A Comparison of Overlapping Data from Bathymetric and Fisheries Multibeam Sonars
David Maggio
Ian Church
Chris McHugh
Min Kim
Hydrographic Science Research Center
University of Southern Mississippi
David Maggio – Graduate Research Assistant
Hydrographic Science Research Center
University of Southern Mississippi
1020 Balch Blvd
Stennis Space Center, MS 39529
E-mail: david.maggio@eagles.usm.edu
Abstract
Although the main purpose of fisheries multibeam sonar is to extract data from the water column, many systems are capable of extracting seafloor soundings and backscatter as well. The differences in the principals of operation between fisheries and traditional bathymetric multibeam sonar make it interesting to compare the data from each system. This project seeks first to compare and contrast the theory of operation between fisheries and bathymetric multibeam sonar. A relative comparison of seafloor backscatter and bathymetry data acquired from each type of sonar is then performed using data collected from an overlapping area near the Pulley Ridge in the West Florida Shelf. The fisheries multibeam data used in this comparison was obtained from a Simrad ME70 aboard the
R/V Pisces operated by NOAA. The bathymetric multibeam data was obtained from a Kongsberg
EM 3002 aboard the R/V Pelican operated by LUMCON.
Introduction
Sonars are commonly used in fisheries research for estimates of biomass, school size, species identification, and avoidance responses (Lurton, 2010). Traditionally, single beam echosounders and panoramic-scanning sonars are used for this purpose. Many bathymetric multibeam sonars are also capable of recording volumetric scattering from objects in the water column, and there have been several fisheries studies that utilized bathymetric multibeam systems (Soria, 1996; Gerlotto,
1999; Mayer, 2002; Paramo, 2006). However, effectiveness of bathymetric multibeam sonars for fisheries research is limited by several factors. The spherical volume, an area of the water column close to the seafloor, is commonly contaminated by high levels of noise in traditional multibeams.
The noise is caused by reverberation from side lobes reflected from the seabed at nadir, which can easily mask volumetric scattering from targets in the water column. The size of the spherical volume increases for beams farther from nadir, making large portions of the water column data in the outer beams unusable.
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The growing interest in this technology has led to the creation of multibeam systems designed for fisheries research. The first such system was the Simrad ME70, first installed in 2005 on the FRV
Thalassa (Trenkel, 2008). The ME70 transducer is a 677mm cylindrical array of 800 elements.
There are numerous properties that make the ME70 ideal for fisheries applications, and thus very different from a traditional bathymetric multibeam. One such property is the highly configurable beam pattern. A total of 3-45 individual beams can be created with a center beamwidth of 2.2-20 degrees (Simrad, 2008). These are wider beams compared to the 0.5-1.5 degree beamwidth of many bathymetric multibeams. The ME70 also allows different frequencies to be selected for each beam, between 70 and 120 kHz. This is especially useful for distinguishing between different species of marine organisms (Lurton, 2010). It is also possible to split beams into groups of 1-4, which is useful for target strength analysis and target tracking (Simrad, 2008). Swath angles may be adjusted for optimal sidelobe reduction, which can reduce the sidelobes to -70dB two-way suppression when combined with a split beam configuration. This creates a significant improvement over bathymetric multibeams by largely reducing the unusable spherical volume.
There are many other ways to adjust the configuration of the ME70, and a more detailed discussion can be found in the literature (Trenkel, 2008).
The ME70 was designed for fisheries research purposes, and is therefore not able to perform bottom detections in fisheries mode. However, Simrad has developed an optional bathymetric mode that utilizes a bottom detection algorithm. It is also possible to extract bottom detections and seabed intensity returns while operating in fisheries mode via independent algorithms. Studies have been conducted with the ME70 demonstrating its ability to perform bathymetric mapping
(Bourguignon, 2009; Cutter, 2010) and its successful use in performing bottom classification
(Weber, 2012).
