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Mapping Coral Reef Resilience Indicators Using Field and Remotely Sensed Data
Remote Sens. 2013, 5(4), 1809-1841; doi:10.3390/rs5041809
Article

Image-Based Coral Reef Classification and Thematic Mapping

1,* , 1
,
1
,
2
 and
3
1 Computer Vision and Robotics Group, Universitat de Girona, Campus Montilivi, Edifici P-IV, E-17071 Girona, Spain 2 Department of Physics, University of Miami, 1320 Campo Sano Ave., Coral Gables, FL 33146, USA 3 Department of Marine Geology and Geophysics, University of Miami, Rickenbacker Causeway, Coral Gables, FL 33146, USA
* Author to whom correspondence should be addressed.
Received: 21 February 2013 / Revised: 29 March 2013 / Accepted: 31 March 2013 / Published: 15 April 2013

Abstract

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 k-nearest 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.
Keywords: automated coral reef classification; benthic habitat classification; optical imagery; texture feature; kernel mapping; support vector machine; opponent angle; thematic mapping; optical mapping; probability density weighted mean distance; local binary pattern; grey level co-occurrence matrix; low resolution automated coral reef classification; benthic habitat classification; optical imagery; texture feature; kernel mapping; support vector machine; opponent angle; thematic mapping; optical mapping; probability density weighted mean distance; local binary pattern; grey level co-occurrence matrix; low resolution
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Shihavuddin, A.; Gracias, N.; Garcia, R.; Gleason, A.C.R.; Gintert, B. Image-Based Coral Reef Classification and Thematic Mapping. Remote Sens. 2013, 5, 1809-1841.

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