Image-Based Coral Reef Classification and Thematic Mapping

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, col...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2013-04, Vol.5 (4), p.1809-1841
Main Authors: Shihavuddin, A.S.M., Gracias, Nuno, Garcia, Rafael, Gleason, Arthur, Gintert, Brooke
Format: Article
Language:English
Subjects:
automated coral reef classification
benthic habitat classification
Benthos
Classification
Coral reefs
grey level co-occurrence matrix
Kernel functions
kernel mapping
Kernel, Funcions de
local binary pattern
low resolution
opponent angle
optical imagery
optical mapping
probability density weighted mean distance
Remote sensing
support vector machine
Teledetecció
texture feature
thematic mapping
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs5041809