Segmentation of SAR images using similarity ratios for generating and clustering superpixels


ELECTRONICS LETTERS, vol.52, no.8, pp.654-655, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 52 Issue: 8
  • Publication Date: 2016
  • Doi Number: 10.1049/el.2016.0020
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.654-655
  • Keywords: radar imaging, synthetic aperture radar, image segmentation, pattern clustering, image colour analysis, iterative methods, SAR image segmentation, similarity ratios, superpixel clustering, superpixel generation, colour and spatial proximity linear combination, boundary recall, undersegmentation error, boundary adherence, SLIC algorithm, simple linear iterative clustering algorithm
  • Middle East Technical University Affiliated: Yes


The superpixels are groups of similar neighbouring pixels which are perceptually meaningful and representationally efficient segments. Among those existing superpixel generating algorithms, simple linear iterative clustering (SLIC) seems to be one of the simplest ones. Its simplicity is due to adaption of a distance measure which is a linear combination of colour and spatial proximity. It is this measure that is modified using a similarity ratio. This modified measure is used to label the pixels within the search areas for generating the superpixels. This generation phase is further augmented with a clustering phase based on the same formulated similarity metric, which clusters the superpixels into larger segments. It has been demonstrated that this modified version performs better in terms of boundary recall and undersegmentation error, and is more robust to the speckle noise than the one in SLIC. Moreover, the clustered segments formed by superpixels generated by this approach has better boundary adherence than those formed by superpixels generated by SLIC.