Applying Anomalous Cluster Approach to Spatial Clustering

Part of the Studies in Computational Intelligence book series (SCI, volume 683)


The concept of anomalous clustering applies to finding individual clusters on a digital geography map supplied with a single feature such as brightness or temperature. An algorithm derived within the individual anomalous cluster framework extends the so-called region growing algorithms. Yet our approach differs in that the algorithm parameter values are not expert-driven but rather derived from the anomalous clustering model. This novel framework successfully applies to the issue of automatically delineating coastal upwelling from Sea Surface Temperature (SST) maps, a natural phenomenon seasonally occurring in coastal waters.



The authors thank the colleagues of Centro de Oceanografia and Department de Engenharia Geográfica, Geofísica e Energia (DEGGE), Faculdade de Ciências, Universidade de Lisboa for providing the SST images examined in this study. The authors are thankful to the anonymous reviewers for their insightful and constructive comments that allowed us to improve our paper.


  1. 1.
    Nascimento, S., Franco, P.: Segmentation of upwelling regions in sea surface temperature images via unsupervised fuzzy clustering. In: Corchado, E. and Yin, H. (Eds.), Procs. Intelligent Data Engineering and Automated Learning (IDEAL 2009), LNCS 5788, Springer-Verlag, 543–553 (2009)Google Scholar
  2. 2.
    Nascimento, S., Franco, P., Sousa, F., Dias, J., Neves, F.: Automated computational delimitation of SST upwelling areas using fuzzy clustering, Computers & Geosciences 43, 207–216 (2012)Google Scholar
  3. 3.
    Mirkin, B.: Clustering: A Data Recovery Approach, 2nd Edition, Chapman and Hall, Boca Raton (2012)Google Scholar
  4. 4.
    Mirkin, B.: A sequential fitting procedure for linear data analysis models. Journal of Classification 7, 167–195 (1990)Google Scholar
  5. 5.
    Arriaza, J., Rojas, F., Lopez, M., Canton, M.: Competitive neural-net-based system for the automatic detection of oceanic mesoscalar structures on AVHRR scenes. IEEE Transactions on Geoscience and Remote Sensing 41(4), 845–885 (2003)Google Scholar
  6. 6.
    Chaudhari, S., Balasubramanian, R., Gangopadhyay, A.: Upwelling Detection in AVHRR Sea Surface Temperature (SST) Images using Neural-Network Framework. 2008 IEEE International Geoscience & Remote Sensing Symposium II, 926–929 (2008)Google Scholar
  7. 7.
    Kriebel, S. T., Brauer, W., Eifler, W.: Coastal upwelling prediction with a mixture of neural networks. IEEE Transactions on Geoscience and Remote Sensing 36(5), 1508–1518 (1998)Google Scholar
  8. 8.
    Marcello, J., Marques, F., Eugenio, F.: Automatic tool for the precise detection of upwelling and filaments in remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing 43(7), 1605–1616 (2005)Google Scholar
  9. 9.
    Nieto, K., Demarcq, H., McClatchie, S.: Mesoscale frontal structures in the Canary Upwelling System: New front and filament detection algorithms applied to spatial and temporal patterns. Remote Sensing of Environment 123, 339–346 (2012)Google Scholar
  10. 10.
    Adams, R., Bischof, L.: Seeded region growing. IEEE Transactions on Pattern Analasys and Machine Intelligence 16, 641–647 (1994)Google Scholar
  11. 11.
    Fan, J., Zeng, G., Body, M., Hacid, M.-S.: Seeded region growing: an extensive and comparative study. Pattern Recognition Letters 26(8), 1139–1156 (2005)Google Scholar
  12. 12.
    Mehnert, A., Jackway, P.: An improved seeded region growing algorithm. Pattern Recognition Letters 18(10), 1065–1071 (1997)Google Scholar
  13. 13.
    Shih, F., Cheng, S.: Automatic seeded region growing for color image segmentation. Image and Vision Computing, 23, 877–886 (2005)Google Scholar
  14. 14.
    Verma, O., Hanmandlu, M., Seba, S., Kulkarni, M., Jain, P.: A Simple Single Seeded Region Growing Algorithm for Color Image Segmentation using Adaptive Thresholding, Procs. of the 2011 International Conference on Communication Systems and Network Technologies, IEEE Computer Society, Washington, DC, USA, pp. 500–503 (2011)Google Scholar
  15. 15.
    Harikrishna-Rai, G. N., Gopalakrishnan-Nair, T.R.: Gradient Based Seeded Region Grow method for CT Angiographic Image Segmentation. International Journal of Computer Science and Networking 1(1), 1–6 (2010)Google Scholar
  16. 16.
    Mancas, M., Gosselin, B., Macq, B.: Segmentation Using a Region Growing Thresholding. Proc. SPIE 5672, Image Processing: Algorithms and Systems IV 388 (2005)Google Scholar
  17. 17.
    Wu, J., Poehlman, S., Noseworthy, M. D., Kamath, M.: Texture feature based automated seeded region growing in abdominal MRI segmentation. Journal of Biomedical Science and Engineering 2, 1–8 (2009)Google Scholar
  18. 18.
    Zanaty, E.A.: Improved region growing method for magnetic resonance images (MRIs) segmentation. American Journal of Remote Sensing 1(2), 53–60 (2013)Google Scholar
  19. 19.
    Fan, J., Yau, D.K.Y., Elmagarmid, A. K., Aref, W. G.: Automatic image segmentation by integrating color-based extraction and seeded region growing. IEEE Transactions on Image Processing 10(10), 1454–1466 (2001)Google Scholar
  20. 20.
    Ugarriza, L. G., Saber, E., Vantaram, S.R., Amuso, V., Shaw, M., Bhaskar, R.: Automatic Image Segmentation by Dynamic Region Growth and Multiresolution Merging. IEEE Transactions on Image Processing 18(10), 2275–2288 (2009)Google Scholar
  21. 21.
    Byun, Y., Kim, D., Lee, J., Kim, Y.: A framework for the segmentation of high-resolution satellite imagery using modified seeded-region growing and region merging. International Journal of Remote Sensing 32(16), 4589–4609 (2011)Google Scholar
  22. 22.
    Zhang, T., Yang, X., Hu, S., Su, F.: Extraction of Coastline in Aquaculture Coast from Multispectral Remote Sensing Images: Object-Based Region Growing Integrating Edge Detection. Remote Sensing 5(9), 4470–4487 (2013)Google Scholar
  23. 23.
    Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on System, Man, and Cybernetics SMC-9(1), 62–66 (1979)Google Scholar
  24. 24.
    Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging 13(1), 146–168 (2004)Google Scholar
  25. 25.
    Nascimento, S., Casca, S., Mirkin, B.: A Seed Expanding Cluster Algorithm for Deriving Upwelling Areas on Sea Surface Temperature Images, Computers & Geociences Special issue on “Statistical learning in geoscience modelling: novel algorithms and challenging case studies”, Elsevier 85(Part B), 74–85 (2015) doi: 10.1016/j.cageo.2015.06.002

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer Science and NOVA Laboratory for Computer Science and Informatics (NOVA LINCS)Faculdade de Ciências e Tecnologia, Universidade Nova de LisboaCaparicaPortugal
  2. 2.Department of Data Analysis and Machine IntelligenceNational Research University Higher School of EconomicsMoscowRussian Federation
  3. 3.Department of Computer ScienceBirkbeck University of LondonLondonUK

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