A Texture Analysis Approach to Identifying Sabellaria Spinulosa Colonies in Sidescan Sonar Imagery

Harrison, R.F.; Bianconi, Francesco; Harvey, Richard W.; Wang, Wenjia

doi:10.1109/imvip.2011.19

Cited by 6 publications

(7 citation statements)

References 23 publications

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“…18 For an evaluation of several state-of-the-art statistical, signal processing-based and morphologybased features applied to a related problem on sonar waterfall imagery, refer to our recent paper. 13 Since GLCM features are probably the most widely used in the domain, we apply them in our test cases.…”

Section: Application Contextmentioning

confidence: 99%

“…45,50 Of particular concern are protected habitats, such as reefs formed by colonies of the tube-building worm, Sabellaria spinulosa (Sabellaria). 13,51,52 In our test cases, we focus on Sabellaria as the target texture in sonar mosaic imagery e Directionally dependent features can be useful for discriminating anisotropic textures but this is not of concern here. and attempt to discriminate it from other specific seabed types.…”

Section: Experimental Datamentioning

confidence: 99%

“…In addition, we give some consideration to Sabellaria textures in waterfall imagery. 13 Photographic images of a selection of natural textures from the Outex database are also used as a benchmark for wider comparisons. Samples of the imagery are shown in Fig.…”

Section: Experimental Datamentioning

confidence: 99%

“…Evaluation of several state-of-the-art statistical, signal processing-based and morphologybased features applied to a related problem has been presented in our recently published paper. 13 The remainder of this paper is organized as follows. In Sec.…”

Section: Introductionmentioning

confidence: 99%

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Novel Consensus Approaches to the Reliable Ranking of Features for Seabed Imagery Classification

Harrison

Birchall²,

Mann³

et al. 2012

Int. J. Neur. Syst.

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Feature saliency estimation and feature selection are important tasks in machine learning applications. Filters, such as distance measures are commonly used as an efficient means of estimating the saliency of individual features. However, feature rankings derived from different distance measures are frequently inconsistent. This can present reliability issues when the rankings are used for feature selection. Two novel consensus approaches to creating a more robust ranking are presented in this paper. Our experimental results show that the consensus approaches can improve reliability over a range of feature parameterizations and various seabed texture classification tasks in sidescan sonar mosaic imagery.

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Section: Application Contextmentioning

confidence: 99%

Section: Experimental Datamentioning

confidence: 99%

Section: Experimental Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Novel Consensus Approaches to the Reliable Ranking of Features for Seabed Imagery Classification

Harrison

Birchall²,

Mann³

et al. 2012

Int. J. Neur. Syst.

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“…Although both first -order statistical texture and second-order statistical texture analysis have been proven as promising input for image segmentation and classification, most studies tend to use second-order analysis. A popular subject of research in sidescan sonar image analysis that can be found in several studies is use of the Grey Level Co-occurrence Matrix (GLCM) method to extract the second-order texture features (Lianantonakis & Petillot, 2007;Harrison et al, 2011;Hamilton, 2015;Buscombe, 2017;Hamill et al, 2018). Blondel et al, (1998) demonstrated GLCM s could be successfully used to segment SSS images.…”

mentioning

confidence: 99%

Support Vector Machine and Decision Tree Based Classification of Side-Scan Sonar Mosaics Using Textural Features

Febriawan

Helmholz

Parnum

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> The diversity and heterogeneity of coastal, estuarine and stream habitats has led to them becoming a prevalent topic for study. Woody ruins are areas of potential riverbed habitat, particularly for fish. Therefore, the mapping of those areas is of interest. However, due to the limited visibility in some river systems, satellites, airborne or other camera-based systems (passive systems) cannot be used. By contrast, sidescan sonar is a popular underwater acoustic imaging system that is capable of providing high- resolution monochromatic images of the seafloor and riverbeds. Although the study of sidescan sonar imaging using supervised classification has become a prominent research subject, the use of composite texture features in machine learning classification is still limited. This study describes an investigation of the use of texture analysis and feature extraction on side-scan sonar imagery in two supervised machine learning classifications: Support Vector Machine (SVM) and Decision Tree (DT). A combination of first- order texture and second-order texture is investigated to obtain the most appropriate texture features for the image classification. SVM, using linear and Gaussian kernels along with Decision Tree classifiers, was examined using selected texture features. The results of overall accuracy and kappa coefficient revealed that SVM using a linear kernel leads to a more promising result, with 77% overall accuracy and 0.62 kappa, than SVM using either a Gaussian kernel or Decision Tree (60% and 73% overall accuracy, and 0.39 and 0.59 kappa, respectively). However, this study has demonstrated that SVM using linear and Gaussian kernels as well as a Decision Tree makes it capable of being used in side-scan sonar image classification and riverbed habitat mapping.</p>

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Integration of close‐range underwater photogrammetry with inspection and mesh processing software: a novel approach for quantifying ecological dynamics of temperate biogenic reefs

Ventura¹,

Dubois

Bonifazi

et al. 2020

Remote Sens Ecol Conserv

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Characterizing and monitoring changes in biogenic 3‐dimensional (3D) structures at multiple scales over time is challenging within the practical constraints of conventional ecological tools. Therefore, we developed a structure‐from‐motion (SfM)‐based photogrammetry method, coupled with inspection and mesh processing software, to estimate important ecological parameters of underwater worm colonies (hummocks) constructed by the sabellariid polychaete Sabellaria alveolata, using non‐destructive, 3D modeling and mesh analysis. High resolution digital images of bioconstructions (hummocks) were taken in situ under natural conditions to generate digital 3D models over different sampling periods to analyse the morphological evolution of four targeted hummocks. 3D models were analysed in GOM Inspect software, a powerful and freely available mesh processing software to follow growth as well as morphology changes over time of each hummock. Linear regressions showed 3D models only slightly overestimated the real dimensions of the reference objects with an average error < 5% between measured and model‐estimated dimensions for both length and volume. Manual inspection of models and semi‐automated surface‐to‐surface comparison allowed the computation of important metrics linked to the ecology of temperate reefs such as volume, surface area, surface complexity/rugosity, number and size of holes and creeks and the mean density of living worms per colony. Moreover we demonstrated the reliability of 3D surface complexity estimates against two linear rugosity measures: a traditional and a virtual variant of the ‘chain‐and‐tape’ method. Finally, besides 3D models deviation analysis via surface comparison, a Bayesian latent variable model approach was adopted to highlight the significative effects of sea state conditions on S. alveolata hummocks metrics. We demonstrated without damaging the benthic organisms that SfM approach allow continuous study of temperate bioconstruction leading to a fine description of short‐term structural modification mediated by hydrodynamics, making this technique accessible and repeatable to many other areas of ecological research.

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A Texture Analysis Approach to Identifying Sabellaria Spinulosa Colonies in Sidescan Sonar Imagery

Cited by 6 publications

References 23 publications

Novel Consensus Approaches to the Reliable Ranking of Features for Seabed Imagery Classification

Novel Consensus Approaches to the Reliable Ranking of Features for Seabed Imagery Classification

Support Vector Machine and Decision Tree Based Classification of Side-Scan Sonar Mosaics Using Textural Features

Integration of close‐range underwater photogrammetry with inspection and mesh processing software: a novel approach for quantifying ecological dynamics of temperate biogenic reefs

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