. In addition, the mixed patterns between land-cover types of vegetation such as hay and grain areas and grass areas were presented to be large. On the other hand, in the classification result using both Landsat images and fusion results, these mixed patterns between land-cover types of vegetation as well as corn and soybean were greatly alleviated. Due to this, the classification accuracy was improved by about 20%p in the classification result using both Landsat images and fusion results. It was considered that the missing of the Landsat images could be compensated for by reflecting the time-series spectral information of the MODIS images in the fusion results through STGDFM. This study confirmed that multi-resolution data fusion can be effectively applied to land-cover classification.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.1
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• pp.115-127
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• 2024

This research aimed to construct models with various structures based on the Transformer module and to perform land cover classification, thereby examining the applicability of the Transformer module. For the classification of land cover, the Unet model, which has a CNN structure, was selected as the base model, and a total of four deep learning models were constructed by combining both the encoder and decoder parts with the Transformer module. During the training process of the deep learning models, the training was repeated 10 times under the same conditions to evaluate the generalization performance. The evaluation of the classification accuracy of the deep learning models showed that the Model D, which utilized the Transformer module in both the encoder and decoder structures, achieved the highest overall accuracy with an average of approximately 89.4% and a Kappa coefficient average of about 73.2%. In terms of training time, models based on CNN were the most efficient. however, the use of Transformer-based models resulted in an average improvement of 0.5% in classification accuracy based on the Kappa coefficient. It is considered necessary to refine the model by considering various variables such as adjusting hyperparameters and image patch sizes during the integration process with CNN models. A common issue identified in all models during the land cover classification process was the difficulty in detecting small-scale objects. To improve this misclassification phenomenon, it is deemed necessary to explore the use of high-resolution input data and integrate multidimensional data that includes terrain and texture information.

• 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.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.19-25
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• 2013

Recently, the high-resolution satellite images is used the land cover and status data for the natural resources or environment management very helpful. The SVM algorithm of image processing has been used in various field. However, classification accuracy by SVM algorithm can be changed by various kernel functions and parameters. In this paper, the typical kernel function of the SVM algorithm was applied to the KOMPSAT-2 image and than the result of land cover performed the accuracy analysis using the checkpoint. Also, we carried out the analysis for selected the SVM kernel function from the land cover of the target region. As a result, the polynomial kernel function is demonstrated about the highest overall accuracy of classification. And that we know that the polynomial kernel and RBF kernel function is the best kernel function about each classification category accuracy.