Seafloor Classification Research Articles

Reply to “Comments on ‘Principal Component Analysis of Single-Beam Echo-Sounder Signal Features for Seafloor Classification’” by J. M. Preston and B. R. Biffard

In the paper published in the IEEE Journal of Oceanic Engineering, vol. 36, no. 2, pp. 259-271, Apr. 2011, results of applying a feature-based classification technique to single-beam echo-sounder data were presented. Comments on this contribution were presented in a correspondence paper published in the IEEE, Journal of Oceanic Engineering, vol. 37, no. 4, pp. 764-765, Oct. 2012). In this reply, the items addressed in the comments are reflected upon.

Comments on “Principal Component Analysis of Single-Beam Echo-Sounder Signal Features for Seafloor Classification” by A. R. Amiri-Simkooei, M. Snellen, and D. G. Simons

Acoustic seabed classification with single-beam sounders is both an established process and a research topic. A recent paper (IEEE Journal of Oceanic Engineering, vol. 36, no. 2, pp. 259-271, Apr. 2011) summarizes some popular techniques, demonstrating that they can generate realistic sediment maps. In the conclusion of that paper, it is claimed that the algorithms therein can substitute for a widely used commercial software suite. This claim is not correct. Among other issues, the techniques in that paper fall short of current practice in two areas: compensation for varying water depths and choosing the best number of clusters in unsupervised classification.

High-frequency multibeam echo-sounder measurements of seafloor backscatter in shallow water: Part 1 – Data acquisition and processing

Abstract Methods for processing multibeam echo-sounder (MBES) bathymetry data are well established, how-ever, there is no universal and established approach to processing and interpretation of MBES backscatter data. The aim of this paper is to (1) detail the different backscatter logging methods implemented in modern high-frequency MBES systems used primarily in shallow water; (2) describe and suggest appropriate methods of data processing; and (3) discuss likely errors and artefacts expected from MBES backscatter processing. These issues are illustrated using data collected with a Reson SeaBat MBES in coastal shelf areas of Australia, although the methods and errors discussed are relevant to other high-frequency shallow-water MBES systems. Production of backscatter mosaics and analysis of backscatter for seafloor classification using MBES data are considered in Part 2 – Mosaic production, analysis and classification (Parnum and Gavrilov, 2011), published alongside this paper.

Principal Component Analysis of Single-Beam Echo-Sounder Signal Features for Seafloor Classification

Acoustic remote sensing is a useful tool for seafloor characterization. This contribution presents the results of seafloor sediment classification using single-beam echo-sounder (SBES) data based on a phenomenological method. Basic concepts of principal component analysis (PCA) and its applicability to the sediment classification using acoustical data are studied. This mathematical tool, which retains most of the variation of the data, is applied to the SBES echo shape parameters such as total energy, time-spread, skewness and flatness on three low (12 kHz), moderate (38 kHz), and high (200 kHz) frequencies, making 12 features in total. These parameters are dependent on sediment types and can therefore be used as attributes for seafloor classification. To decrease the statistical fluctuations of the extracted features, an averaging over a sufficiently large number of consecutive pings have been applied to the features. The SBES classification results based on the PCA and K-means clustering approach can clearly discriminate between different sediment classes. The signal at 12 kHz contains information on sediment layers (5 m depth). The performance of the method and the results obtained are assessed using the following independent criteria: 1) inspection of the track crossings indicates stable feature extraction and processing strategy; 2) comparison between the class numbers of the classification results and of the grab samples shows a significant correlation coefficient of 0.90; and 3) an error matrix verifies the stability and independence of the classification results from the features considered.

Seafloor Deposition State Based Geoacoustic Model of the South China Sea

Considered the complexity of the actual seabottom state due to characteristics of layers, diverse temperature and pressure in the South China Sea, sediments are sorted into porosity-unchanged type (PDC) and porosity-decreased type (PDD). By stress-strain analysis of two-phase medium of sediment under gravity, the establishment of a wave propagation model considered depth variety (DFCMCM) theoretically reveals the acoustic properties that with depth PDC type sediments change slowly, while PDD type sediments increase in compressional wave velocity. A novel geoacoustic model expression concerned about seabottom sedimentary state is introduced and four representative basic geoacoustic models of sediments in the South China Sea to be expressed as: low-speed PDC (GMI1), low-speed PDD (GMI2), high-speed PDC (GMII1) and high-speed PDD (GMII2). By using the combination of the four basic models, the actual sedimentary acoustic state could be rationally explained with the example of the Lu's three sound-speed structures of the South China Sea. The understanding of basic geoacousic models will benefit the seafloor detection and classification of bottom region with acoustic means in the South China Sea.

