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Crops Classification Using Imagery of Unmanned Aerial Vehicle (UAV)

무인비행기 (UAV) 영상을 이용한 농작물 분류

Park, Jin Ki;Park, Jong Hwa
박진기;박종화

  • Received : 2015.07.15
  • Accepted : 2015.11.05
  • Published : 2015.11.30

Abstract

The Unmanned Aerial Vehicles (UAVs) have several advantages over conventional RS techniques. They can acquire high-resolution images quickly and repeatedly. And with a comparatively lower flight altitude i.e. 80~400 m, they can obtain good quality images even in cloudy weather. Therefore, they are ideal for acquiring spatial data in cases of small agricultural field with mixed crop, abundant in South Korea. This paper discuss the use of low cost UAV based remote sensing for classifying crops. The study area, Gochang is produced by several crops such as red pepper, radish, Chinese cabbage, rubus coreanus, welsh onion, bean in South Korea. This study acquired images using fixed wing UAV on September 23, 2014. An object-based technique is used for classification of crops. The results showed that scale 250, shape 0.1, color 0.9, compactness 0.5 and smoothness 0.5 were the optimum parameter values in image segmentation. As a result, the kappa coefficient was 0.82 and the overall accuracy of classification was 85.0 %. The result of the present study validate our attempts for crop classification using high resolution UAV image as well as established the possibility of using such remote sensing techniques widely to resolve the difficulty of remote sensing data acquisition in agricultural sector.

