A Novel Graph Based Clustering Technique for Hybrid Segmentation of Multi-spectral Remotely Sensed Images

  • Biplab Banerjee
  • Pradeep Kumar Mishra
  • Surender Varma
  • Buddhiraju Krishna Mohan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8192)


This paper proposes a novel unsupervised graph based clustering method for the purpose of hybrid segmentation of multi-spectral satellite images. In hybrid image segmentation framework, the source image is initially (over)segmented while preserving the fine image details. A region merging strategy has to be adopted next for further refinement. Here mean-shift (MS) based technique has been considered for initially segmenting the source image as it performs edge preserving smoothing beforehand hence eliminates noise. The objects found after this step are merged together in a low-level image feature space using the proposed graph based clustering algorithm. A graph topology combining k-nearest-neighbor (KNN) and minimum spanning tree has been considered on which the proposed iterative algorithm has been applied to eliminate the edges which span different clusters. It results in a set of connected components where each component represents a separate cluster. Comparison with two other hybrid segmentation techniques establishes the comparable accuracies of the proposed framework.


Image Segmentation Graph Based Clustering Mean-Shift Hybrid Segmentation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Biplab Banerjee
    • 1
  • Pradeep Kumar Mishra
    • 2
  • Surender Varma
    • 1
  • Buddhiraju Krishna Mohan
    • 1
  1. 1.Satellite Image Processing Lab, CSREIITBombayIndia
  2. 2.Vision and Image Processing Lab, EEIIT BombayIndia

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