Detection of sub-tidal mussel beds on rocky reefs using acoustics

The collection of recently settled wild mussels from beach-cast seaweed provides the seed for a large and rapidly expanding mussel aquaculture industry in New Zealand, but the locations of adult populations producing this seed are unknown. Traditional methods of assessing sub-tidal habitats of rocky reefs (diver census, video camera drops) are neither cost-effective nor practical for the relatively large spatial scales involved. Remote sensing tools offer solutions to this problem. We tested the ability of the acoustic seafloor classification system, Quester Tangent Corporation (QTC) Impact, to detect the presence of green-lipped mussel (Perna canaliculus) populations on sub-tidal rocky reefs. Acoustic data were collected from a range of visual subtidal habitats, combined, and analysed using QTC Impact's principal component analysis and clustering software. Discriminant function analysis assessed whether visually distinctive habitats could be correctly classified using this acoustic data only. Consistent and reliable classifications were found for continuous mussel beds (100% correct, 0% misclassification from other habitat types), and sand, while the other various classes of rocky reef habitats (i.e. reef with varying levels of biological cover of kelps and encrusting invertebrates, including occasional mussel patches at one site) were identifiable to a general class of rocky reef, without significant mussel cover. These results demonstrate that QTC Impact can reliably discriminate subtidal mussel beds on rocky reefs from other habitats, making it feasible to proceed with large-scale mapping.

Predicting Spatial Variability of Sediment Properties From Hydrographic Data for Geoacoustic Inversion

Seafloor classification using acoustic remote sensing techniques is an attractive approach due to its high coverage capabilities and limited costs compared to taking samples of the seafloor. This paper focuses on the characterization of sediments in a coastal environment by combining different hydrographic systems, which are a multibeam echosounder (MBES), a single-beam echosounder (SBES), and seismic systems. The area is located close to the west coast of Italy, southeast of Elba Island, which is known to be composed of very fine-grained material. Both MBES and SBES are, in general, high-frequency systems (100 kHz), providing bathymetry and backscatter information of the upper part of the sea bottom. MBES systems provide this information with a high resolution, due to the beam opening angle of typically 1 -3 , and high coverage. An SBES provides measurements directly underneath the ship only, but is widespread. For the classification by means of MBES data, we use the Bayesian approach, employing backscatter measurements per beam. For the SBES, echo shape parameters are determined and are combined in a principal component analysis (PCA). Both approaches give results that are in very good agreement with respect to the distribution of different surficial sediment types. Complementary, low-frequency seismic systems ( 20 kHz) give insight into the sediment layering. Combining the different acoustic approaches is shown to be an essential ingredient for establishing the environmental picture. This picture is of use for a large range of applications, such as habitat mapping, cable laying, or mine hunting. For the current research, it is aimed to act as a basis for selecting areas for subseafloor sediment classification by geoacoustic inversion techniques. Contrary to the hydrographic systems, geoacoustic inversion techniques provide the actual physical properties, i.e., densities, compression and shear wave speeds, and respective attenuations of the sediment body, and allow sediment characterization over large areas without the need to cover the complete area. A validation is given that the environmental picture, obtained by the hydrographic systems, indeed identifies regions with different acoustic properties.