The differences in the beam pattern characteristics between the ME70 and a bathymetric multibeam makes it worthwhile to examine how the bathymetry and backscatter data acquired from each system compare. This paper seeks to examine bathymetry and backscatter data with overlapping coverage from a Simrad ME70 fisheries multibeam and a Kongsberg EM3002 shallow water bathymetric multibeam. A swath surface was generated from each system and a difference surface was taken between them. The ability of each multibeam to detect small targets was assessed using these surfaces. Mosaics were generated from the backscatter for each system. Grey level cooccurrence matrices (GLCM’s) were then constructed over sections of the mosaic containing interesting features. Statistics were derived from these mosaics and principle component analysis was conducted to determine the statistics best describing the image texture.
Methods
The data used in this study was acquired on the Pulley Ridge, a mesophotic coral reef on the West
Florida Shelf in the Gulf of Mexico (Figure 1). Two sets of bathymetric data that overlapped in this area were utilized. The first was acquired in 2013 using a Simrad ME70 fisheries multibeam on the NOAA Ship Pisces . The second was acquired with a Kongsberg EM3002 shallow water bathymetric multibeam during a 2014 research cruise on the R/V Pelican , operated by the
Louisiana Universities Marine Consortium (LUMCON).
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Figure 1: The survey area on the Pulley Ridge.
The ME70 data was acquired while operating in fisheries mode. Therefore, the data was run through an algorithm developed by Tom Weber at UNH to calculate bottom detections from the raw data and to convert the files to .gsf format. From here, the ME70 and EM3002 data followed a similar processing pipeline. The data were brought into CARIS HIPS software for processing and cleaning. Swath surfaces gridded to 5x5m and 2x2m were created for each dataset. A difference surface was then generated from the 5x5m surfaces for the overlapping area. Difference surfaces were also generated for two subsets of the overlapping surface, one over a bathymetric feature and another over a flat surface. This allows for an examination of how changes in bathymetry effect the difference surface, which will aid in comparing the ability of each sonar to resolve changes in bathymetry. Statistics and histograms were then calculated for the difference surfaces to determine the spread, mean, and standard deviation of the difference. The ability of the
ME70 and the EM3002 to resolve small targets was assessed by a search for features <3m in their longest dimension that appeared in both 2x2m surfaces.
To process the backscatter imagery, the data was exported from CARIS HIPS to .gsf files, then imported into Hypack’s Geocoder implementation. Backscatter mosaics were generated for the entirety of each dataset at a 1x1m grid with corrections for TX power RX gain, area correction, and spherical spreading. Despeckling was automatically applied upon mosaicking. The mosaics were then exported as geotiffs. Each geotiff was then manually inspected to identify areas with interesting features. The data were then re-mosaicked and re-exported to geotiffs using the bounding coordinates of each selected area, creating a pair of geotiffs for each area.
In order to compare the backscatter imagery obtained from each multibeam system, a method of textual analysis using second order statistics derived from Grey Level Co-occurrence Matrices
(GLCM’s) was selected. GLCM’s describe the frequencies with which all possible combinations of grey levels between pairs of pixels occur in a specified direction and subset of the image. Grey level co-occurrence statistics have been utilized in many studies to determine patterns in intensity
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imagery and have been applied to multibeam backscatter and side scan sonar intensity data (Reed,
1989; Diaz, 2000; Blondel, 2009). In this study, GLCM’s are used to make a comparison of the second order statistics that best describe the texture of the backscatter between the overlapping images from each multibeam. This analysis will provide an estimate of the ability of the multibeam sonars to detect similar textures from an area of seabed.
The geotiffs were imported into Matlab and quantized to eight grey levels to reduce noise and obtain a statistically reliable estimate of the probability distribution (Diaz, 2000; Albregtsen,
2008). The quantized images were used to calculate GLCM’s of the image by utilizing Matlab’s image processing toolbox. The GLCM’s were created using a 25x25 pixel window, a pixel step size of 1 (the distance separating a pair of pixels), and were calculated for 0, 45, 90, and 135 degree directions. These were then made symmetric and averaged into directionally invariant GLCM’s and normalized to show the probabilities rather than number of occurrences.
