DOI: https://doi.org/10.14710/geoplanning.3.2.127-136

IMPLEMENTATION OF THE MARKOV RANDOM FIELD FOR URBAN LAND COVER CLASSIFICATION OF UAV VHIR DATA

*Jati Pratomo  -  Lokalaras Indonesia Institute, Indonesia
Triyoga Widiastomo  -  Institut Penelitian Inovasi Bumi, INOBU, Indonesia

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Abstract

The usage of Unmanned Aerial Vehicle (UAV) has grown rapidly in various fields, such as urban planning, search and rescue, and surveillance. Capturing images from UAV has many advantages compared with satellite imagery. For instance, higher spatial resolution and less impact from atmospheric variations can be obtained. However, there are difficulties in classifying urban features, due to the complexity of the urban land covers. The usage of Maximum Likelihood Classification (MLC) has limitations since it is based on the assumption of the normal distribution of pixel values, where, in fact, urban features are not normally distributed. There are advantages in using the Markov Random Field (MRF) for urban land cover classification as it assumes that neighboring pixels have a higher probability to be classified in the same class rather than a different class. This research aimed to determine the impact of the smoothness (λ) and the updating temperature (Tupd) on the accuracy result (κ) in MRF. We used a UAV VHIR sized 587 square meters, with six-centimetre resolution, taken in Bogor Regency, Indonesia. The result showed that the kappa value (κ) increases proportionally with the smoothness (λ) until it reaches the maximum (κ), then the value drops. The usage of higher (Tupd) has resulted in better (κ) although it also led to a higher Standard Deviations (SD). Using the most optimal parameter, MRF resulted in slightly higher (κ) compared with MLC.

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Keywords: Markov Random Field; UAV; VHIR; Land Cover Classification
Funding: Mr.Valentyn Tolpekin, University of Twente; Lokalaras Indonesia Institute

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Section: Original Research
Language : EN
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  1. Aarts, E., Korst, J., & Michiels, W. (2005). Simulated Annealing. In E. K. Burke & G. Kendall (Eds.), Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques (pp. 187–210). Boston, MA: Springer US. [http://doi.org/10.1007/0-387-28356-0_7">CrossRef]

  2. Ardila, J. P., et al. (2011). Markov-random-field-based super-resolution mapping for identification of urban trees in VHR images. ISPRS Journal of Photogrammetry and Remote Sensing, 66(6), 762–775. [https://doi.org/10.1016/j.isprsjprs.2011.08.002">CrossRef]

  3. Cihlar, J. (2000). Land cover mapping of large areas from satellites: status and research priorities. International Journal of Remote Sensing, 21(6–7), 1093–1114. [https://doi.org/10.1080/014311600210092">CrossRef]

  4. Colwell, R. N. (1960). Manual for Photographic Interpretation. Washington DC: The American Society of Photogrammetry.

  5. Geman, S., & Geman, D. (1984). Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-6(6), 721–741. [http://doi.org/10.1109/TPAMI.1984.4767596">CrossRef]

  6. Getzin, S., Wiegand, K., & Schöning, I. (2012). Assessing biodiversity in forests using very high-resolution images and unmanned aerial vehicles. Methods in Ecology and Evolution, 3(2), 397–404. [https://doi.org/10.1111/j.2041-210x.2011.00158.x">CrossRef]

  7. Gunal, S., & Edizkan, R. (2008). Subspace based feature selection for pattern recognition. Information Sciences, 178(19), 3716–3726. [https://doi.org/10.1016/j.ins.2008.06.001">CrossRef]

  8. Lam, J. (1988). An Efficient Simulated Schedule. Yale University.

  9. Liang, S., Fang, H., & Chen, M. (2001). Atmospheric correction of Landsat ETM+ land surface imagery. I. Methods. IEEE Transactions on Geoscience and Remote Sensing, 39(11), 2490–2498. [https://doi.org/10.1109/36.964986">CrossRef]

  10. Lin, Y., et al. (2015). Use of UAV oblique imaging for the detection of individual trees in residential environments. Urban Forestry & Urban Greening, 14(2), 404–412. [http://dx.doi.org/10.1016/j.ufug.2015.03.003">CrossRef]

  11. Lu, D., & Weng, Q. (2007). Review article A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823–870. [http://doi.org/10.1080/01431160600746456">CrossRef]

  12. Maselli, F., Conese, C., & Petkov, L. (1994). Use of probability entropy for the estimation and graphical representation of the accuracy of maximum likelihood classifications. ISPRS Journal of Photogrammetry and Remote Sensing, 49(2), 13–20. [https://doi.org/10.1016/0924-2716(94)90062-0">CrossRef]

  13. Myint, S. W., et al. (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">CrossRef]

  14. Myint, S. W., Mesev, V., & Lam, N. (2006). Urban textural analysis from remote sensor data: Lacunarity measurements based on the differential box counting method. Geographical Analysis, 38(4), 371–390. [https://doi.org/10.1111/j.1538-4632.2006.00691.x">CrossRef]

  15. Nasrullah, A. R. (2016). Systematic Analysis of Unmanned Aerial Vehicle (UAV) Derived Product Quality. Faculty of Geo-Information Science and Earth Observation of the University of Twente.

  16. Otukei, J. R., & Blaschke, T. (2010). Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12(SUPPL. 1), 27–31. [http://doi.org/10.1016/j.jag.2009.11.002">CrossRef]

  17. Perumal, K., & Bhaskaran, R. (2010). Supervised classification performance of multispectral images. arXiv Preprint arXiv:1002.4046.

  18. Rango, A., et al. (2009). Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management. Journal of Applied Remote Sensing, 3(1), 33542. [https://doi.org/10.1117/1.3216822">CrossRef]

  19. Richards, J. A. (2013). Remote Sensing Digital Image Analysis. Springer Nature.

  20. Tiwari, L. K., et al. (2015). Markov random field based method for super-resolution mapping of forest encroachment from remotely sensed ASTER image. Geocarto International, 31(4), 428–445. [http://doi.org/10.1080/10106049.2015.1054441">CrossRef]

  21. Tobler, W. R. (1970). A Computer Movie Simulation Urban Growth in Detroit Region. Economic Geography, 46, 234–240. [http://dx.doi.org/10.2307/143141">CrossRef] http://dx.doi.org/10.2307/143141"> 

  22. Tolpekin, V. A., & Stein, A. (2009). Quantification of the Effects of Land-Cover-Class Spectral Separability on the Accuracy of Markov-Random-Field-Based Superresolution Mapping. IEEE Transactions on Geoscience and Remote Sensing, 47(9), 3283–3297. [http://doi.org/10.1109/TGRS.2009.2019126">CrossRef]

  23. Wang, L., & Wang, Q. (2013). Subpixel mapping using Markov random field with multiple spectral constraints from subpixel shifted remote sensing images. IEEE Geoscience and Remote Sensing Letters, 10(3), 598–602. [https://doi.org/10.1109/lgrs.2012.2215573">CrossRef]

  24. Yan, W. Y., Shaker, A., & El-Ashmawy, N. (2015). Urban land cover classification using airborne LiDAR data: A review. Remote Sensing of Environment, 158, 295–310. [http://dx.doi.org/10.1016/j.rse.2014.11.001">CrossRef]


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