Morphological characteristics of the Basque continental shelf (Bay of Biscay, northern Spain); their implications for Integrated Coastal Zone Management

This contribution integrates and analyzes data from high-resolution multibeam, seismic profiles, bathymetric LiDAR, and surficial sediment data for the geomorphological seascapes characterisation and process-description of the Basque inner and middle continental shelf (northern Spain). From the data obtained, the Basque shelf is characterised by a heterogeneous seafloor where, on a small spatial scale, different morphologies and sedimentary processes can be observed. Tectonic activity and sea-level changes, together with present processes of sediment supply and climatic conditions, have a critical influence on the present configuration of the continental shelf and the distribution of seafloor types. On the basis of all of the datasets, seafloor classification, bedform analysis, long-term sea-level change-induced seafloor features and anthropogenic features over the seafloor, are described. As a result, three distinct zones have been identified for the Basque shelf, related to the main geomorphological features and seascapes, as summarised below. (i) A western part, with a northwestern orientation, which coincides with the prevailing wave direction. This zone is characterised by a predominantly rocky substrate, except within the mouth of the Nervión estuary, where sandy sediments appear to infill a paleo-channel. (ii) A central part, which is oriented towards the northeast and receives less wave energy. Within this zone, a sedimentary seabed is predominant. Sorted bedforms are well developed and can be detected in water depths in excess of 90 m. (iii) An eastern zone, which is characterised by sedimentary seafloor and shore terraces; here, a flat rocky seafloor is covered by a thin layer of sediments. Throughout the study area, the bathymetry and sedimentary features of the shoreface and inner shelf are controlled mainly by climatic conditions, coastal dynamics and the underlying geological framework. The integration of these data sets, together with their interpretation, is a valuable source of information for Integrated Coastal Zone Management (ICZM) and constitutes a useful tool for implementing various European Directives.

Fuzzy clustering for seafloor classification

In order to develop quantitative seafloor sediment classification techniques it is important to acknowledge that by nature the boundaries between soft sediments are characterized by transition zones and therefore are indeterminate and gradual. A fuzzy clustering method, fuzzy c-means (FCM), was used to identify these transition zones within a subset of the data used to generate the Australian Seascapes classification model. The overlapping classes and gradual boundaries resulting from the fuzzy c-means algorithm provided estimates of sediment boundaries that are a closer model of reality than sharp boundaries. FCM output is given in the form of membership layers for each class, hard classes for each grid cell based on the maximum membership value, and a confusion index layer quantifying uncertainty in class attribution. The confusion index layer provided a spatial representation of transition zones and overlap between seafloor classes and highlighted areas of greatest uncertainty. We extended the standard FCM algorithm by applying the new FMLE fuzzy clustering algorithm that takes into account spatial relationships in the data. In addition, we implemented and applied new cluster validity techniques, PCAES, PBMF, and XB to determine the optimal number of clusters in the data, which is a novel pattern recognition application for seabed mapping. The 5-class FCM classification provided the most reliable result. The results of this research were tested and validated on a simulated dataset and then the clustering and validation algorithms were applied to marine sediment data to identify Seascapes. The new results were compared with previously published Seascapes classes identified with hard ISODATA clustering techniques from GeoScience Australia's Seascapes classification result. With the increasing use of physical surrogates to explain marine biodiversity, this research plays a crucial role in the development of techniques to identify habitat zones on the seabed.

A Bayesian approach to seafloor classification using multi-beam echo-sounder backscatter data

Seafloor classification using acoustic remote sensing techniques is an attractive approach due to its high-coverage capabilities and limited costs. The multi-beam echo-sounder (MBES) system provides high-resolution bathymetry and backscatter information with 100% coverage. In this paper, we present a seafloor classification method that employs the MBES backscatter data. The method uses the averaged backscatter data per beam. It, therefore, is independent on the quality of the MBES calibration. Also, its performance is insensitive to seafloor type variation along the MBES swathe and corrections for the angular dependence of the backscatter are not needed. The method accounts for the ping-to-ping variability of the backscatter intensity. It estimates both the number of seafloor types present in the survey area and the probability density function for the backscatter strength at a certain angle for each of the seafloor types. Application of the method to MBES backscatter data acquired in a well-known test area in the North Sea shows very good agreement with available ground truth. The method’s discriminatory performance for this area is demonstrated to be comparable to that of taking samples of the sediment. All seafloor types known to be present in the area are resolved for. Application of the method to the Stanton bank data set shows clearly separable areas that differ in seafloor composition.

