The Hyper-Angular Cube Concept for Improving the Spatial and Acoustic Resolution of MBES Backscatter Angular Response Analysis

Abstract

This study presents a novel approach, based on high-dimensionality hydro-acoustic data, for improving the performance of angular response analysis (ARA) on multibeam backscatter data in terms of acoustic class separation and spatial resolution. This approach is based on the hyper-angular cube (HAC) data structure which offers the possibility to extract one angular response from each cell of the cube. The HAC consists of a finite number of backscatter layers, each representing backscatter values corresponding to single-incidence angle ensonifications. The construction of the HAC layers can be achieved either by interpolating dense soundings from highly overlapping multibeam echo-sounder (MBES) surveys (interpolated HAC, iHAC) or by producing several backscatter mosaics, each being normalized at a different incidence angle (synthetic HAC, sHAC). The latter approach can be applied to multibeam data with standard overlap, thus minimizing the cost for data acquisition. The sHAC is as efficient as the iHAC produced by actual soundings, providing distinct angular responses for each seafloor type. The HAC data structure increases acoustic class separability between different acoustic features. Moreover, the results of angular response analysis are applied on a fine spatial scale (cell dimensions) offering more detailed acoustic maps of the seafloor. Considering that angular information is expressed through high-dimensional backscatter layers, we further applied three machine learning algorithms (random forest, support vector machine, and artificial neural network) and one pattern recognition method (sum of absolute differences) for supervised classification of the HAC, using a limited amount of ground truth data (one sample per seafloor type). Results from supervised classification were compared with results from an unsupervised method for inter-comparison of the supervised algorithms. It was found that all algorithms (regarding both the iHAC and the sHAC) produced very similar results with good agreement (>0.5 kappa) with the unsupervised classification. Only the artificial neural network required the total amount of ground truth data for producing comparable results with the remaining algorithms.

Multi-Frequency, Multi-Sonar Mapping of Shallow Habitats—Efficacy and Management Implications in the National Marine Park of Zakynthos, Greece

“…It involves similar pixels being grouped into connected segments (objects), classified using some intrinsic information. It has mostly been applied to MBES data [12,15,16,18,56,67,68], where the objects are extracted through segmentation of MBES backscatter mosaics and the available information (bathymetric, backscatter and angular backscatter responses or acoustic modeling parameters) is averaged within each segment. Segmentation practices are also used in SSS analyses using backscatter information for habitat mapping [38,47], and more recently for SBP data [47].…”

Limitations of Predicting Substrate Classes on a Sedimentary Complex but Morphologically Simple Seabed

“…Promising new possibilities at various stages of the classification process are currently being developed. These include developments of remote sensing technology and improvements of backscatter processing approaches, including the merging of uncalibrated backscatter mosaics [101], calibrated backscatter [102], multispectral backscatter [103], ARA [104] and HAC [21], acquiring more representative samples by applying rigorous sampling designs [105,106] and the derivation of novel features from the primary MBES data with increased discriminatory power for sediment classification [107,108]. We expect progress to mainly occur through refinement at each stage of the mapping process as discussed above.…”

“…Refining the relationship between frequency, seafloor type (including porosity, compactness and permeability) and backscatter response is continually being improved [14,16,63,65,69,70]. Further developments to better utilise backscatter data such as angular response analysis (ARA) [71][72][73] and the hyper-angular cube (HAC) [21,74] may also increase the predictive power of acoustic data [75]. The recent development of MBES systems that can acquire multi-frequency backscatter simultaneously also has the potential to improve mapped accuracy [29,76].…”

“…Review papers were presented dealing with acoustic seabed classification [16], benthic habitat mapping [17], spatial scale and geographic context in benthic habitat mapping [18], marine geomorphometry [19] and the transfer of knowledge from terrestrial mapping to image-based seabed classification [20]. A sizeable amount of published studies compared predominantly supervised classification approaches in their ability to map seabed substrates and habitats [21][22][23][24][25][26][27][28]. Such studies typically use a measure of map accuracy as a metric to compare the different methods in their ability to derive reliable seabed maps.…”

Detection of Boulders in Side Scan Sonar Mosaics by a Neural Network

“…A reliable direct classification of cobbles and fine boulders will need higher resolution input data, which can be difficult for large areas of interest. If covering a large percentage of seafloor, the presence of cobbles and fine boulders may be detected indirectly, e.g., by utilizing angular response curves [35,36] and multifrequency backscatter approaches currently under active development [37][38][39][40]. Eventually, the density of medium and large boulders may be used as a proxy for the overall density of hard substrata available for biota and consequently for the identification of reefs sensu habitats directive (code 1170).…”