• Title/Summary/Keyword: 지역(地域) 분류(分類) 방법(方法)

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Crop Classification for Inaccessible Areas using Semi-Supervised Learning and Spatial Similarity - A Case Study in the Daehongdan Region, North Korea - (준감독 학습과 공간 유사성을 이용한 비접근 지역의 작물 분류 - 북한 대홍단 지역 사례 연구 -)

  • Kwak, Geun-Ho;Park, No-Wook;Lee, Kyung-Do;Choi, Ki-Young
    • Korean Journal of Remote Sensing
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    • v.33 no.5_2
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    • pp.689-698
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    • 2017
  • In this paper, a new classification method based on the combination of semi-supervised learning with spatial similarity of adjacent pixels is presented for crop classification in inaccessible areas. Iterative classification based on semi-supervised learning is applied to extract reliable training data from both the initial classification result with a small number of training data, and classification results of adjacent pixels are also considered to extract new training pixels with less uncertainty. To evaluate the applicability of the proposed method, a case study of the classification of field crops was carried out using multi-temporal Landsat-8 OLI acquired in the Daehongdan region, North Korea. From a case study, the misclassification of crops and forests, and isolated pixels in the initial classification result were greatly reduced by applying the proposed semi-supervised learning method. In addition, the combination of classification results of adjacent pixels for the extraction of new training data led to the great reduction of both misclassification results and isolated pixels, compared to the initial classification and traditional semi-supervised learning results. Therefore, it is expected that the proposed method would be effectively applied to classify areas in which it is difficult to collect sufficient training data.

A Study of Land-Cover Classification Technique for Merging Image Using Fuzzy C-Mean Algorithm (Fuzzy C-Mean 알고리즘을 이용한 중합 영상의 토지피복분류기법 연구)

  • 신석효;안기원;양경주
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.22 no.2
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    • pp.171-178
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    • 2004
  • The advantage of the remote sensing is extraction the information of wide area rapidly. Such advantage is the resource and environment are quick and efficient method to grasps accurately method through the land cover classification of wide area. Accordingly this study was presented more better land cover classification method through an algorithm development. We accomplished FCM(Fuzzy C-Mean) classification technique with MLC (Maximum Likelihood classification) technique to be general land cover classification method in the content of research. And evaluated the accuracy assessment of two classification method. This study is used to the high-resolution(6.6m) Electro-Optical Camera(EOC) panchromatic image of the first Korea Multi-Purpose Satellite 1(KOMPSAT-1) and the multi-spectral Moderate Resolution Imaging Spectroradiometer(MODIS) image data(36 bands).

Land Cover Classification of a Wide Area through Multi-Scene Landsat Processing (다량의 Landsat 위성영상 처리를 통한 광역 토지피복분류)

  • 박성미;임정호;사공호상
    • Korean Journal of Remote Sensing
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    • v.17 no.3
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    • pp.189-197
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    • 2001
  • Generally, remote sensing is useful to obtain the quantitative and qualitative information of a wide area. For monitoring earth resources and environment, land cover classification of remotely sensed data are needed over increasingly larger area. The objective this study is to propose the process for land cover classification method over a wide area using multi-scene satellite data. Land cover of Korean peninsula was extracted from a Landsat TM and ETM+ mosaic created from 23 scenes at 100-meter resolution. Well-known techniques that used to general image processing and classification are applied to this wide area classification. It is expected that these process is very useful to promptly and efficiently grasp of small scale spatial information such as national territorial information.

Habitat change monitoring using high-spatial satellite image around the topical coastal area (고해상도 위성영상을 이용한 열대해역 생태분포 변화 모니터링)

