Image-based coral reef classification and thematic mapping

A. S.M. Shihavuddin, Nuno Gracias, Rafael Garcia, Arthur C.R. Gleason, Brooke Gintert

Research output: Contribution to journalArticlepeer-review

56 Scopus citations


This paper presents a novel image classification scheme for benthic coral reef images that can be applied to both single image and composite mosaic datasets. The proposed method can be configured to the characteristics (e.g., the size of the dataset, number of classes, resolution of the samples, color information availability, class types, etc.) of individual datasets. The proposed method uses completed local binary pattern (CLBP), grey level co-occurrence matrix (GLCM), Gabor filter response, and opponent angle and hue channel color histograms as feature descriptors. For classification, either knearest neighbor (KNN), neural network (NN), support vector machine (SVM) or probability density weighted mean distance (PDWMD) is used. The combination of features and classifiers that attains the best results is presented together with the guidelines for selection. The accuracy and efficiency of our proposed method are compared with other state-of-the-art techniques using three benthic and three texture datasets. The proposed method achieves the highest overall classification accuracy of any of the tested methods and has moderate execution time. Finally, the proposed classification scheme is applied to a large-scale image mosaic of the Red Sea to create a completely classified thematic map of the reef benthos.

Original languageEnglish (US)
Pages (from-to)1809-1841
Number of pages33
JournalRemote Sensing
Issue number4
StatePublished - Apr 2013


  • Automated coral reef classification
  • Benthic habitat classification
  • Grey level co-occurrence matrix
  • Kernel mapping
  • Local binary pattern
  • Low resolution
  • Opponent angle
  • Optical imagery
  • Optical mapping
  • Probability density weighted mean distance
  • Support vector machine
  • Texture feature
  • Thematic mapping

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)


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