한국지반공학회논문집 (Journal of the Korean Geotechnical Society)

  • 제38권5호
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  • Pages.61-71
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  • 2022
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  • 1229-2427(pISSN)
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  • 2288-646X(eISSN)
한국지반공학회 (Korean Geotechnical Society)
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디지털 이미지 프로세싱 기반 토색 분석을 위한 CIELAB 색 표시계 활용 연구

Using the CIELAB Color System for Soil Color Identification Based on Digital Image Processing

  • 백성하 (한국건설기술연구원 지반연구본부) ;
  • 박가현 (한국건설기술연구원 지반연구본부) ;
  • 전준서 (한국건설기술연구원 지반연구본부) ;
  • 곽태영 (한국건설기술연구원 지반연구본부)
  • Baek, Sung-Ha (Korea Institute of Civil Engr. and Building Tech.) ;
  • Park, Ka-Hyun (Korea Institute of Civil Engr. and Building Tech.) ;
  • Jeon, Jun-Seo (Korea Institute of Civil Engr. and Building Tech.) ;
  • Kwak, Tae-Young (Korea Institute of Civil Engr. and Building Tech.)
  • 투고 : 2022.05.04
  • 심사 : 2022.05.10
  • 발행 : 2022.05.31
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초록

토색은 흙을 분류하고 물리적, 화학적, 생물학적 특성을 예측하기 위한 기초 지표로 널리 활용된다. 일반적으로 토색은 육안으로 관찰해 결정하지만 관찰자의 예민도 혹은 주관이 개입될 가능성이 높으며 많은 시간이 소요된다. 디지털 이미지 프로세싱은 디지털 이미지를 이용해 원하는 정보를 획득하는 일련의 과정으로, 이를 통해 빠르고 정확한(수치적인 혹은 통계적인) 토색 분석이 가능할 것으로 기대된다. 본 연구는 현장의 불규칙한 광조건을 고려할 수 있는 디지털 이미지 프로세싱 기반 토색 분석 기술 개발을 위한 기초단계로서 수행되었다. 자연광의 특성(조도 및 색온도)을 모사할 수 있는 디지털 이미지 촬영 스튜디오를 구축하고, 두 가지 흙 시료(주문진 표준사 및 안성 풍화토)를 대상으로 광조건을 12회 씩 바꿔가며 디지털 이미지를 촬영했다. 디지털 이미지 프로세싱을 통해 촬영된 시료의 토색을 두 가지 색 표시계(RGB, CIELAB)에 대해 추출한 결과, CIELAB 색 표시계를 활용해 현장의 불규칙한 광조건을 고려할 수 있음을 확인했다.

Soil color is used to determine soil classification and its physical, chemical, and biological properties. Visual determination is the most commonly used method for identifying soil color. However, it is subjective and, in many cases, non-repeatable. Digital image processing obtains useful information from digital images, accelerates soil classification, and enables the rapid identification of soil types in a field. This study develops a digital image processing-based soil color analysis technology that can consider irregular light conditions in the field. The digital image studio was designed to simulate the characteristics of natural light (illuminance and color temperature). Also, digital images of two soil samples (Jumoonjin sand and Anseong weathered soil) were captured under 12 different light conditions. For the RGB and CIELAB color systems, soil color intensities of 24 images were obtained using digital image processing. CIELAB was suitable for dealing with irregular light conditions in the field.

키워드

과제정보

본 연구는 과학기술정보통신부 한국건설기술연구원 연구운영비지원(주요사업)사업으로 수행되었습니다(과제번호 20220173-001, (22주요-대1-목적)지반분야 재난재해 대응과 미래 건설산업 신성장을 위한 지반 기술 연구(2/2)).

