Call for Paper - July 2018 Edition
IJCA solicits original research papers for the July 2018 Edition. Last date of manuscript submission is June 20, 2018. Read More

Probabilistic Distance Clustering: A Spatial Clustering Approach for Remote Sensing Imagery

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2015
Y. Jayababu, G.P.S. Varma, A. Govardhan

Y Jayababu, G P S Varma and A Govardhan. Article: Probabilistic Distance Clustering: A Spatial Clustering Approach for Remote Sensing Imagery. International Journal of Computer Applications 126(14):9-14, September 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Y. Jayababu and G.P.S. Varma and A. Govardhan},
	title = {Article: Probabilistic Distance Clustering: A Spatial Clustering Approach for Remote Sensing Imagery},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {126},
	number = {14},
	pages = {9-14},
	month = {September},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


Spatial clustering has been widely applied in various applications, especially in remote sensing technology. Clustering the geographical nature of the remote sensing imagery is challenging due to its wide and dense spatial distribution. Renowned clustering algorithms such as k-means and other probabilistic clustering algorithms have been reported in the literature. However, they are not robust to handle such peculiar data distribution. This paper employs probabilistic d – clustering algorithm to cluster the geographical information of the remote sensing imagery. The methodology considers diverse neighborhood connectivity and degree of connectivity to investigate the performance of probabilistic d – clustering algorithm. Experimental investigation demonstrates that probabilistic d – clustering algorithm is better than k – means clustering algorithm in handling remote sensing imagery.


  1. Li, N., Huo, H., Zhao, Y.M., Chen, X. and Fang, T. 2013. A Spatial Clustering Method with Edge Weighting for Image Segmentation. IEEE Geoscience and Remote Sensing Letters. 10 (Sep. 2013), 1124-1128.
  2. Pal, N.R. and Pal, S.K. 1993. A review on image segmentation techniques. Pattern Recognit. 26 (Sep. 1993), 1277-1294.
  3. Cloude, S.R. and Pottier, E. 1997. An entropy based classification scheme for land applications of polarimetric SAR. IEEE Trans. Geosci. Remote Sens. 35 (Jan. 1997), 68-78.
  4. Van, Z. 1989. Unsupervised classification of scattering mechanisms using radar polarimetry data. IEEE Trans. Geosci. Remote Sens. 27 (1989), 36-45.
  5. Lee, J.S., Grunes, M. and Kwok, R. 1994. Classification of multi-look polarimetric SAR imagery based on the complex Wishart distribution. Int. J. Remote Sens. 15 (1994), 2299-2311.
  6. Zhao, Y.Q., Zhang, D. and Kong, S.G. 2011. Band-Subset-Based Clustering and Fusion for Hyperspectral Imagery Classification. IEEE Trans. Geosci. Remote Sens. 49 (Feb. 2011), 747-756.
  7. Yu, D.J., Hu, J., Yang, J., Shen, H.B., Tang, J. and Yang, J.Y. 2013. Designing Template-Free Predictor for Targeting Protein-Ligand Binding Sites with Classifier Ensemble and Spatial Clustering. IEEE/ACM Trans. Computational Biology and Bioinformatics. 10 (Jul.-Aug. 2013), 994-1008.
  8. Packer, E., Bak, P., Nikkila, M., Polishchuk, V. and Ship, H.J. 2013. Visual Analytics for Spatial Clustering: Using a Heuristic Approach for Guided Exploration. IEEE Trans. Visualization and Computer Graphics. 19 (Dec. 2013), 2179-2188.
  9. Comaniciu, D. 2002. Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24 (May 2002), 603-619.
  10. Ilea, D.E. and Whelan, P.F. 2008. CTex—An adaptive unsupervised segmentation algorithm based on color–texture coherence. IEEE Trans. Image Process. 17 (Oct. 2008), 1926-1939.
  11. Kuwahara, K.H., Ehiu, S. and Kinoshita, M. 1976. Processing of riangiocardiographic images. In Proceedings of the Digital Processing of Biomedical Images. New York: Plenum, 187-203.
  12. Dong, G. and Xie, M. 2005. Color clustering and learning for image segmentation based on neural networks. IEEE Trans. Neural Netw. 16 (Jul. 2005), 925-936.
  13. Xiangrong, Z., Jiao, L., Liu, F., Bo, L. and Gong, M. 2008. Spectral clustering ensemble applied to SAR image segmentation. IEEE Trans. Geosci. Remote Sens. 46 (Jul. 2008), 2126-2136.
  14. Tarabalka, Y., Benediktsson, J.A. and Chanussot, J. 2009. Spectral–spatial classification of hyperspectral imagery based on partitional clustering techniques. IEEE Trans. Geosci. Remote Sens. 47 (Aug. 2009), 2973-2987.
  15. Liew, A.W., Yan, H. and Law, N.F. 2005. Image segmentation based on adaptive cluster prototype estimation. IEEE Trans. Fuzzy Syst. 13 (Aug. 2005), 444-453.
  16. Dulyakarn, P. and Rangsanseri, Y. 2001. Fuzzy C-means clustering using spatial information with application to remote sensing. In Proceedings of the 22nd Asian Conference on Remote Sens., Singapore.
  17. Ahmed, M.N., Yamany, S. M., Mohamed, N., Farag, A.A. and Moriarty, T. 2002. A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans. Med. Imag. 21 (Mar. 2002), 193-199.
  18. Akbari, V., Doulgeris, A.P., Moser, G., Eltoft, T., Anfinsen, S.N. and Serpico, S.B. 2013. A Textural–Contextual Model for Unsupervised Segmentation of Multipolarization Synthetic Aperture Radar Images. IEEE Trans. Geosci. Remote Sens. 51 (Apr. 2013), 2442-2453.
  19. Yang, S., Qiao,Y., Yang, L., Jin, P.L. and Jiao, L. 2014. Hyperspectral Image Classification Based on Relaxed Clustering Assumption and Spatial Laplace Regularizer. IEEE Geoscience and Remote Sensing Letters. 11 (May 2014), 901-905.
  20. Zhong, Y., Ma, A. and Zhang, L. 2014. An Adaptive Memetic Fuzzy Clustering Algorithm with Spatial Information for Remote Sensing Imagery. IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing. 7 (Apr. 2014), 1235-1248.
  21. Ji, J. and Wang, K.L. 2014. A Robust Nonlocal Fuzzy Clustering Algorithm with Between-Cluster Separation Measure for SAR Image Segmentation. IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing. 7 (Dec. 2014), 4929- 4936.
  22. Wang, J. and Su, X. “An improved K-Means clustering algorithm”, 2011 IEEE 3rd International Conference on Communication Software and Networks (ICCSN), Page(s): 44 – 46, 2011
  23. Bacher, J. 2000. A Probabilistic Clustering Model for Variables of Mixed Type. Quality and Quantity. 34 (Aug. 2000), 223-235.
  24. Iyigun, C., Ben-Israel, A. 2009. Semi-supervised Probabilistic Distance Clustering and the Uncertainty of Classification. Advances in Data Analysis, Data Handling and Business Intelligence, Studies in Classification, Data Analysis, and Knowledge Organization, 3-20.
  25. Ben-Israel, A., Iyigun, C. 2008. Probabilistic D-Clustering. J. Classification. 25 (Jun. 2008), 5-26.
  26. Davies, D.L., Bouldin, D.W. 1979. A Cluster Separation Measure. IEEE Trans. Pattern Analysis and Machine Intelligence. PAMI-1(Apr. 1979), 224-227.


Spatial clustering; probabilistic d – clustering; remote sensing; geographic clustering