scholarly journals Acoustic Seafloor Classification Using the Weyl Transform of Multibeam Echosounder Backscatter Mosaic

2021 ◽  
Vol 13 (9) ◽  
pp. 1760
Author(s):  
Ting Zhao ◽  
Giacomo Montereale Gavazzi ◽  
Srđan Lazendić ◽  
Yuxin Zhao ◽  
Aleksandra Pižurica
Keyword(s):  
Spatial Scales ◽  
Textural Feature ◽  
Separation Potential ◽  
Seafloor Mapping ◽  
Multibeam Echosounder ◽  
Weyl Transform ◽  
Class Separation ◽  
Sediment Classification ◽  
Seafloor Classification ◽  
Seafloor Sediment

The use of multibeam echosounder systems (MBES) for detailed seafloor mapping is increasing at a fast pace. Due to their design, enabling continuous high-density measurements and the coregistration of seafloor’s depth and reflectivity, MBES has become a fundamental instrument in the advancing field of acoustic seafloor classification (ASC). With these data becoming available, recent seafloor mapping research focuses on the interpretation of the hydroacoustic data and automated predictive modeling of seafloor composition. While a methodological consensus on which seafloor sediment classification algorithm and routine does not exist in the scientific community, it is expected that progress will occur through the refinement of each stage of the ASC pipeline: ranging from the data acquisition to the modeling phase. This research focuses on the stage of the feature extraction; the stage wherein the spatial variables used for the classification are, in this case, derived from the MBES backscatter data. This contribution explored the sediment classification potential of a textural feature based on the recently introduced Weyl transform of 300 kHz MBES backscatter imagery acquired over a nearshore study site in Belgian Waters. The goodness of the Weyl transform textural feature for seafloor sediment classification was assessed in terms of cluster separation of Folk’s sedimentological categories (4-class scheme). Class separation potential was quantified at multiple spatial scales by cluster silhouette coefficients. Weyl features derived from MBES backscatter data were found to exhibit superior thematic class separation compared to other well-established textural features, namely: (1) First-order Statistics, (2) Gray Level Co-occurrence Matrices (GLCM), (3) Wavelet Transform and (4) Local Binary Pattern (LBP). Finally, by employing a Random Forest (RF) categorical classifier, the value of the proposed textural feature for seafloor sediment mapping was confirmed in terms of global and by-class classification accuracies, highest for models based on the backscatter Weyl features. Further tests on different backscatter datasets and sediment classification schemes are required to further elucidate the use of the Weyl transform of MBES backscatter imagery in the context of seafloor mapping.

Seafloor mapping based on multibeam echosounder bathymetry and backscatter data using Object-Based Image Analysis: a case study from the Rewal site, the Southern Baltic

2018 ◽  
Vol 47 (3) ◽  
pp. 248-259 ◽  
Author(s):  
Łukasz Janowski ◽  
Jarosław Tęgowski ◽  
Jarosław Nowak
Keyword(s):  
Image Analysis ◽  
Large Scale ◽  
Fine Sand ◽  
Empirical Method ◽  
Angular Variable ◽  
Seafloor Mapping ◽  
Multibeam Echosounder ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Southern Baltic

Abstract Seafloor mapping is a fast developing multidisciplinary branch of oceanology that combines geophysics, geostatistics, sedimentology and ecology. One of its objectives is to isolate distinct seabed features in a repeatable, fast and objective way, taking into consideration multibeam echosounder (MBES) bathymetry and backscatter data. A large-scale acoustic survey was conducted by the Maritime Institute in Gdańsk in 2010 using Reson 8125 MBES. The dataset covered over 20 km2 of a shallow seabed area (depth of up to 22 m) in the Polish Exclusive Economic Zone within the Southern Baltic. Determination of sediments was possible based on ground-truth grab samples acquired during the MBES survey. Four classes of sediments were recognized as muddy sand, very fine sand, fine sand and clay. The backscatter mosaic created using the Angular Variable Gain (AVG) empirical method was the primary contribution to the image processing method used in this study. The use of the Object-Based Image Analysis (OBIA) and the Classification and Regression Trees (CART) classifier makes it possible to isolate the backscatter image with 87.5% overall and 81.0% Kappa accuracy. The obtained results confirm the possibility of creating reliable maps of the seafloor based on MBES measurements. Once developed, the OBIA workflow can be applied to other spatial and temporal scenes.

scholarly journals ASSESSMENT OF MULTIBEAM BACKSCATTER TEXTURE ANALYSIS FOR SEAFLOOR SEDIMENT CLASSIFICATION

2017 ◽  
Vol XLII-4/W5 ◽  
pp. 177-183 ◽  
Author(s):  
S. A. Samsudin ◽  
R. C. Hasan
Keyword(s):  
Spatial Resolution ◽  
Clustering Algorithm ◽  
High Spatial Resolution ◽  
Texture Classification ◽  
Classification Method ◽  
Sediment Type ◽  
Range Analysis ◽  
Sediment Classification ◽  
Sediment Types ◽  
Seafloor Sediment