  • Min, Jee-Eun;Ryu, Joo-Hyung;Kim, Key-Lim;Park, Heung-Sik
    • Proceedings of the KSRS Conference
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    • 2009.03a
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    • pp.26-30
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    • 2009
  • 본 연구는 고해상도 위성영상을 이용하여 열대해역에서의 생태환경 분포도를 작성함으로써 생태 환경의 변화를 효과적으로 모니터링 할 수 있도록 하는 데에 목적이 있다. 지구온난화 현상에 따라 산호 면적이 감소하고 있다. 이처럼 산호는 환경 변화가 민감하게 반응을 하기 때문에 열대해역에서 산호를 모니터링 하는 것은 주변 생태환경 변화 전체에 대한 관리 역할을 하기 때문에 중요하다. 본 연구에서는 이러한 열대해역의 환경을 효과적으로 모니터링 하기위하여 고해상도 위성영상인 IKONOS와 Kompsat-2 영상을 이용하여 생태환경 분포도를 작성하여보았다. 연구지역은 한남태평양연구센터가 위치한 마이크로네시아 연방국의 Weno 섬 북동쪽 연안이고, 이 지역에서 2007년과 2008년 2번의 현장관측을 실시하여 총 121개 정점에서 광관측 및 환경 자료를 얻었다. 기존의 감독분류와 무감독분류 방법, 그리고 객체지향 영상분류 방법 등을 이용하여 분포도를 작성하였고, 현장관측 자료를 이용하여 검증하였다. 고해상도 영상이기 때문에 기존 방법에서 나타나는 오분류 현상이 객차지향 영상분류 방법을 사용할 경우 적어지는 결과를 얻을 수 있었다.

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Systematic Classification of Container Ports in European Union Countries (유럽지역 컨테이너항만의 체계적 분류에 관한 연구)

  • Yeo, Gi-Tae
    • Journal of the Korean association of regional geographers
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    • v.12 no.3
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    • pp.382-391
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    • 2006
  • The aim of this research is to classify the 21 container ports in European Union countries using components of competition and co-operation under the well-known methodology, FCM(Fuzzy C-Mean). Through this approach, those 21 ports were classified into six poet groups, and also membership degree of each port within the six port groups were suggested. As results, Rotterdam which positioned Group C, is turned out the most competitive independent port. The next competitive group is found out as Group B which consisted of port of Hamburg and Antwerp. In another point of view, Group A and B which have six and four ports respectively, were needed to search the co-operation strategies. Finally, the lowest competitive port groups in the targeted area were shown as Group D and F.

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Biotope Type Classification based on the Vegetation Community in Built-up Area (시가화지역 식물군집 특성에 기초한 비오톱 유형분류)

  • Kim, Ji-Suk;Jung, Tae-Jun;Hong, Suk-Hwan
    • Korean Journal of Environment and Ecology
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    • v.29 no.3
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    • pp.454-461
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    • 2015
  • This study aims to classify the biotope types based on the vegetation community in built-up areas by different land use and to map the plant communities. By classifying biotopes according to a taxonomic system, the characteristics of a biological community can be well-represented. The biotope classification indexes for the target area include human behavioral factors such as land use intensity, land-use patterns and land-cover types. The type classification was divided into four hierarchic ranks starting with Biotope Class, next by Biotope Group and Biotope Type and lastly by Biotope Sub-Type. The Biotope Class was first divided into two areas: the areas improved by humans and the areas unimproved by humans. The improved areas were again divided into permeable and non-permeable regions on the Biotope Group level. In the Biotope Type level, permeable paving areas were divided into areas with wide gap pavers and those with narrow gap pavers. The differential species of each biotope type are Lindera glauca, Conyza canadensis, Mazus pumilus, Vicia tetrasperma, Crepidiastrum sonchifolium, Zoysis japonica, Potentilla supina and Festuca arundinacea. The results of this study suggest that the biotope classification methodology, using a subjective phytosociological approach, is a useful and valuable tool and the results also suggest the possibility of applying more objective and scientific methods in mapping and classifying various environments.

Multiple Classifier Fusion Method based on k-Nearest Templates (k-최근접 템플릿기반 다중 분류기 결합방법)

  • Min, Jun-Ki;Cho, Sung-Bae
    • Journal of KIISE:Computing Practices and Letters
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    • v.14 no.4
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    • pp.451-455
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    • 2008
  • In this paper, the k-nearest templates method is proposed to combine multiple classifiers effectively. First, the method decomposes training samples of each class into several subclasses based on the outputs of classifiers to represent a class as multiple models, and estimates a localized template by averaging the outputs for each subclass. The distances between a test sample and templates are then calculated. Lastly, the test sample is assigned to the class that is most frequently represented among the k most similar templates. In this paper, C-means clustering algorithm is used as the decomposition method, and k is automatically chosen according to the intra-class compactness and inter-class separation of a given data set. Since the proposed method uses multiple models per class and refers to k models rather than matches with the most similar one, it could obtain stable and high accuracy. In this paper, experiments on UCI and ELENA database showed that the proposed method performed better than conventional fusion methods.