High frequency scattering measurements for mussel bed characterisation

Several approaches exist towards seafloor classification using high-frequency backscattering measurements. The classification approach taken in this paper is a model-based classification employing backscatter data measured by a multibeam-echosounder (MBES) system. The method discriminates between sediments in the most optimal way by applying the Bayes decision rule for multiple hypotheses, implicitly accounting for the backscatter strength ping-to-ping variability. The method's applicability for seafloor classification has been demonstrated by using 300 kHz MBES data collected in the Cleaver-Bank area (North Sea). The area is well-known from a geological point of view due to extensive sampling campaigns and is characterized by a wide variety of seafloor types. Here we apply the classification method to MBES data acquired in the Oosterschelde estuary (the Netherlands) which is known to contain mussel culture spots. Also recordings using a video camera towed close to the seafloor and core measurements have been taken. From the video recordings estimates of mussel coverage as a function of position were derived. Analysis of the MBES results shows that they clearly reveal the presence of the mussel beds, indicating the usefulness of acoustic classification for habitat mapping. A comparison between MBES analysis results, video recordings and sample analysis will be presented.

Application of LVQ Neural Network Combined with the Genetic Algorithm in Acoustic Seafloor Classification

AbstractThe Learning Vector Quantization (LVQ) Neural Network approach has been widely used in acoustic seafloor classification. However, one of the major weak points of LVQ is its sensitivity to the initialization, affecting the seafloor classification accuracy. In this paper, Genetic Algorithm (GA) is used to optimize the initial values of LVQ. The GA‐based LVQ can rapidly provide the most optimized initial reference vectors and accurately identify many types of seafloor, such as rock, gravel, sand, fine sand and mud in the surveyed areas. The proposed new approach has been applied to seafloor classification using Multibeam Echo Sounder (MBES) backscatter data in Jiaozhou Bay near Qingdao City of China. Comparing the evolving LVQ with the standard LVQ, the experiment results indicate that the GA‐based LVQ approach has improved the seafloor classification speed and accuracy.

Sedimentology and Acoustic Mapping of Modern Rhodolith Facies on a Non-Tropical Carbonate Shelf (Gulf of California, Mexico)

Abstract Rhodolith facies are characterized by an abundance of free-living coralline red algae, and are a common feature of modern and ancient carbonate shelves worldwide. Rhodolith communities contribute significantly to the global calcium carbonate budget, and fossil rhodoliths are commonly used to obtain paleoecologic and paleoclimatic information. Few attempts have been made to quantify the spatial extent of modern rhodolith facies, despite their importance and common occurrence. Combining sediment sampling with acoustic facies mapping, this study provides the first set of quantitative data on rhodolith facies distribution in the warm-temperate southwestern Gulf of California, Mexico. Though rhodoliths were the main carbonate producers, other important calcareous biota were bivalves (19%), bryozoans (13%), and corals (6%). Based on cluster analysis, biota were grouped into a rhodolith, a rhodolith–bivalve–coral, and a bivalve–bryozoa biofacies. The acoustic seafloor classification distinguished four acoustic facies, which were confirmed by ground-truthing: an outer-shelf silt facies, a biogenic sand facies, a peripheral rhodolith facies, and a central rhodolith facies. Rhodolith facies covered approximately 40% of the surveyed seafloor (45 km2). Results from quantitative analysis of sediment samples and acoustic mapping showed a significant correlation between grain size, biofacies, and acoustic seafloor facies. We successfully applied an acoustic device to provide highly resolved continuous coverage of the seafloor and discriminate modern rhodolith facies from surrounding sediment. This has important implications for quantifying rhodolith carbonate production in other regions, as well as for ecological and conservation studies.

Seafloor classification of the mound and channel provinces of the Porcupine Seabight: an application of the multibeam angular backscatter data

