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Journal of Forests

June 2020, Volume 7, 1, pp 18-31

Color-Based Forest Cover Type Image Segmentation using K-Means Clustering Approach

Yeong Nain Chi

Yeong Nain Chi 1


  1. Department of Agriculture, Food and Resource Sciences University of Maryland Eastern Shore Princess Anne, MD, USA. 1

on Google Scholar
on PubMed

Pages: 18-31

DOI: 10.18488/journal.101.2020.71.18.31

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Article History:

Received: 25 March, 2020
Revised: 30 April, 2020
Accepted: 03 June, 2020
Published: 29 June, 2020


Abstract:

In order to understand forest composition, classifying forest cover type can help research regarding forest resilience, carbon sequestration, and climate change concerns. The purposes of this study were to develop and implement some image processing functions based on the histogram of forest cover type color image, and to classify forest cover type using its color feature sets of image pixels. Color-based image segmentation that is based on the color feature of image pixels assumes that homogeneous colors in the image correspond to separate clusters and hence meaningful objects in the image. The Image Processing Toolbox of MATLAB R2019a was used to convert the original forest cover type image to the enhance contrast image, including histogram of enhance contrast image. Furthermore, It was also used to analyze color-based forest cover type image segmentation using the enhance contrast image for this study. Using K-Means clustering analysis, a three-cluster solution was developed, labeled as Hardwoods (Yellow Color) Cover Type, Hardwoods (Gray Color) Cover Type, and Loblolly Pines Cover Type. There was a significant difference among three different forest cover type clusters in terms of histograms and L*a*b* color space features visually.
Contribution/ Originality
This study is one of very few studies which have classified forest cover type using its color feature sets of image pixels in order to understand forest composition. This study also addresses that K-Means clustering analysis can be utilized to develop and implement the classification of forest cover type image.

Keywords:

Forest cover type, Color-based, Image segmentation, Histogram, K-means clustering, MATLAB.

Reference:

[1]          Global Forest Observations Initiative (GFOI), "Integration of remote-sensing and ground-based observations for estimation of emissions and removals of greenhouse gases in forests: Methods and Guidance (Edition 2.0). Food and Agriculture Organization, Rome. Retrieved from: https://unfccc.int/files/land_use_and_climate_change/redd/submissions/application/pdf/redd_20140218_mgd_report_gfoi.pdf," 2016.

[2]          MathWorks, MATLAB (R2019b) Image processing toolbox™ user's guide. Natick, MA: The MathWorks, Inc, 2019.

[3]          L. G. Shapiro and G. C. Stockman, Computer vision. New Jersey: Prentice-Hall, 2001.

[4]          F. Schroff, A. Criminisi, and A. Zisserman, "Single-histogram class models for image segmentation. In: Kalra, P. K. and Peleg, S. (eds.) Computer Vision, Graphics and Image Processing. Lecture Notes in Computer Science," ed Berlin, Heidelbergb: Springer, 2006, p. 4338.

[5]          T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silvermank, and A. Y. Wu, "An efficient k-means clustering algorithm: Analysis and implementation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, pp. 881-892, 2002. Available at: https://doi.org/10.1109/TPAMI.2002.1017616.

[6]          Z. Huang, "Extensions to the k-means algorithm for clustering large data sets with categorical values," Data Mining and Knowledge Discovery, vol. 2, pp. 283-304, 1998.

[7]          X. Wang, R. Hänsch, L. Ma, and O. Hellwich, "Comparison of different color spaces for image segmentation using graph-cut," in Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP-2014), 2014, pp. 301-308. Retrieved from: https://www.cv.tu-berlin.de/fileadmin/fg140/VISAPP_2014_127_CR.pdf .

[8]          P. J. Baldevbhai and R. Anand, "Color image segmentation for medical images using L* a* b* color space," IOSR Journal of Electronics and Communication Engineering, vol. 1, pp. 24-45, 2012. Available at: https://doi.org/10.9790/2834-0122445.

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Funding:

This work is supported by the USDA National Institute of Food and Agriculture, McIntire Stennis project [Accession No. 1019401].

Competing Interests:

The author declares that there are no conflicts of interests regarding the publication of this paper.

Acknowledgement:


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