A Comparative Study on the Possibility of Land Cover Classification of the Mosaic Images on the Korean Peninsula

한반도 모자이크 영상의 토지피복분류 활용 가능성 탐색을 위한 비교 연구

  • Moon, Jiyoon (National Satellite Operation & Application Center, Korea Aerospace Research Institute) ;
  • Lee, Kwang Jae (National Satellite Operation & Application Center, Korea Aerospace Research Institute)
  • 문지윤 (한국항공우주연구원 국가위성정보활용지원센터) ;
  • 이광재 (한국항공우주연구원 국가위성정보활용지원센터)
  • Received : 2019.10.31
  • Accepted : 2019.11.24
  • Published : 2019.12.31


The KARI(Korea Aerospace Research Institute) operates the government satellite information application consultation to cope with ever-increasing demand for satellite images in the public sector, and carries out various support projects including the generation and provision of mosaic images on the Korean Peninsula every year to enhance user convenience and promote the use of satellite images. In particular, the government has wanted to increase the utilization of mosaic images on the Korean Peninsula and seek to classify and update mosaic images so that users can use them in their businesses easily. However, it is necessary to test and verify whether the classification results of the mosaic images can be utilized in the field since the original spectral information is distorted during pan-sharpening and color balancing, and there is a limitation that only R, G, and B bands are provided. Therefore, in this study, the reliability of the classification result of the mosaic image was compared to the result of KOMPSAT-3 image. The study found that the accuracy of the classification result of KOMPSAT-3 image was between 81~86% (overall accuracy is about 85%), while the accuracy of the classification result of mosaic image was between 69~72% (overall accuracy is about 72%). This phenomenon is interpreted not only because of the distortion of the original spectral information through pan-sharpening and mosaic processes, but also because NDVI and NDWI information were extracted from KOMPSAT-3 image rather than from the mosaic image, as only three color bands(R, G, B) were provided. Although it is deemed inadequate to distribute classification results extracted from mosaic images at present, it is believed that it will be necessary to explore ways to minimize the distortion of spectral information when making mosaic images and to develop classification techniques suitable for mosaic images as well as the provision of NIR band information. In addition, it is expected that the utilization of images with limited spectral information could be increased in the future if related research continues, such as the comparative analysis of classification results by geomorphological characteristics and the development of machine learning methods for image classification by objects of interest.


Grant : 정부 위성정보활용협의체 지원

Supported by : 한국항공우주연구원


  1. Jensen, J. R., 2007. Remote sensing of the environment: an earth resource perspective, Pearson Prentice Hall, Upper Saddle River, NJ, USA.
  2. Kang, N.Y., S.Y. Go, and G.S. Cho, 2013. A comparative study on suitable SVM kernel function of land cover classification using KOMPSAT-2 imagery, Journal of the Korean Society for Geospatial Information System, 21(2): 19-25 (in Korean with English abstract).
  3. Kim, H.O. and J.M. Yeom, 2012. A study on objectbased image analysis methods for land cover classification in agricultural areas, Journal of the Korean Association of Geographic Information Studies, 15(4): 26-41 (in Korean with English abstract).
  4. Ku, C.Y., 2011. A study on managing and processing the images with different spatial resolution for the systemic land cover classification, Journal of the Korean Association of Professional Geographers, 45(3): 375-386 (in Korean with English abstract).
  5. Li, M., S. Zang, B. Zhang, S. Li, and C. Wu, 2014. A review of remote sensing image classification techniques: the role of spatio-contextual information, European Journal of Remote Sensing, 47(1): 389-411.
  6. Lu, D. and Q. Weng, 2007. A survey of image classifi - cation methods and techniques for improving classification performance, International Journal of Remote Sensing, 28(5): 823-870.
  7. Mahmon, N.A. and N. Ya'acob, 2014. A review on classification of satellite image using Artificial Neural Network (ANN), Proc. of IEEE 5th Control and System Graduate Research Colloquium, Shah Alam, Malaysia, Aug. 11-12, pp. 153-157.
  8. Moon, J.Y. and Y.S. Kim, 2017. Comparison of the estimated result of ecosystem service value using pixel-based and object-based analysis, Korean Journal of Remote Sensing, 33(6-3): 1187-1196 (in Korean with English abstract).
  9. Myint, S.W., P. Gober, A. Brazel, S. Grossman-Clarke, and Q. Weng, 2011. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery, Remote Sensing of Environment, 115(5): 1145-1161.
  10. Na, H.S. and J.S. Lee, 2014. Analysis of land cover characteristics with object-based classification method - Focusing on the DMZ in Inje-gun, Gangwon-do, Journal of the Korean Association of Geographic Information Studies, 17(2): 121-135 (in Korean with English abstract).
  11. Oh, C.Y., S.Y. Park, H.S. Kim, Y.W. Lee, and C.U. Choi, 2010. Comparison of landcover map accuracy using high resolution satellite imagery, Journal of the Korean Association of Geographic Information Studies, 13(1): 89-100 (in Korean with English abstract).