A set of second order statistics was then calculated from the normalized averaged GLCM’s
(Uppuluri, 2008). These statistics seek to describe the texture of the GLCM’s and may be categorized into statistical, contrast, and orderliness groups (Hall-Beyer, 2007). The contrast group describes the local variability or similarity between pixels and the orderliness group describes how regular (orderly) or irregular (disorderly) the GLCM pixels are. The statistics calculated for this study, based on recommendations from the literature, were GLCM variance and correlation from the statistical group, homogeneity (also called inverse difference moment) from the contrast group, and entropy from the orderliness group (Hall-Beyer, 2007). Useful information about the texture can be derived from the value of these statistics. For example, high entropy implies a rough and irregular texture and low entropy implies a smooth regular texture like ripples (Blondel, 2009).
More details on these values are provided in the table below.
Statistic Group Measure Equation
GLCM
Variance
Statistical
Dispersion of the GLCM pixels about the mean
Correlation Statistical
The linear dependence of pixels on their neighbors
Homogeneity Contrast
The similarity of pixels with their neighbors
Entropy Orderliness
Disorder or irregularity of the pixels
Table 1: Texture features calculated on the GLCM’s.
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Principal Component Analysis (PCA) was then conducted on the set of statistics for each image.
The first benefit of PCA is that is shows which statistics account for the most variability in the
GLCM. The second benefit of PCA is reducing the dimensionality of the dataset, thus removing statistics that show redundant textural information (Gomez, 2012). Because the redundant statistics do not have a significant impact on classification, the identified principle components are the controlling factors describing the image texture. Prior to performing principal component analysis, the statistics were scaled to their z-scores to prevent the component’s dynamic range from impacting the analysis (Hall-Beyer, 2007). The principle components of each image were visualized using “biplot” function from the Matlab statistics toolbox. The contribution of each principle component to the overall variability was visualized by a scree plot using the Matlab
“pareto” function.
Results
The swath surfaces for the ME70 and EM3002 data show a depth range of 60-78m (Figure 2-4).
Similar bathymetric features are visible in both surfaces. There is a banding artifact evident in the
ME70 data that runs parallel to the track lines. Examining the depths in CARIS HIPS swath editor reveal that these are due to a refraction artifact causing the outer beams to bend upward. This was likely caused by a sound speed error resulting from the CTD casts being too infrequent to capture the sound speed variability. Attempts to remove this artifact were made by shifting the sound speed profile, but these did not result in significant improvement. A difference surface for the overlapping portion of the swath surfaces was generated by subtracting the EM3002 surface from the ME70 surface (Figure 5). Statistics were generated showing the mean, standard deviation, and histogram distribution binned to 0.1m (Figure 6).
Figure 2: Swath surface gridded to 5m for the ME70. Units in meters.
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Figure 3: Swath surface gridded to 5m for the EM3002. Units in meters.
Figure 4: Overlaid swath surfaces for the ME70 and EM3002. Units in meters.
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Figure 5: Difference surface generated from subtracting the EM3002 from the ME70 surface.
Units in meters.
Mean
0.46m
Median
0.50m
Standard Deviation
0.43
Figure 6: Histogram and statistics for the difference surface.
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Values for the difference surfaces within 2 standard deviations from the 0.5m mean range from
-0.3 to 1.3m. The maximum difference occurred at nadir, with the EM3002 surface about 1m deeper than the ME70 surface. The differences approached zero in the outer swaths. This is due to the banding pattern running parallel to the Pisces tracklines that carried over from the ME70 bathymetry. As stated previously, the banding was due to an upwards refraction artifact in the outer beams of the ME70 data, possible due to sound speed correction error. Assuming that the ME70 nadir beams are more accurate than the outer beams, which is supported by the standard deviation surfaces, the ME70 produced depths that were on about 1m more shoal than the EM3002.
