Benthic Habitat Mapping from Seabed Images using Ensemble of Color, Texture, and Edge Features

Abstract In this paper we present a novel approach to produce benthic habitat maps from sea floor images in Derwent estuary. We have developed a step—by—step segmentation method to separate sea—grass, sand, and rock from the sea floor image. The sea—grass was separated first using color filtering. T...
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Autor*in:	Rahman, Ashfaqur [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2013

Schlagwörter:	Benthic habitat mapping ensemble classifier image classification

Anmerkung:	© the authors 2017

Übergeordnetes Werk:	Enthalten in: International journal of computational intelligence systems - Paris : Atlantis Press, 2008, 6(2013), 6 vom: 01. Nov., Seite 1072-1081
Übergeordnetes Werk:	volume:6 ; year:2013 ; number:6 ; day:01 ; month:11 ; pages:1072-1081

Links:	Volltext

DOI / URN:	10.1080/18756891.2013.816055

Katalog-ID:	SPR054572908

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allfieldsSound	10.1080/18756891.2013.816055 doi (DE-627)SPR054572908 (SPR)18756891.2013.816055-e DE-627 ger DE-627 rakwb eng Rahman, Ashfaqur verfasserin aut Benthic Habitat Mapping from Seabed Images using Ensemble of Color, Texture, and Edge Features 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © the authors 2017 Abstract In this paper we present a novel approach to produce benthic habitat maps from sea floor images in Derwent estuary. We have developed a step—by—step segmentation method to separate sea—grass, sand, and rock from the sea floor image. The sea—grass was separated first using color filtering. The remaining image was classified into rock and sand based on color, texture, and edge features. The features were fed into an ensemble classifier to produce better classification results. The base classifiers in the ensemble were made complementary by changing the weight (i.e. cost of misclassification) of the classes. The habitat maps were produced for three regions in Derwent estuary. Experimental results demonstrate that the proposed method can indentify different objects and produce habitat maps from the sea—floor images with very high accuracy. Benthic habitat mapping (dpeaa)DE-He213 ensemble classifier (dpeaa)DE-He213 image classification (dpeaa)DE-He213 Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 6(2013), 6 vom: 01. Nov., Seite 1072-1081 (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:6 year:2013 number:6 day:01 month:11 pages:1072-1081 https://dx.doi.org/10.1080/18756891.2013.816055 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2013 6 01 11 1072-1081
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source	Enthalten in International journal of computational intelligence systems 6(2013), 6 vom: 01. Nov., Seite 1072-1081 volume:6 year:2013 number:6 day:01 month:11 pages:1072-1081
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author	Rahman, Ashfaqur
spellingShingle	Rahman, Ashfaqur misc Benthic habitat mapping misc ensemble classifier misc image classification Benthic Habitat Mapping from Seabed Images using Ensemble of Color, Texture, and Edge Features
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topic_title	Benthic Habitat Mapping from Seabed Images using Ensemble of Color, Texture, and Edge Features Benthic habitat mapping (dpeaa)DE-He213 ensemble classifier (dpeaa)DE-He213 image classification (dpeaa)DE-He213
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title	Benthic Habitat Mapping from Seabed Images using Ensemble of Color, Texture, and Edge Features
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title_full	Benthic Habitat Mapping from Seabed Images using Ensemble of Color, Texture, and Edge Features
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title_sort	benthic habitat mapping from seabed images using ensemble of color, texture, and edge features
title_auth	Benthic Habitat Mapping from Seabed Images using Ensemble of Color, Texture, and Edge Features
abstract	Abstract In this paper we present a novel approach to produce benthic habitat maps from sea floor images in Derwent estuary. We have developed a step—by—step segmentation method to separate sea—grass, sand, and rock from the sea floor image. The sea—grass was separated first using color filtering. The remaining image was classified into rock and sand based on color, texture, and edge features. The features were fed into an ensemble classifier to produce better classification results. The base classifiers in the ensemble were made complementary by changing the weight (i.e. cost of misclassification) of the classes. The habitat maps were produced for three regions in Derwent estuary. Experimental results demonstrate that the proposed method can indentify different objects and produce habitat maps from the sea—floor images with very high accuracy. © the authors 2017
abstractGer	Abstract In this paper we present a novel approach to produce benthic habitat maps from sea floor images in Derwent estuary. We have developed a step—by—step segmentation method to separate sea—grass, sand, and rock from the sea floor image. The sea—grass was separated first using color filtering. The remaining image was classified into rock and sand based on color, texture, and edge features. The features were fed into an ensemble classifier to produce better classification results. The base classifiers in the ensemble were made complementary by changing the weight (i.e. cost of misclassification) of the classes. The habitat maps were produced for three regions in Derwent estuary. Experimental results demonstrate that the proposed method can indentify different objects and produce habitat maps from the sea—floor images with very high accuracy. © the authors 2017
abstract_unstemmed	Abstract In this paper we present a novel approach to produce benthic habitat maps from sea floor images in Derwent estuary. We have developed a step—by—step segmentation method to separate sea—grass, sand, and rock from the sea floor image. The sea—grass was separated first using color filtering. The remaining image was classified into rock and sand based on color, texture, and edge features. The features were fed into an ensemble classifier to produce better classification results. The base classifiers in the ensemble were made complementary by changing the weight (i.e. cost of misclassification) of the classes. The habitat maps were produced for three regions in Derwent estuary. Experimental results demonstrate that the proposed method can indentify different objects and produce habitat maps from the sea—floor images with very high accuracy. © the authors 2017
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title_short	Benthic Habitat Mapping from Seabed Images using Ensemble of Color, Texture, and Edge Features
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