참고문헌

  1. ASTM (2008), Standard Practice for Specifying Color by the Munsell System. ASTM International D 1535-08, PA, USA.
  2. Aydemir, S., Keskin, S., and Drees, L.R. (2004), Quantification of Soil Features Using Digital Image Processing (DIP) Techniques, Geoderma, 119, pp.1-8. https://doi.org/10.1016/S0016-7061(03)00218-0
  3. Berns, R. (2000), Billmeyer and Saltzman's Principles of Color Technology, Wiley Interscience.
  4. Billmeyer, F.W. and Saltzman, M. (1981), Principles of Color Technology, Second Edition Wiley, New York.
  5. Commission Internationale de l'Eclairage (CIE) (1931), CIE Proceedings, Cambridge University Press, Cambridge, UK.
  6. Commission Internationale de l'Eclairage (CIE) (1978), Recommendations on Uniform Color Spaces, Color Differences, and Psychometric Color Terms. Calorimetry CIE, Paris Suppl. no. 2 to Publication no. 15.
  7. Gomez-Robledo, L., Lopez-Ruiz, N., Melgosa, M., and Palma, A.J. (2013), Using the Mobile Phone as Munsell Soil-colour Sensor: An Experiment under Controlled Illumination Conditions, Comput. Electron. Agric., Vol.99, No.7, pp.200-208. https://doi.org/10.1016/j.compag.2013.10.002
  8. Pengcheng Han, Daming Dong, Xiande Zhao, Leizi Jiao, and Yun Lang (2016), A Smartphone-based Soil Color Sensor: For Soil Type Classification, 1. Comput. Electron. Agric., 123, pp.232-241. https://doi.org/10.1016/j.compag.2016.02.024
  9. Iba Ez-Asensio, S., Marques-Mateu, A., Moreno-Ramon, H., and Balasch, S. (2013), Statistical Relationships between Soil Colour and Soil Attributes in Semiarid Areas, Biosyst. Eng., 116, pp. 120-129. https://doi.org/10.1016/j.biosystemseng.2013.07.013
  10. IUSS Working Group WRB (2006), World reference base for soil resources 2006. World Soil Resources Reports 103.
  11. Jeon, G.W., Oh, S.T., and Lim, J.H. (2021), Algorithm for Judging Anomalies Using Sliding Window to Reproduce the Color Temperature Cycle of Natural Light, Journal of Korea Multimedia Society, Vol.24, No.1, pp.30-39. https://doi.org/10.9717/KMMS.2020.24.1.030
  12. Kim, D.K. (2020), Prediction on Physical Properties of Soil Based on Deep Learning using Digital Image Processing, Doctoral Thesis, Seoul National University.
  13. Kirillova, N.P., Vodyanitskii, Yu.N., and Sileva, T.M. (2014), Conversion of Soil Color Parameters from the Munsell System to the CIE-L.a.b. System, Eurasian Soil Sci., Vol.48 No.5, pp.468-475. https://doi.org/10.1134/S1064229315050026
  14. Lusby, F., Zieve, D., and Black, B. (Eds.) (2013), May 7. Color Blindness. .
  15. Moonrungsee, N., Pencharee, S., and Jakmunee, J. (2015), Colorimetric Analyzer based on Mobile Phone Camera for Determination of Available Phosphorus in Soil, Talanta, 136, pp.204-209. https://doi.org/10.1016/j.talanta.2015.01.024
  16. Mouazen, A.M., Karoui, R., Deckers, J., De Baerdemaeker, J., and Ramon, H. (2007), Potential of Visible and Near-infrared Spectroscopy to Derive Colour Groups Utilising the Munsell Soil Colour Charts, Biosyst. Eng., 97, pp.131-143. https://doi.org/10.1016/j.biosystemseng.2007.03.023
  17. Munsell Color Company (1975), Munsell Soil Color Charts. Macbeth Division of Kollmorgen, Baltimore, MD.
  18. O'Donnell, T.K., Goyne, K.W., Miles, R.J., Baffaut, C., Anderson, S.H., and Sudduth, K.A. (2011), Determination of Representative Elementary Areas for Soil Redoximorphic Features Identified by Digital Image Processing, Geoderma, 161, pp.138-146. https://doi.org/10.1016/j.geoderma.2010.12.011
  19. Park, J.S. (2017), Soil Classification and Characterization using Unmanned Aerial Vehicle and Digital Image Processing, Doctoral Thesis, Seoul National University.
  20. Persson, M. (2005), Estimating Surface Soil Moisture from Soil Color Using Image Analysis, Vadose Zone Journal, Vol.4, No.4, pp.1119-1122. https://doi.org/10.2136/vzj2005.0023
  21. Santos, J. F., Silva, H. R., Pinto, F. A., and Assis, I. R. d. (2016), Use of Digital Images to Estimate Soil Moisture, Revista Brasileira de Engenharia Agricola e Ambiental, Vol.20, No.12, pp.1051-1056. https://doi.org/10.1590/1807-1929/agriambi.v20n12p1051-1056
  22. Soil Survey Staff (1999), Soil Taxonomy: A Basic System of Soil Classification for Making and Interpreting Soil Surveys, 2nd ed.
  23. Thompson, J., Pollio, A., and Turk, P. (2013), Comparison of Munsell Soil Color Charts and the GLOBE Soil Color Book, Soil Sci. Soc. Am. 77 (March 25), pp.2089-2093 https://doi.org/10.2136/sssaj2013.03.0117n
  24. Torrent, J. and Barron, V. (1993), Laboratory Measurement of Soil Color: Theory and Practice. In: Bigham, J.M., Ciolkosz, E.J. (Eds.), Soil Color, Special Publication No. 31, SSSA, Madison, WI, pp. 21-33.
  25. Viscarra Rossel, R.A., Fouad, Y., and Walter, C. (2008), Using a Digital Camera to Measure Soil Organic Carbon and Iron Contents, Biosyst. Eng., 100, pp. 149-159. https://doi.org/10.1016/j.biosystemseng.2008.02.007
  26. Viscarra Rossel, R.A., Minasny, B., Roudier, P., and McBratney, A.B. (2006), Colour Space Models for Soil Science, Geoderma, 133, pp.320-337. https://doi.org/10.1016/j.geoderma.2005.07.017
  27. Webster, R. and Butler, B.E. (1976), Soil Classification and Survey Studies at Ginninderra, Australian Journal of Soil Research, 14, pp.1-24. https://doi.org/10.1071/SR9760001
  28. Wyszecki, G. and Stiles, W.S. (1982), Color Science: Concepts and Methods, Quantitative Data and Formulae, Second Edition Wiley, New York.
  29. Zanetti, S. S., Cecilio, R. A., Alves, E. G., Silva, V. H., and Sousa, E. F. (2015), Estimation of the Moisture Content of Tropical Soils Using Colour Images and Artificial Neural Networks, Catena, 135, pp.100-106. https://doi.org/10.1016/j.catena.2015.07.015
  30. Zhu, Y., Wang, Y., and Shao, M. (2010), Using Soil Surface Gray Level to Determine Surface Soil Water Content, Science China Earth Sciences, Vol.53, No.10, pp.1527-1532. https://doi.org/10.1007/s11430-010-4049-1