To compare how the sonars resolved changes in bathymetry, difference surfaces were generated for two subsets of the survey area. The first was over a bathymetric feature and the second was over a flat surface (Figure 7). Statistics were generated for each of these surfaces (Figure 8).
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Figure 7: Difference surfaces for subsets of the larger surface, taken over flat seafloor
(top) and a bathymetric feature (bottom). Units in meters.
Mean
0.64m
Median
0.69m
Standard Deviation
0.38
Mean
0.48m
Median
0.54m
Standard Deviation
0.37
Figure 8: Difference surface histograms and statistics for the subsets with the bathymetric feature
(top) and flat seafloor (bottom).
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In order to assess the ability of the ME70 and the EM3002 to detect small targets, a manual search for features less than or equal to 3m appearing in 2x2m gridded surfaces for both systems was conducted. One such target is a small circular depression 3.0m in diameter. This feature is clearly visible in the EM3002 surface (Figure 9). The feature is also visible in the ME70 surface in the nadir of the swath, although the resolution is poorer than for the EM3002 surface. The feature has a depth of 67.2m in the EM3002 surface and 67.1m in the ME70 surface. The blank pixels in the surface are the result of the 2x2m grid pushing the limits of resolution achievable with the ME70’s
2.2° nadir minimum beamwidth, which was calculated to be 2.57m at a depth of 67m, and they disappear when larger grids are used.
Figure 9: A 3.0m diameter circular depression visible in the swath surfaces for the EM3002 (top) and the ME70 (bottom).
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Backscatter surfaces were generated in Geocoder and several areas were selected for textural analysis (Figure 10-11). Areas 1 and 2 coincide with the non-flat and flat areas selected for the difference surfaces. The GLCM’s were generated and textural statistics were calculated as described in the methods section. The statistics calculated for the backscatter data from areas 1 and 2 were similar to all other areas, which are therefore not shown.
Area 1
Area 2
Figure 10: ME70 backscatter mosaic with areas 1 and 2 outlined in red.
Area 2 Area 1
Figure 11: EM3002 backscatter mosaic with areas 1 and 2 outlined in red.
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Sonar
ME70
EM3002
GLCM Variance
34.18
28.79
Correlation
0.81
0.40
Homogeneity
Table 2: Area 1 GLCM statistics.
0.85
0.67
Entropy
1.93
2.56
Sonar
ME70
EM3002
GLCM Variance
31.17
28.93
Correlation
0.83
0.34
Homogeneity
0.84
0.67
Entropy
2.19
2.30
Table 3: Area 2 GLCM statistics.
Biplots were then created to show the principal textural components controlling the variability of the mosaic (Figure 12-13). The red dots are the PCA scores for each GLCM statistic, which are the coordinates in the new reference frame defined by the principal components. The axis of the first principal component (the x-axis) is the direction with the greatest spread of the PCA scores.
The axis of the second principal component (the y-axis) is that with the greatest PCA score spread perpendicular to the 1 st
axis. The biplot vectors each represent a GLCM statistic. Their magnitude along each axis displays their contribution to the principal component represented by that axis. For example, entropy in the top left of figure 12 has a large component in the x-axis and a small component in the y-axis. This indicates a strong contribution to the first principal component and a weak contribution to the second.
Scree plots were also generated to show how much of the variance in the data is explained by each principal component. The blue line is the cumulative variance explained. Only the principal components accounting for the first 95% of the variability are plotted.
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Figure 12: Area 1 biplots and scree plots showing the principal components and percent variance explained by those components for the ME70 (top) and the EM3002 (bottom).
Figure 13: Area 2 biplots and scree plots showing the principal components and percent variance explained by those components for the ME70 (top) and the EM3002 (bottom).
