Application of Hybrid Techniques (Self-Organizing Map and Fuzzy Algorithm) Using Backscatter Data for Segmentation and Fine-Scale Roughness Characterization of Seepage-Related Seafloor Along the Western Continental Margin of India

Chakraborty, Bhaskar; Menezes, Andrew; Dandapath, Sumanta; Fernandes, William; Karisiddaiah, S. M.; Haris, K.; Gokul, G. S.

doi:10.1109/joe.2013.2294279

Cited by 18 publications

(3 citation statements)

References 27 publications

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“…The combination of image-based segmentation, dimensionality reduction, and density-based clustering provide an objective solution to synthesizing these data spatially to identify distinct seabed types. Similar solutions have applied PCA for dimensionality reduction of backscatter data followed by k-means clustering for unsupervised classification (e.g., , and other approaches towards similar ends have included combinations of self-organizing maps and fuzzy clustering (Chakraborty et al, 2015), and Bayesian probability estimation (Amiri-Simkooei et al, 2009;Simons and Snellen, 2009). Several properties of density-based clustering are desirable in an exploratory context though, including automatic identification of number of clusters, enhanced data visualization and hierarchical clustering solutions, and rejection of outliers that are dissimilar from other well-defined clusters.…”

Section: Benthic Map Predictionmentioning

confidence: 99%

Applying a Multi-Method Framework to Analyze the Multispectral Acoustic Response of the Seafloor

Menandro

Bastos

Misiuk

et al. 2022

Front. Remote Sens.

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Improvements to acoustic seafloor mapping systems have motivated novel marine geological and benthic biological research. Multibeam echosounders (MBES) have become a mainstream tool for acoustic remote sensing of the seabed. Recently, “multispectral” MBES backscatter, which is acquired at multiple operating frequencies, has been developed to characterize the seabed in greater detail, yet methods for the use of these data are still being explored. Here, we evaluate the potential for seabed discrimination using multispectral backscatter data within a multi-method framework. We present a novel MBES dataset acquired using four operating frequencies (170, 280, 400, and 700 kHz) near the Doce River mouth, situated on the eastern Brazilian continental shelf. Image-based and angular range analysis methods were applied to characterize the multifrequency response of the seabed. The large amount of information resulting from these methods complicates a manual seabed segmentation solution. The data were therefore summarized using a combination of dimensionality reduction and density-based clustering, enabling hierarchical spatial classification of the seabed with sparse ground-truth. This approach provided an effective solution to synthesizing these data spatially to identify two distinct acoustic seabed classes, with four subclasses within one of the broader classes, which corresponded closely with seafloor sediment samples collected at the site. The multispectral backscatter data also provided information in likely, unknown, sub-surface substrate differences at this site. The study demonstrates that the adoption of a multi-method framework combining image-based and angular range analysis methods with multispectral MBES data can offer significant advantages for seafloor characterization and mapping.

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Section: Benthic Map Predictionmentioning

confidence: 99%

Applying a Multi-Method Framework to Analyze the Multispectral Acoustic Response of the Seafloor

Menandro

Bastos

Misiuk

et al. 2022

Front. Remote Sens.

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“…Fernandes and Chakraborty [30] processed the correction of the angular response and beam pattern of EM 1002 multibeam sonar. Applications of using the multibeam sonar data for seabed classification have also been introduced in many studies [31][32][33][34]. Notably, the multibeam sonar and SSS suffer from the same angular response effect, but they have different beam pattern effects.…”

Section: Related Work In the Multibeam Sonar Aspectmentioning

confidence: 99%

A New Radiometric Correction Method for Side-Scan Sonar Images in Consideration of Seabed Sediment Variation

et al. 2017

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Abstract:Affected by the residual of time varying gain, beam patterns, angular responses, and sonar altitude variations, radiometric distortion degrades the quality of side-scan sonar images and seriously affects the application of these images. However, existing methods cannot correct distortion effectively, especially in the presence of seabed sediment variation. This study proposes a new radiometric correction method for side-scan sonar images that considers seabed sediment variation. First, the different effects on backscatter strength (BS) are analyzed, and along-track distortion is removed by establishing a linear relationship between distortion and sonar altitude. Second, because the angle-related effects on BSs with the same incident angle are the same, a novel method of unsupervised sediment classification is proposed for side-scan sonar images. Finally, the angle-BS curves of different sediments are obtained, and angle-related radiometric distortion is corrected. Experiments prove the validity of the proposed method.

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“…However, there are few studies that have offered general methods for using a machine-focused approach to combine and use the information found in co-located bathymetric digital elevation models (DEMs) and acoustic mosaics [16][17][18][19][20][21][22]. Modern multibeam sonars and processing software now typically produce geo-located bathymetry and backscatter mosaic products, thus offering the opportunity to treat both data sets together [21,23].…”

Section: Introductionmentioning

confidence: 99%

A Bathymetry- and Reflectivity-Based Approach for Seafloor Segmentation

2018

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A robust and flexible technique to segment seafloor acoustic mapping data by analyzing co-located bathymetric digital elevation models and acoustic backscatter mosaics is presented. The algorithm first uses principles of topographic openness, pattern recognition, and texture classification to identify geomorphic elements of the seafloor or "area kernels", and then derives the final seafloor segmentation by merging or splitting the kernels based on principles of similarity and multi-modality. The output is a collection of homogeneous, non-overlapping seafloor segments of consistent morphology and acoustic backscatter texture. Each labeled segment is enriched by a list of derived, physically-meaningful attributes that can be used for subsequent task-specific analysis.

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Application of Hybrid Techniques (Self-Organizing Map and Fuzzy Algorithm) Using Backscatter Data for Segmentation and Fine-Scale Roughness Characterization of Seepage-Related Seafloor Along the Western Continental Margin of India

Cited by 18 publications

References 27 publications

Applying a Multi-Method Framework to Analyze the Multispectral Acoustic Response of the Seafloor

Applying a Multi-Method Framework to Analyze the Multispectral Acoustic Response of the Seafloor

A New Radiometric Correction Method for Side-Scan Sonar Images in Consideration of Seabed Sediment Variation

A Bathymetry- and Reflectivity-Based Approach for Seafloor Segmentation

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