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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, 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

This work was partially funded by the Spanish MICINN under grant CTM2010-15216 (MuMap) and by the EU Project FP7-ICT-2011-288704 (MORPH) and US DoD/DoE/EPA project ESTCP SI2010. ASM Shihavuddin was supported by the MICINN under the FI program. Art Gleason was supported by a grant from the Gale Foundation

Remote Sensing, 2013, vol. 5, núm. 4, p. 1809-1841

MDPI (Multidisciplinary Digital Publishing Institute)

Autor: Shihavuddin, A.S.M.
Grácias, Nuno Ricardo Estrela
García Campos, Rafael
Gleason, Arthur C. R.
Gintert, Brooke
Data: 2013
Resum: 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
This work was partially funded by the Spanish MICINN under grant CTM2010-15216 (MuMap) and by the EU Project FP7-ICT-2011-288704 (MORPH) and US DoD/DoE/EPA project ESTCP SI2010. ASM Shihavuddin was supported by the MICINN under the FI program. Art Gleason was supported by a grant from the Gale Foundation
Format: application/pdf
ISSN: 2072-4292 (versió paper)
2072-4292 (versió electrònica)
Accés al document: http://hdl.handle.net/10256/8507
Llenguatge: eng
Editor: MDPI (Multidisciplinary Digital Publishing Institute)
Col·lecció: MICINN/PN 2011-2014/CTM2010-15216
Reproducció digital del document publicat a: http://dx.doi.org/10.3390/rs5041809
Articles publicats (D-ATC)
info:eu-repo/grantAgreement/EC/FP7/288704
És part de: Remote Sensing, 2013, vol. 5, núm. 4, p. 1809-1841
Drets: Attribution 3.0 Spain
URI Drets: http://creativecommons.org/licenses/by/3.0/es/
Matèria: Kernel, Funcions de
Kernel functions
Teledetecció
Remote sensing
Títol: Image-Based Coral Reef Classification and Thematic Mapping
Tipus: info:eu-repo/semantics/article
Repositori: DUGiDocs

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