In this study multibeam angular backscatter data acquired in the eastern slope of the Porcupine Seabight are analysed. Processing of the angular backscatter data using the ‘NRGCOR’ software was made for 29 locations comprising different geological provinces like: carbonate mounds, buried mounds, seafloor channels, and inter-channel areas. A detailed methodology is developed to produce a map of angle-invariant (normalized) backscatter data by correcting the local angular backscatter values. The present paper involves detailed processing steps and related technical aspects of the normalization approach. The presented angle-invariant backscatter map possesses 12 dB dynamic range in terms of grey scale. A clear distinction is seen between the mound dominated northern area (Belgica province) and the Gollum channel seafloor at the southern end of the site. Qualitative analyses of the calculated mean backscatter values i.e., grey scale levels, utilizing angle-invariant backscatter data generally indicate backscatter values are highest (lighter grey scale) in the mound areas followed by buried mounds. The backscatter values are lowest in the inter-channel areas (lowest grey scale level). Moderate backscatter values (medium grey level) are observed from the Gollum and Kings channel data, and significant variability within the channel seafloor provinces. The segmentation of the channel seafloor provinces are made based on the computed grey scale levels for further analyses based on the angular backscatter strength. Three major parameters are utilized to classify four different seafloor provinces of the Porcupine Seabight by employing a semi-empirical method to analyse multibeam angular backscatter data. The predicted backscatter response which has been computed at 20° is the highest for the mound areas. The coefficient of variation (CV) of the mean backscatter response is also the highest for the mound areas. Interestingly, the slope value of the buried mound areas are found to be the highest. However, the channel seafloor of moderate backscatter response presents the lowest slope and CV values. A critical examination of the inter-channel areas indicates less variability within the estimated three parameters. Financial support of this study was granted by the European Commission Fifth Framework Project GEOMOUND (contract no. EVK3-CT-1999-00016).

Distribution and seasonal biomass of drift macroalgae in the Indian River Lagoon (Florida, USA) estimated with acoustic seafloor classification (QTCView, Echo plus)

Three areas of the Indian River Lagoon, Florida (USA) were surveyed to show seasonal changes in the distribution and biomass of macroalgae and seagrass. Acoustic seafloor discrimination based on first and second echo returns of a 50 kHz and 200 kHz signal, and two different survey systems (QTCView and ECHO plus) were used. System verification in both the field and a controlled environment showed it was possible to distinguish acoustically between seagrass, sparse algae, and dense algae. Accuracy of distinction of three classes (algae, seagrass, bare substratum) was around 60%. Maps were produced by regridding the survey area to a regular grid and using a nearest-neighbor interpolation to provide filled polygons. Biomass was calculated by counting pixels assigned to substratum classes with known wet-weight biomass values (sparse algae 250 g m − 2 , dense algae 2000 g m − 2 , seagrass 100 g m − 2 ) that were measured in the field. In three study areas (Melbourne, Sebastian Inlet, and Cocoa Beach), a dependence of algal biomass on depth and season was observed. Seagrass most frequently occurred in water less than 1 m deep, and in November, seagrass beds tended to be covered by dense algae that also extended up- and downstream of shoals in the Lagoon. In March, the pattern was similar, with the exception that some areas of previously dense algae had started thinning into sparse algae. Macrophyte biomass was lowest in May in the Melbourne and Cocoa Beach study areas, with the opposite situation in the Sebastian Inlet study area. In May, seagrass areas were largely devoid of dense algae and most algae accumulations were sparse. In August, dense algae covered large areas of the deep Lagoon floor while shoals were largely free of algae or had only sparse cover. We suggest this summer pattern to reflect moribund algae being washed from the shallows to deeper channels and from there being removed from the lagoonal ecosystem either through tidal passages into the open ocean or by degradation and breakdown in situ. The differences between the study areas indicate high spatial and temporal variability in biomass and distribution of macrophyte biomass in the Indian River Lagoon.

Processing Multibeam Backscatter Data

A new highly precise source of data has recently become available using multibeam sonar systems in hydrography. Multibeam sonar systems can provide hydrographic quality depth data as well as high-resolution seafloor sonar images. We utilize the seafloor backscatter strength data of each beam from multibeam sonar and the automatic classification technology so that we can get the seafloor type identification maps. In this article, analyzing all kinds of error effects in backscatter strength, data are based on the relationship between backscatter strength and seafloor types. We emphasize particularly analyzing the influences of local bottom slope and near nadir reflection in backscatter strength data. We also give the correction algorithms and results of these two influent factors. After processing the raw backscatter strength data and correcting error effects, we can get processed backscatter strength data which reflect the features of seafloor types only. Applying the processed backscatter strength data and mosaicked seafloor sonar images, we engage in seafloor classification and geomorphy interpretation in future research.

