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Texture analysis of seabed images: Quantifying the presence of Posidonia oceanica at Palma Bay

Texture analysis of seabed images: Quantifying the presence of Posidonia oceanica at Palma Bay
Texture analysis of seabed images: Quantifying the presence of Posidonia oceanica at Palma Bay
An automatic classifier algorithm has been designed to assess the population of Posidonia oceanica over a set of underwater images at Palma Bay. Law's energy filters and statistical descriptors of the Gray Level Co-occurrence Matrix have been use to correctly classify the input image patches in two classes: Posidonia oceanica or not Posidonia oceanica. The input images have been first preprocessed and splitted in three different patch sizes in order to find the best patch size to better classify this seagrass. From all the attributes obtained in these patches, a best subset algorithm has been run to choose the best ones and a decision tree classifier has been trained. The classifier was made by training a Logistic Model Tree from 125 pre-classified images. This classifier was finally tested on 100 new images. The classifier outputs gray level images where black color indicates Posidonia oceanica presence and white no presence. Intermediate values are obtained by overlapping the processed patches, resulting in a smoother final result. This images can be merged in an offline process to obtain density maps of this algae in the sea.
Massot-Campos, Miquel
a55d7b32-c097-4adf-9483-16bbf07f9120
Oliver-Codina, Gabriel
99a9b816-4f5d-4724-8525-aa7c45fb0d3d
Ruano-Amengual, Laura
5cc5ecc3-79b8-4224-b33a-718516f5407c
Miro-Julia, Margaret
0fbe1045-a5d6-40a5-85d7-8a793f57e47e
Massot-Campos, Miquel
a55d7b32-c097-4adf-9483-16bbf07f9120
Oliver-Codina, Gabriel
99a9b816-4f5d-4724-8525-aa7c45fb0d3d
Ruano-Amengual, Laura
5cc5ecc3-79b8-4224-b33a-718516f5407c
Miro-Julia, Margaret
0fbe1045-a5d6-40a5-85d7-8a793f57e47e

Massot-Campos, Miquel, Oliver-Codina, Gabriel, Ruano-Amengual, Laura and Miro-Julia, Margaret (2013) Texture analysis of seabed images: Quantifying the presence of Posidonia oceanica at Palma Bay. In, OCEANS 2013 MTS/IEEE Bergen: The Challenges of the Northern Dimension. (OCEANS 2013 MTS/IEEE Bergen: The Challenges of the Northern Dimension, , (doi:10.1109/OCEANS-Bergen.2013.6607991)) (doi:10.1109/OCEANS-Bergen.2013.6607991).

Record type: Book Section

Abstract

An automatic classifier algorithm has been designed to assess the population of Posidonia oceanica over a set of underwater images at Palma Bay. Law's energy filters and statistical descriptors of the Gray Level Co-occurrence Matrix have been use to correctly classify the input image patches in two classes: Posidonia oceanica or not Posidonia oceanica. The input images have been first preprocessed and splitted in three different patch sizes in order to find the best patch size to better classify this seagrass. From all the attributes obtained in these patches, a best subset algorithm has been run to choose the best ones and a decision tree classifier has been trained. The classifier was made by training a Logistic Model Tree from 125 pre-classified images. This classifier was finally tested on 100 new images. The classifier outputs gray level images where black color indicates Posidonia oceanica presence and white no presence. Intermediate values are obtained by overlapping the processed patches, resulting in a smoother final result. This images can be merged in an offline process to obtain density maps of this algae in the sea.

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Published date: 2013

Identifiers

Local EPrints ID: 428715
URI: http://eprints.soton.ac.uk/id/eprint/428715
PURE UUID: d8cd63f4-ebae-47fb-bd7e-0488e2a2fb03
ORCID for Miquel Massot-Campos: ORCID iD orcid.org/0000-0002-1202-0362

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Date deposited: 07 Mar 2019 17:30
Last modified: 23 Jul 2020 00:46

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