Utilizad este identificador para citar o enlazar este documento: http://hdl.handle.net/2072/231989

Image-Based Coral Reef Classification and Thematic Mapping
Shihavuddin, A.S.M.; Grácias, Nuno Ricardo Estrela; García Campos, Rafael; Gleason, Arthur C. R.; Gintert, Brooke
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
07-06-2014
Kernel, Funcions de
Kernel functions
Teledetecció
Remote sensing
Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
Artículo
info:eu-repo/semantics/publishedVersion
MDPI
         

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