• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.13-17
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• 2009

고해상도 위성영상의 분류 기술은 최근 가장 활발히 연구되고 있는 분야 중 하나로 텍스쳐(texture), NDVI, PCA 영상 등 다양한 전처리 정보들을 추출하고 이를 멀티스펙트럴 밴드와 조합하여 분류 정확도를 높이는 기술을 개발하는 연구들이 주를 이루고 있다. 고해상도 위성영상에서 건물의 그림자와 옆벽면의 폐색 지역은 개체 추출 및 분류를 방해하는 주된 요인이 되며, 다양한 형태와 분광특성을 갖는 개개의 건물은 자동 분류 과정을 통해 제대로 식별되지 않는다는 한계를 갖는다. 이에 본 연구에서는 KOMPSAT-2 단영상으로부터 효율적으로 건물 정보 및 토지피복을 분류하기 위하여, 추출된 건물 정보를 바탕으로 건물의 그림자와 폐색지역을 보정한 후 비건물 지역에 대한 분류를 수행하여 분류 정확도를 높이고자 하였다. 우선 삼각벡터구조 기반의 반자동 인터페이스를 이용하여 건물의 3차원 모델 및 그림자 영역을 추출하고 이로부터 추출된 그림자 영역을 효과적으로 보정하기 위해 반복 선형회귀 연산을 이용한 그림자 보정을 수행한 후 inpainting 기법을 건물 폐색영역 복원에 적용하여 영상의 품질을 향상시켰다. 이러한 과정을 통해 도심 지역의 영상 분석에 있어 가장 큰 오차를 일으키는 인공물의 그림자와 폐색에 의한 오차를 최소화한 후 분류에 적용하여 이를 보정 전 영상을 이용한 분류 결과와 비교하였다.

• Journal of the Korean Regional Science Association
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• v.38 no.2
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• pp.43-57
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• 2022

The Land Suitability Assessment is mandatory by National Land Planning and Utilization Act and the results are considered in the establishment of urban master plan and urban management plan. The study aims to examine whether the application of Land Suitability Assessment in developed urban areas is appropriate. A simulation analysis based on the Seoul's data of environmental ecological, physical, and spatial characteristics was conducted on urban green, the only applicable land for the assessment in Seoul. The results of the assessment shows that all pieces of lands in urban green is suitable for 'development'. This conflicts to the purpose of land use of urban green which needs to be conserved to protect the natural environment and landscape, animals and plants, environmental pollution, and urban sprawl. In the analysis applying optional indicators such as the distance from the area of Biotope Class 1 to prevent this conflict, the results shows little difference. This supports the necessity to review this regulation including an option to exclude developed urban areas such as Seoul in the assessment.

• Korean Architects
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• no.8 s.197
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• pp.100-100
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• 1985

전국 도시 토지개발사업을 시행함에 있어 환경 및 수질 오염으로 인한 주거환경의 악화를 방지하고 쾌적한 신 시가지를 조성하기 위하여 생황오수는 일반하수관 외의 별도 오수관을 시설하여 하수종말처리를 할 수 있도록 그 시설방법을 소개하니 건축물의 허가와 준공시 이를 확인하여 시설물이 제기능을 다 할수 있도록 하여 주시기 바랍니다.

• Journal of Environmental Impact Assessment
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• v.24 no.1
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• pp.35-50
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• 2015

The purposes of this study are to apply species distribution modeling in urban management planning for habitat conservation in non-urban area and to provide a detailed classification method for management zone. To achieve these objectives, Species Distribution Model was used to generate species richness and then to compare with the results from land suitability assessment. 59 species distribution models were developed by Maxent. This study used 15 model variables (5 topographical variables, 4 vegetation variables, and 6 distance variables) for Maxent models. Then species richness was created by sum of predicted species distributions. Land suitability assessment was conducted with criteria from type I of "Guidelines for land suitability assessment". After acquiring evaluation values from species richness and land suitability assessment, the results from these two models were compared according to the five grades of classification. The areas with the identical grade in Species richness and land suitability assessment are categorized and then compared each other. The comparison results are Grade1 10.92%, Grade2 37.10%, Grade3 34.56%, Grade4 20.89% and Grade5 1.73%. Grade1 and Grade5 showed the lowest agreement rate. Namely, development or conservation grade showed high disagreement between two assessment system. Therefore, the areas located between urban, agriculture, forest, and reserve have a tendency to change easily by development plans. Even though management areas are not the core area of reserve, it is important to provide a venue for species habitat and eco-corridor to protect and improve biodiversity in terms of landscape ecology. Consequently, adoption of species richness in three levels of management area classification such as conservation, production, planning should be considered in urban management plan.

