Abstract
Image segmentation is defined as: “the search for homogenous regions in an image and later the classification of these regions”. In this research, a remote sensing image, Pekanbaru city of Riau Province-Indonesia is provided for the green land segmentation. It is obtained by observing the surface of the earth using the Google Earth Imagery. To segment the green land of the given image, two different methods are used in this research, K-Means Clustering and Normalized RGB Color Space methods. This research is expected to have two clusters output: the spreading of green fields and not green fields. The result shows that the given Google Earth imagery can be segmented about 40.50 and 47.01 % pixels from all image pixels by K-Means Clustering and Normalized RGB Color Space respectively.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chipman, J., Kiefer, R.W., Lillesand, T.: Remote Sensing and Image Interpretation, 6th edn. Willey, Inc., USA (2007)
Ruzgiene, B., Xiang, Q.Y., Gecyte, S.: Rectification of satellite imagery from google earth: application for large scale city mapping. In: The 8th International Conference on Environmental Engineering, vol. 3, pp. 1451–1454. Vilnius, Lithuania (2011)
Li, Z., Liu, Z., Shi, W.: Semiautomatic airport runway extraction using a line-finder-aided level set evolution. IEEE J. Sel. Topics Appl. Earth Obs. 99, 1–13 (2014)
Aher, M., Pradhan, S., Dangdawate, Y.: Rainfall estimation over roof-top using land-cover classification of google earth images. In: International Conference on Electronic Systems, Signal Processing and Computing Technologies (ICESC), pp. 111–116. Nagpur, India (2014)
Coralini, A., Gidazzoli, A., Malfetti, P.: Cloud computing and virtual heritage: the social media-oriented paradigm experienced at cineca. In: IEEE 3rd Symposium on Network Cloud Computing and Applications (NCCA), pp. 121–124, Rome, Italy (2014)
Sahin, K., Ulusoy, I.: Automatic Multi-scale segmentation of high spatial resolution satellite images using waterhseds. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 2505–2508, Melbourne (2013)
Almeer, M.H.: Vegetation extraction from free google earth images of deserts using a robust BPNN approach in HSV Space. Int. J. Adv. Res. Comput. Commun. Eng. 2(5), 1–8 (2012)
Janssen, I., Rosu, A.: Measuring sidewalk distances using google earth. J. BMC Med. Res. Methodol. 12(1), 1–10 (2012)
Ploton, P., et al.: Assessing aboveground tropical forest biomass using google earth canopy images. In: Ecol. Soc. Am. Ecolo. Appl. 22(3), 993–1003 (2012)
Stepanus, R.: Analisis kebutuhan ruang terbuka hijau di kota Pekanbaru. Master Thesis Institute of Pertanian Bogor, Bogor, Indonesia (2006)
Chinki, C., Soni. C., Khurshid, A.A.: An approach to image segmentation using K-Means clustering algorithm. Int. J. Info. Technol. (IJIT) 1(1), 11–17 (2012)
Narayana, M.: Comparison between euclidean distance metric and SVM for CBIR using level set features. Int. J. Eng. Sci. Technol. (IJEST) 4(1), 56–66 (2012)
Ravindra, S., Sangolli, H., Rajeshwari, K.: Segmentation of google map images based on color features. In: Proceedings of International Conference on Communication, Computation, Management and Nanotechnology (ICN), pp 77–80 (2011)
Moreno, F., Andrade-Cetto, J., Sanfeliu, A.: Localization of human faces fusing color segmentation and depth from stereo. In: Proceedings of the 8th IEEE International Conference on Emerging Technologies and Factory Automation, vol. 2, pp. 527–535. Antibes, France (2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer India
About this paper
Cite this paper
Rozanda, N.E., Ismail, M., Permana, I. (2015). Segmentation Google Earth Imagery Using K-Means Clustering and Normalized RGB Color Space. In: Jain, L., Behera, H., Mandal, J., Mohapatra, D. (eds) Computational Intelligence in Data Mining - Volume 1. Smart Innovation, Systems and Technologies, vol 31. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2205-7_36
Download citation
DOI: https://doi.org/10.1007/978-81-322-2205-7_36
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2204-0
Online ISBN: 978-81-322-2205-7
eBook Packages: EngineeringEngineering (R0)