Journal of the Korean Association of Geographic Information Studies (한국지리정보학회지)

The Korean Association of Geographic Information Studies (한국지리정보학회)
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A Study on the Evaluation of the Different Thresholds for Detecting Urban Areas Using Remote-Sensing Index Images: A Case Study for Daegu, South Korea

원격탐사 지수 영상으로부터 도시 지역 탐지를 위한 임계점 평가에 관한 연구: 대구광역시를 사례로

  • CHOUNG, Yun-Jae (Global Landsat Satellite Information Center, Kyungpook National University) ;
  • LEE, Eung-Joon (GIS Research Center, Geo C&I Co., Ltd.) ;
  • JO, Myung-Hee (Dept, of Aero-Satellite Geo-Informatics Engineering, School of Convergence and Fusion System Engineering, College of Science and Technology, Kyungpook National University)
  • 정윤재 (경북대 국토위성정보연구소) ;
  • 이응준 ((주)지오씨엔아이 GIS 연구소) ;
  • 조명희 (경북대 융복합시스템공학부 항공위성시스템전공)
  • Received : 2019.03.15
  • Accepted : 2019.03.26
  • Published : 2019.03.31
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Abstract

Mapping urban areas using the earth observation satellites is useful for monitoring urban expansions and measuring urban developments. In this research, the different thresholds for detecting the urban areas separately from the remote-sensing index images (normalized-difference built-up index(NDBI) and urban index(UI) images) generated from the Landsat-8 image acquired in Daegu, South Korea were evaluated through the following steps: (1) the NDBI and UI images were separately generated from the given Landsat-8 image; (2) the different thresholds (-0.4, -0.2, and 0) for detecting the urban areas separately from the NDBI and UI images were evaluated; and (3) the accuracy of each detected urban area was assessed. The experiment results showed that the threshold -0.2 had the best performance for detecting the urban areas from the NDBI image, while the threshold -0.4 had the best performance for detecting the urban areas from the UI image. Some misclassification errors, however, occurred in the areas where the bare soil areas were classified into urban areas or where the high-rise apartments were classified into other areas. In the future research, a robust methodology for detecting urban areas, including the various types of urban features, with less misclassification errors will be proposed using the satellite images. In addition, research on analyzing the pattern of urban expansion will be carried out using the urban areas detected from the multi-temporal satellite images.

지구관측 위성영상을 활용한 도시지역 매핑 작업은 도시지역의 팽창 및 도시발전의 관측을 위해 매우 중요하다. 본 연구에서는 대구광역시를 촬영한 Landsat-8 위성영상의 분광밴드를 이용하여 제작한 두 원격탐사 지수 영상(정규 건축물 지수(NDBI) 영상 및 도시 지수 (UI) 영상) 으로부터 도시지역을 탐지하기 위한 임계점 평가에 관한 연구를 다음과 같이 진행하였다. 우선, Landsat-8 영상의 분광밴드를 이용하여 NDBI 영상과 UI 영상을 각각 제작한다. 그리고 다양한 임계점(-0.4, -0.2 및 0)을 NDBI 및 UI 영상에 적용하여 도시지역을 탐지하고, 탐지된 도시지역의 정확도를 산출한다. 본 연구를 통해 진행한 실험결과를 분석한 결과, NDBI 영상에서는 임계점으로 -0.2를 적용시켰을 때 탐지된 도시지역의 정확도(88%)가 가장 높았고, UI 영상에서는 임계점으로 -0.4를 적용시켰을 때 탐지된 도시지역의 정확도(88%)가 가장 높았다. 또한, 일부 지역에서는 나지가 도시지역으로 오분류 되었으며, 고층 아파트 지역이 비도시 지역으로 오분류 되었다. 추후 연구에서는 위성영상에서 오분류를 줄이고 다양한 도시지역 객체를 추출할 수 있는 개선된 방법을 제안하도록 한다. 또한 다중시기 위성영상에서 탐지된 도시지역을 이용하여 도시 팽창 패턴을 분석하는 추후 연구도 수행할 계획이다.

Keywords

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FIGURE 1. Urban area of Daegu City in South Korea, selected as the study area

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FIGURE 2. Flowchart showing the procedure for evaluating the different thresholds for detecting urban areas from the NDBI and UI images generated from the given Landsat-8 satellite image

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FIGURE 3. NDBI and UI images generated using the SWIR1, SWIR2, and NIR bands of the given Landsat-8 satellite image: (a) NDBI image; and (b) UI image

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FIGURE 4. Given Landsat-8 satellite image and the urban areas separately detected from the NDBI image by evaluating the different thresholds (-0.4, -0.2, and 0): (a) the given Landsat-8 satellite image; (b) the urban area detected from the NDBI image by evaluating the threshold -0.4; (c) the urban area detected from the NDBI image by evaluating the threshold -0.2; and (d) the urban area detected from the NDBI image by evaluating the threshold 0

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FIGURE 4. Continued

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FIGURE 5. Given Landsat-8 satellite image and the urban areas separately detected from the UI image by evaluating the different thresholds (-0.4, -0.2 and 0): (a) the given Landsat-8 satellite image; (b) the urban area detected from the UI image by evaluating the threshold –0.4; (c) the urban area detected from the UI image by evaluating the threshold –0.2; and (d) the urban area detected from the UI image by evaluating the threshold 0

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FIGURE 6. Checkpoints manually digitized based on the given Landsat-8 satellite image

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FIGURE 7. Example of the misclassification errors that occurred in the areas where the bare soil areas were classified into urban areas: (a) the given Landsat-8 image showing the bare soil areas; (b) the urban area detected from the NDBI image by evaluating the threshold –0.2; and (c) the urban area detected from the UI image by evaluating the threshold –0.4

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FIGURE 7. Continued

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FIGURE 8. Example of the misclassification errors that occurred in the areas where the high-rise apartments were classified into other areas: (a) the given Landsat-8 image showing the high-rise apartments; (b) the urban area detected from the NDBI image by evaluating the threshold –0.2; and (c) the urban area detected from the UI image by evaluating the threshold –0.4

TABLE 1. Accuracy of each urban map separately generated from the NDBI and UI images by evaluating the different thresholds (-0.4, -02, and 0): (a) Accuracy of the urban map generated from the NDBI image by evaluating the threshold –0.4; (b) Accuracy of the urban map generated from the NDBI image by evaluating the threshold –0.2; (c) Accuracy of the urban map generated from the NDBI image by evaluating the threshold 0; (d) Accuracy of the urban map generated from the UI image by evaluating the threshold –0.4; (e) Accuracy of the urban map generated from the UI image by evaluating the threshold –0.2; and (f) Accuracy of the urban map generated from the UI image by evaluating the threshold 0

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