Keywords

UAV;crop classification;object-based;remote sensing;image segmentation

References

  1. Blaschke T., 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1): 2-16. https://doi.org/10.1016/j.isprsjprs.2009.06.004
  2. Booth, D. T. and P. T. Tueller, 2003. Rangeland monitoring using remote sensing. Arid Land Research and Management, 17: 455-467. https://doi.org/10.1080/713936105
  3. Booth, D. T., S. E. Cox, T. Meikle and H. R. Zuuring. 2008. Ground-cover measurements: assessing correlation among aerial and ground-based methods. Environmental Management, 42(6): 1091-1100. https://doi.org/10.1007/s00267-008-9110-x
  4. Boschetti, L., S. P. Flasse and P. A. Brivio, 2004. Analysis of the conflict between omission and commission in low spatial resolution dichotomic thematic products: the Pareto boundary. Remote Sensing of Environment, 91(3-4): 280-292. https://doi.org/10.1016/j.rse.2004.02.015
  5. De Jong, S. M., T. Hornstra and H. G. Maas, 2001. An integrated spatial and spectral approach to the classification of Mediterranean land cover types: the SSC method. International Journal of Applied Earth Observation and Geoinformation, 3(2): 176-183. https://doi.org/10.1016/S0303-2434(01)85009-1
  6. Dingle R. L. and D. J. King, 2011. Comparison of pixel- and object-based classification in land cover change mapping. International Journal of Remote Sensing, 32(6): 1505-1529. https://doi.org/10.1080/01431160903571791
  7. Freeman, P. K. and R. S. Freeland, 2014. Politics & technology: US polices restricting unmanned aerial systems in agriculture. Food Policy, 49: 302-311. https://doi.org/10.1016/j.foodpol.2014.09.008
  8. Goktogan A. H., S. Sukkarieh, M. Bryson, J. Randle and T. Lupton, 2010. A Rotary-wing unmanned air vehicle for aquatic weed surveillance and management. Journal of Intelligent and Robotic Systems, 57: 467-480. https://doi.org/10.1007/s10846-009-9371-5
  9. Hunt, E. R., J. H. Everitt, J. C. Ritchie, M. S. Moran, D. T. Booth, G. L. Anderson, P. E. Clark and M. S. Seyfried, 2003. Applications and research using remote sensing for rangeland management. Photogrammetric Engineering & Remote Sensing, 69(6): 675-693. https://doi.org/10.14358/PERS.69.6.675
  10. Jo, M. H., S. J. Kim, D. Y. Kim and K. S. Choi, 2012. Comparative analysis of classification accuracy for calculating cropland areas by using satellite images. Journal of the Korean Society of Agricultural Engineers, 54(2): 47-53 (in Korean).
  11. Kim, B. S., 2015. Crop classification using unmanned aerial vehicle imagery and object-based classification method. The master's thesis, Chungbuk National University (in Korean).
  12. 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). https://doi.org/10.11108/kagis.2012.15.4.026
  13. Laliberte, A. S., A. Rango, J. E. Herrick, E. L. Fredrickson and L. Burkett, 2007. An object-based image analysis approach for determining fractional cover of senescent and green vegetation with digital plot photography. Journal of Arid Environments, 69(1): 1-14. https://doi.org/10.1016/j.jaridenv.2006.08.016
  14. Laliberte, A. S., J. E. Herrick, A. Rango and W. Craig, 2010. Acquisition, orthorectification, and classification of unmanned aerial vehicle (UAV) imagery for rangeland monitoring. Photogrammetric Engineering & Remote Sensing, 76: 661-672. https://doi.org/10.14358/PERS.76.6.661
  15. Lee, S. H., Y. G. Oh, N. Y. Park, S. H. Lee and J. Y. Choi, 2014. Extraction of paddy field in Jaeryeong, north Korea by objectoriented classification with RapidEye NDVI imagery. Journal of the Korean Society of Agricultural Engineers, 56(3): 55-64 (in Korean).
  16. Lee, J. W., G. A. Park, H. K. Joh, K. H. Lee, S. I. Na, J. H. Park and S. J. Kim, 2011. Analysis of relationship between vegetation indices and crop yield using KOMPSAT (KOrea Multi-Purpose SATellite)-2 imagery and field investigation data. Journal of the Korean Society of Agricultural Engineers, 53(3): 75-82 (in Korean).
  17. Mathieu, R., J. Aryal and A. K. Chong, 2007. Object-based classification of Ikonos imagery for mapping large-scale vegetation communities in urban areas. Sensors, 7: 2860-2880. https://doi.org/10.3390/s7112860
  18. Muint, 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. https://doi.org/10.1016/j.rse.2010.12.017
  19. 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). https://doi.org/10.11108/kagis.2014.17.2.121
  20. Na, S. I., J. W. Park and J. K. Park, 2012. Development of Korean paddy rice yield prediction model (KRPM) using meteorological element and MODIS NDVI. Journal of the Korean Society of Agricultural Engineers, 54(3): 141-148 (in Korean).
  21. Nam W. H., T. Tadesse, B. D. Wardlow, M. W. Jang and S. Y. Hong, 2015. Satellite-based hybrid drought assessment using vegetation drought response index in South Korea (VegDRISKorea), Journal of the Korean Society of Agricultural Engineers, 57(4): 1-9 (in Korean).
  22. Park, J. K. and J. H. Park, 2015. Reservoir failure monitoring and identified by the UAV aerial images. Journal of safety and crisis management, 11(4): 157-169 (in Korean).
  23. Teal Group, 2012. Worldwide UAS market will total $89 billion in 10 years. Http://tinyurl.com/l9hav8g. Accessed 8 Mar. 2015.
  24. Toevs, G. R., J. W. Karl, J. J. Taylor, G. S. Spurrier, M. Karl, M. R. Bobo and J. E. Herrick, 2011. Consistent indicators and methods and a scalable sample design to meet assessment, inventory, and monitoring information needs across scales. Rangelands, 33: 14-20.
  25. Yan, G., J. F. Mas, B. H. P. Maathuis, Z. Xiangmin and P. M. van Dijk, 2006, Comparison of pixel-based and object-oriented image classification approaches-a case study in a coal fire area, Wuda, Inner Mongolia, China. International Journal of Remote Sensing, 27(18): 4039-4055. https://doi.org/10.1080/01431160600702632
  26. Yu, Q., P. Gong, N. Clinton, G. Biging, M. Kelly and D. Schirokauer, 2006. Object based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering and Remote Sensing, 72(7): 799-811. https://doi.org/10.14358/PERS.72.7.799
  27. Zhang, C. and J. Kovacs, 2012. The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture, 13(6): 693-712. https://doi.org/10.1007/s11119-012-9274-5

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Acknowledgement

Supported by : 한국연구재단