Correlation Between the Rock Mass Classification Methods (암반분류방법간의 상관관계에 대한 고찰)

  • 선우춘;황세호;정소걸;이상규;한공창
    • Journal of the Korean Geotechnical Society
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    • v.17 no.4
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    • pp.127-134
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    • 2001
  • 본 논문에서는 국내 여러 지역에서 수행된 도로, 철도 및 기타 토목공사를 위한 설계과정에서 조사가 이루어진 현지조사와 시추코아 및 시추공을 대상으로 암반평가가 이루어진 자료들을 대상으로 암반분류방법간의 상관관계에 대해 조사하였다. 상관관계에 대한 해석은 암반분류에서 많이 사용되고 있는 RMR과 Q분류법간의 상관관계 그리고 RQD와 두 암반평가방법간의 관계에 대하여 암석성인별 분류 즉 화성암, 퇴적암 및 변성암별로 검토를 실시하였다. 전체적으로 분류방법의 상관관계는 좋게 나타나고 있다. 그리고 음파검층에 의한 탄성파 P파 속도와 RMR의 상관관계를 고찰하였는데, 이 두 요소간의 상관성은 비교적 양호하였으며 보다 신뢰성 있는 관계식을 유도하기 위한 노력이 필요하다.

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Rotation Transformation Invariant Texture Classification for Object Recognition of Surveillance Camera Image (감시 카메라 영상의 객체 인식을 위한 회전 변화에 강인한 질감 분류)

  • Kim, Won-Hee;Park, Seong-Mo;Kim, Jong-Nam
    • Proceedings of the Korea Information Processing Society Conference
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    • 2009.04a
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    • pp.171-172
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    • 2009
  • 질감 분류 기술은 패턴인식과 컴퓨터 비전 분야에서 널리 사용되는 기술로서, 최근 들어서는 감시 카메라 시스템에서의 정확한 객체 인식을 위한 회전 변화에 강인한 질감 분류 연구가 진행되고 있다. 본 논문에서는 순환 가보 웨이블렛 필터를 이용한 회전 변환에 강인한 질감 분류 방법을 제안한다. 제안하는 방법은 순환 가보 웨이블렛 필터링된 영상에서 전역 및 지역 특징 벡터를 계산하고 특징 벡터의 차이를 이용한 유사도 측정 판별식으로 질감 분류를 수행한다. Brodatz 질감 앨범을 이용한 실험에서 기존의 방법들보다 2~6% 향상된 질감 분류 비율을 확인할 수 있었다. 제안하는 방법은 질감 기반 객체 인식에 관련된 응용 분야에서 유용하게 사용될 수 있다.

Application of the Rule-Based Image Classification Method to Jeju Island (규칙기반 영상분류 방법의 제주도 지역의 적용)

  • Lee, Jin-A;Lee, Sung-Soon
    • Spatial Information Research
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    • v.21 no.1
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    • pp.63-73
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    • 2013
  • Geographic features are reflected in satellite images, which contain characteristic elements. Information on changes can be obtained through a comparison of images taken at different times. If multi-temporal images can be classified through the use of an unsupervised method, this is likely to improve the accuracy of image classification and contribute to various applications. A rule-based image classification algorithm for automatic processing without human involvement has been developed, but it must be verified that its results are not affected by imperfect elements. In this study, Landsat images of Jeju Island were used to carry out a rule-based image classification. The application results were examined for complex cases, including the presence of clouds in the images, different photographed times, and the type of target area, such as city, mountain, or field. The presence of clouds did not affect calculations, and appropriate classification rules were applied, depending on the different photographed times. The expansion of the urban areas of Jeju and the increase of facilities such as vinyl greenhouses in Seoguipo were identified. Furthermore, space information changes and accurate classifications for Jeju Island were obtained. With the goal of performing high-quality unsupervised classifications, measures to generalize and improve the methods employed were searched for. The findings of this study could be used in time-series analyses of images for various applications, including urban development and environmental change monitoring.