Seafloor classification using artificial neural network architecture from central western continental shelf of India

Seafloor classification studies are carried out at the central western continental shelf of India employing two frequency normal incidence single beam echo-sounder backscatter data. Echo waveform data from different seafloor sediment areas are utilized for present study. Three artificial neural network (ANN) architectures, e.g., Self-Organization Feature Maps (SOFM), Multi-Layer Perceptron (MLP), and Learning Vector Quantization (LVQ) are applied for seafloor classifications. In case of MLP, features are extracted from the received echo signal, on the basis of which, classification is carried out. In the case of the SOFM, a simple moving average echo waveform pre-processing technique is found to yield excellent classification results. Finally, LVQ, which is known as ANN of hybrid architecture is found to be the efficient seafloor classifier especially from the point of view of the real-time application. The simultaneously acquired sediment sample, multi-beam bathymetry and side scan sonar and echo waveform based seafloor classifications results are indicative of the depositional (inner shelf), non-depositional or erosion (outer shelf) environment and combination of both in the transition zone. [Work supported by DIT.]

Seafloor Classification of Multibeam Sonar Data Using Neural Network Approach

In this study, the self-organizing map (SOM), which is an unsupervised clustering algorithm, and a supervised proportional learning vector quantization (PLVQ), are employed to develop a combined method of seafloor classification using multibeam sonar backscatter data. The PLVQ is a generalized learning vector quantization based on the proportional learning law (PLL). The proposed method was evaluated in an area where there are four types of sediments. The results show that the performance of the proposed method is better than the SOM and a statistical classification method.

Detection of shallow subtidal corals from IKONOS satellite and QTC View (50, 200 kHz) single-beam sonar data (Arabian Gulf; Dubai, UAE)

We compared the results of seafloor classifications with special emphasis on detecting coral versus non-coral areas that were obtained from a 4×4-m pixel-resolution multispectral IKONOS satellite image and two acoustic surveys using a QTC View Series 5 system on 50 and 200 kHz signal frequency. A detailed radiative transfer model was obtained by in situ measurement of optical parameters that then allowed calibration of the IKONOS image against in situ optical measurements and a series of ground-truthing points. Eight benthic classes were distinguished optically with an overall accuracy of 69% and a Tau index T of 65. The classification of the IKONOS image allowed discrimination of three different coral assemblages (dense live, dense dead, sparse), which were confirmed by ground-truthing. Data evaluation of the acoustic surveys involved culling of datapoints with <90% confidence and <30% probability, two QTC-provided statistics, and the deletion of data classes without clear spatial patterns (visualized by single-class trackplots). The deletion of these ubiquitous classes was necessary in order to obtain any clearly interpretable spatial pattern of echo classes after the surveys were resampled to a regular grid and areas between the lines interpolated using a nearest neighbor algorithm. The 50 kHz acoustic seafloor classification was able to determine two classes (unconsolidated sand versus hardground) but was not able to determine corals. The 200 kHz survey determined high rugosity (=corals and sand ripples) versus low rugosity (=flat areas) but was not able to determine consolidated and unconsolidated sediments. Classes were extrapolated to the entire grid and polygons obtained from the two surveys were combined to provide maps containing four classes (rugose hardground=coral, flat hardground=rock, rugose softground=ripples and algae, flat softground=bare sand). Compared with the classification map derived from the IKONOS image, they were 66% accurate ( T=59) when the most highly processed data (only selected classes, >90% accuracy and >30% probability) were used, and 60% accurate ( T=53) when less processed data (selcted classes only, all data) were used. Accuracy against ground-truthing points of the most highly processed dataset was 56% ( T=46). These results indicate that results from optical and acoustic surveys have some degree of commonality. Therefore, there is a potential to produce maps outlining coral areas from optical remote-sensing in shallow areas and acoustic methods in adjacent deeper areas beyond optical resolution with the limitation that acoustic maps will resolve fewer habitat classes and have lower accuracy.

Seafloor Classification Using Echo-Waveforms: A Method Employing Hybrid Neural Network Architecture

This letter presents seafloor classification study results of a hybrid artificial neural network architecture known as learning vector quantization. Single beam echo-sounding backscatter waveform data from three different seafloors of the western continental shelf of India are utilized. In this letter, an analysis is presented to establish the hybrid network as an efficient alternative for real-time seafloor classification of the acoustic backscatter data.