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Area1
1 st Principal Component ME70 Entropy
(43.5%)
1 st Principal Component EM3002 Homogeneity
(51.7%)
Area2
Homogeneity
(43.9%)
Entropy
(48.6%)
Discussion
The swath surfaces for the ME70 and the EM3002 both show similar bathymetric features. The difference surfaces are very similar, each demonstrating the banding pattern and similar mean and standard deviation values. There is a difference of about 1m at nadir between the surfaces, ignoring the effects of the refraction artifacts. It is possible that this difference was caused by not accounting for the tidal variability. Although the banding pattern is apparent, the surfaces do not show any evidence of change in bathymetry. The fact indicates that the ME70 and EM3002 are both have a similar ability to resolve large scale changes in bathymetry. The appearance of the small hole in both swath surfaces has shown that each multibeam is able to detect small scale features up to
3.0m in diameter at 67m depth (4.5% of the water depth) at nadir, although the EM3002 does so with superior resolution to the ME70. This makes sense given the EM3002’s 1.5° beamwidth compared to the ME70’s 2.2° minimum nadir beamwidth.
From the biplots, it can be seen that the homogeneity and the entropy are the first principal components that occur most frequently. Their magnitude is similar, meaning that they describe the variance of the image equally well. Their direction is opposite because entropy has a large value when homogeneity has a low value, both of which indicate a rough irregular texture. These statistics are consistently the first principal components in all analyzed areas, indicating that the measure of order and disorder is the dominant factor explaining the textural variability of the backscatter for both multibeam systems.
The GLCM statistics calculated for the backscatter data both show that the ME70 consistently produces higher values for correlation and homogeneity but lower values for entropy than the
EM3002. Homogeneity and entropy measure order and disorder respectively. Therefore, the ME70 backscatter has a smoother and more regular texture than the EM3002 for the same area. The
ME70 also shows a high correlation, meaning the GLCM pixels have a higher dependence on their neighbors. The GLCM variance is also greater for the ME70, meaning the backscatter values have a greater spread around the mean than for the EM3002. Although the sonars show clear differences in their statistics, there is not a big difference between area 1 and area 2. This was unexpected, because of the feature present in area 1 and the lack of a feature in area 2. As the GLCM statistics are sensitive to the selected window and step size, it is possible that the chosen values are not adequate to capture the textual variability. The speckle in the images may also have masked the true image texture. It is also possible that the patterns show a strong directional dependence, and thus the directionally invariant GLCM may not capture this variability.
Conclusions and Future Research
It has been shown that the ME70 and EM3002 are equally able to resolve large scale bathymetric features. Both multibeams were also able to detect small scale features at least 2.5m in diameter,
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although the EM3002’s smaller beamwidth allowed it to produce a better resolution image than the ME70. When ignoring the refraction artifact, the EM3002 data were consistently 1m deeper than the ME70, which may be accounted for by tidal variability. The backscatter texture is primarily described by the homogeneity and entropy. The correlation and homogeneity were consistently higher for the ME70 than the EM3002, and the entropy was consistently lower. There was no apparent difference in the GLCM statistics between the homogenous backscatter area and the area with a feature. This may be due to the window size, step size, excessive speckle, or directionally invariant GLCM being inadequate to capture the textual variability of the backscatter.
Future research into this area would do well to collect data with the purpose of a comparison in mind. This would include running lines along the same direction to help prevent backscatter variation from ensonifying the same patch of seafloor from different angles. The textural analysis would also benefit from surveying an area with regularly repeating features, such as sand waves.
Target analysis would also benefit if these features had a known size and were smaller than 2.5 meters. Care should also be taken to reduce refraction artifacts by collecting frequent sound speed profiles, which would allow for a more clear comparison of the bathymetry. Future backscatter comparisons should test different directional GLCM’s, as well as different window sizes and step sizes. Another possibility for backscatter analysis would be to attempt angular response analysis and to compare the results of bottom classification generated from the backscatter of each sonar.
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