• Land and Housing Review
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• v.14 no.1
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• pp.83-97
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• 2023

The study aims to provide an academic basis for the preservation and restoration of abandoned paddy wetland and the enhancement of its carbon accumulation function. First, the temporal change of the wetlands was analysed, and a typological classification system for wetlands was attempted with the goal of carbon reduction. The types of wetland were classified based on three variables: hydrological environment, vegetation, and carbon accumulation, with a special attention on the function of carbon accumulation. The types of abandoned paddy wetlands were classified into 12 categories based on hydrologic variables- either high or low levels of water inflow potential-, vegetation variables with either dominance of aquatic plants or terrestrial plants, and three carbon accumulation variables including organic matter production, soil organic carbon accumulation, and decomposition. It was found that the development period of abandoned paddy analyzed with aerial photographs provided by the National Geographic Information Institute happened between 2010 and 2015. In the case of the wetland in Daejeon 1 (DJMN01) farming stopped by 1990 and it appeared to be a similar structure to natural wetlands after 2010 . Over the past 40 years the abandoned paddy wetland changed to a high proportion of forests and agricultural lands. As time went by, such forests and agricultural lands tended to decrease rapidly and the lands were covered by artificial grass and other types of forests.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.3 s.33
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• pp.33-40
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• 2005

The aim of remotely sensed data classification is to produce the best accuracy map of the earth surface assigning each pixel to its appropriate category of the real-world. The classification of satellite multi-spectral image data has become tool for generating ground cover map. Many classification methods exist. In this study, MLC(Maximum Likelihood Classification), ANN(Artificial neural network), SVM(Support Vector Machine), Naive Bayes classifier algorithms are compared using IKONOS image of the part of Dalsung Gun, Daegu area. Two preprocessing methods are performed-PCA(Principal component analysis), ICA(Independent Component Analysis). Boosting algorithms also performed. By the combination of appropriate feature selection pre-processing and classifier, the best results were obtained.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.3
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• pp.33-39
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• 2007

Hyperspectral remote sensing data contain plenty of information about objects, which makes object classification more precise. In this paper, we proposed a new spectral similarity measure, called Spectral Mutual Information (SMI) for hyperspectral image classification problem. It is derived from the concept of mutual information arising in information theory and can be used to measure the statistical dependency between spectra. SMI views each pixel spectrum as a random variable and classifies image by measuring the similarity between two spectra form analogy mutual information. The proposed SMI was tested to evaluate its effectiveness. The evaluation was done by comparing the results of preexisting classification method (SAM, SSV). The evaluation results showed the proposed approach has a good potential in the classification of hyperspectral images.

• Spatial Information Research
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• v.16 no.3
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• pp.373-383
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• 2008

LiDAR, unlike satellite imagery and aerial photographs, which provides irregularly distributed three-dimensional coordinates of ground surface, enables three-dimensional modeling. In this study, urban area was classified based on 3D information collected by LiDAR. Morphological and spatial properties are determined by the ratio of ground and non-ground point that are estimated with the number of ground reflected point data of LiDAR raw data. With this information, the residential and forest area could be classified in terms of height and density of trees. The intensity of the signal is distinguished by a statistical method, Jenk's Natural Break. Vegetative area (high or low density) and non-vegetative area (high or low density) are classified with reflective ratio of ground surface.

• Korean Journal of Remote Sensing
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• v.21 no.2
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• pp.145-162
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• 2005

To improve the accuracy of land-cover discrimination in SAB data classification, this paper presents a methodology that includes feature extraction and fusion steps with multi-temporal SAR data. Three features including average backscattering coefficient, temporal variability and coherence are extracted from multi-temporal SAR data by considering the temporal behaviors of backscattering characteristics of SAR sensors. Dempster-Shafer theory of evidence(D-S theory) and fuzzy logic are applied to effectively integrate those features. Especially, a feature-driven heuristic approach to mass function assignment in D-S theory is applied and various fuzzy combination operators are tested in fuzzy logic fusion. As experimental results on a multi-temporal Radarsat-1 data set, the features considered in this paper could provide complementary information and thus effectively discriminated water, paddy and urban areas. However, it was difficult to discriminate forest and dry fields. From an information fusion methodological point of view, the D-S theory and fuzzy combination operators except the fuzzy Max and Algebraic Sum operators showed similar land-cover accuracy statistics.

• Korean Journal of Remote Sensing
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• v.33 no.6_2
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• pp.1061-1073
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• 2017

In this study, images were classified using convolutional neural network (CNN) - a deep learning technique - to investigate the feasibility of information production through a combination of artificial intelligence and spatial data. CNN determines kernel attributes based on a classification criterion and extracts information from feature maps to classify each pixel. In this study, a CNN network was constructed to classify materials with similar spectral characteristics and attribute information; this is difficult to achieve by conventional image processing techniques. A Compact Airborne Spectrographic Imager(CASI) and an Airborne Imaging Spectrometer for Application (AISA) were used on the following three study sites to test this method: Site 1, Site 2, and Site 3. Site 1 and Site 2 were agricultural lands covered in various crops,such as potato, onion, and rice. Site 3 included different buildings,such as single and joint residential facilities. Results indicated that the classification of crop species at Site 1 and Site 2 using this method yielded accuracies of 96% and 99%, respectively. At Site 3, the designation of buildings according to their purpose yielded an accuracy of 96%. Using a combination of existing land cover maps and spatial data, we propose a thematic environmental map that provides seasonal crop types and facilitates the creation of a land cover map.