Recently, there have been many debates to analyse backscatter data from multibeam echosounder system (MBES) for seafloor classifications. Among them, two common methods have been used lately for seafloor classification; (1) signal-based classification method which using Angular Range Analysis (ARA) and Image-based texture classification method which based on derived Grey Level Co-occurrence Matrices (GLCMs). Although ARA method could predict sediment types, its low spatial resolution limits its use with high spatial resolution dataset. Texture layers from GLCM on the other hand does not predict sediment types, but its high spatial resolution can be useful for image analysis. The objectives of this study are; (1) to investigate the correlation between MBES derived backscatter mosaic textures with seafloor sediment type derived from ARA method, and (2) to identify which GLCM texture layers have high similarities with sediment classification map derived from signal-based classification method. The study area was located at Tawau, covers an area of 4.7&amp;thinsp;km<sup>2</sup>, situated off the channel in the Celebes Sea between Nunukan Island and Sebatik Island, East Malaysia. First, GLCM layers were derived from backscatter mosaic while sediment types (i.e. sediment map with classes) was also constructed using ARA method. Secondly, Principal Component Analysis (PCA) was used determine which GLCM layers contribute most to the variance (i.e. important layers). Finally, K-Means clustering algorithm was applied to the important GLCM layers and the results were compared with classes from ARA. From the results, PCA has identified that GLCM layers of Correlation, Entropy, Contrast and Mean contributed to the 98.77&amp;thinsp;% of total variance. Among these layers, GLCM Mean showed a good agreement with sediment classes from ARA sediment map. This study has demonstrated different texture layers have different characterisation factors for sediment classification and proper analysis is needed before using these layers with any classification technique.

scholarly journals Measuring Centimeter-Scale Sand Ripples Using Multibeam Echosounder Backscatter Data from the Brown Bank Area of the Dutch Continental Shelf

Geosciences ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 495
Author(s):  
Leo Koop ◽  
Karin J. van der Reijden ◽  
Sebastiaan Mestdagh ◽  
Tom Ysebaert ◽  
Laura L. Govers ◽  
...  
Keyword(s):  
Flow Direction ◽  
Multibeam Bathymetry ◽  
Sediment Composition ◽  
Multibeam Echosounder ◽  
Acoustic Data ◽  
Sand Ripples ◽  
The North ◽  
The North Sea ◽  
Complete Transformation ◽  
Seafloor Sediment

Backscatter data from multibeam echosounders are commonly used to classify seafloor sediment composition. Previously, it was found that the survey azimuth affects backscatter when small organized seafloor structures, such as sand ripples, are present. These sand ripples are too small to be detected in the multibeam bathymetry. Here, we show that such azimuth effects are time dependent and are useful to examine the orientation of sand ripples in relation to the flow direction of the tide. To this end, multibeam echosounder data at four different frequencies were gathered from the area of the Brown Bank in the North Sea. The acoustic results were compared to video and tide-flow data for validation. The sand ripples affected the backscatter at all frequencies, but for the lowest frequencies the effect was spread over more beam angles. Using the acoustic data made it possible to deduce the orientations of the sand ripples over areas of multiple square kilometers. We found that the top centimeter(s) of the seafloor undergoes a complete transformation every six hours, as the orientation of the sand ripples changes with the changing tide. Our methodology allows for morphology change detection at larger scales and higher resolutions than previously achieved.

Acoustic seafloor sediment classification using self-organizing feature maps

2001 ◽  
Vol 39 (12) ◽  
pp. 2722-2725 ◽  
Author(s):  
B. Chakraborty ◽  
R. Kaustubha ◽  
A. Hegde ◽  
A. Pereira
Keyword(s):  
Feature Maps ◽  
Sediment Classification ◽  
Seafloor Sediment ◽  
Self Organizing

High spatial resolution x-ray microanalysis of interfaces

1995 ◽  
Vol 53 ◽  
pp. 284-285
Author(s):  
J. R. Michael
Keyword(s):  
Spatial Resolution ◽  
Electron Probe ◽  
Solid Angle ◽  
Collection Efficiency ◽  
Spatial Scales ◽  
Energy Dispersive Spectrometer ◽  
X Rays ◽  
X Ray ◽  
Probe Size ◽  
Brightness Field

X-ray microanalysis in the analytical electron microscope (AEM) refers to a technique by which chemical composition can be determined on spatial scales of less than 10 nm. There are many factors that influence the quality of x-ray microanalysis. The minimum probe size with sufficient current for microanalysis that can be generated determines the ultimate spatial resolution of each individual microanalysis. However, it is also necessary to collect efficiently the x-rays generated. Modern high brightness field emission gun equipped AEMs can now generate probes that are less than 1 nm in diameter with high probe currents. Improving the x-ray collection solid angle of the solid state energy dispersive spectrometer (EDS) results in more efficient collection of x-ray generated by the interaction of the electron probe with the specimen, thus reducing the minimum detectability limit. The combination of decreased interaction volume due to smaller electron probe size and the increased collection efficiency due to larger solid angle of x-ray collection should enhance our ability to study interfacial segregation.

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