Automated mapping of marine habitats from marine sonar and bathymetric data M. Trigg1*, W. Wilsher2, G. Schumann1, B. Pearce2, L. Seiderer2, and P.D. Bates1 1
University of Bristol, School of Geographical Sciences, Bristol (
[email protected]), 2 Marine Ecological Surveys Ltd, Bath
5.2 Approach 2 – Real time xtf file processing
4. Feature and Algorithm Catalogues
1. Introduction Extraction of aggregates and other activities affecting the ocean floor require detailed sea floor survey for the identification of marine habitats and other important features to ensure they are preserved during extraction activities. These surveys generate large quantities of sonar and bathymetric data that are currently processed manually by experts in order to identify features of interest. This is an expensive and time consuming process.
In order to ensure appropriate algorithm application to feature identification, two data catalogues were created. The feature catalogue was compiled by scientists at MES limited, identifying the acoustic signature characteristics of features divided into: (i) background sediments (to be excluded); (ii) anthropogenic features (eg ship wrecks, trawl scars); and (iii) marine habitats of conservation interest (eg Bream nests, Sabellaria reefs). The algorithm catalogue was generated by the University of Bristol, and lists a full range of image analysis methods with their suitability for different acoustic signature characteristics and computation speed.
Real time processing of raw xtf files, direct from the sonar equipment onboard the survey vessel, is being explored as a way of identifying areas of interest that warrant further survey or optimum locations for grab samples.
2. Project Aim There is significant potential to support the expert data analysis through the application of a range of automated image processing techniques which could accelerate processing as well as bring more rigor and consistency to the process. This pilot project aims to explore the potential of applying scientific expertise in image analysis developed at the University of Bristol for the processing of satellite remote sensing data in order to support the sonar and bathymetric data processing operation.
The first stage in the project involved documenting the current methods used in the feature identification process in order to understand how automated methods from remote sensing analysis may be best used.
Repeated screening
Trawl scars
Colour photographs of the seabed showing: patchy Sabellaria associated with boulders [left]; Sabellaria spinulosa reefs [centre]; and black bream nest [right] (©MES Ltd)
5. Investigative Approaches
Mosaic providing context for identified habitats
CODA Sonar processing software
•
Mosaic output of entire survey, multiple overlapping survey lines (0.5 to 5 pixels per m)
Visual Identification Expert identification of features from survey line
Context Map Mosaic and other GIS data layers to understand context of feature
Tiff capture Manually freeze display, digitise 4 corners of area of interest and export tiff image of area
Polygon Layer Manually create polygons from 4 corner points and hyperlink to *.tiff file
Close up of sonar mosaic showing gaps and overlap anomalies which present challenges to automated feature identification methods.
6. Findings so far
5.1 Approach 1 – Tiff image analysis
Scrolling display Manually controlled, variable rate, scrolling monitor output of single survey line
Tiff images of features of interest are analysed automatically using a range of algorithms to classify features according to the feature catalogue and assign a identification certainty level. An example using morphological processing of Bream nests is shown below.
• • •
Features of interest have a broad range of characteristics and it is therefore likely that a whole variety of algorithms will be required to automatically identify all features in catalogue. Careful and appropriate use of automated methods has the potential to greatly increase the speed and consistency of habitat identification. Real time processing of raw sonar data on the survey vessel could provide the tools to enable adaptive surveying, maximising research value. Identified challenges: large quantities of data may affect algorithm choice; defining confidence of feature identification; integrating into current process; level of automation; processing computation cost; sensitivity of automatic methods to data quality; algorithm setup and tuning time.
7. Next Steps Classified outputs from automated algorithms across a range of different habitat types, with a consistent approach across different survey areas, has the potential to highlight previously unknown connections between habitat types and their locations. It will be important to apply the methods in a rigorous and consistent manner, with results of different methods tailored to fit within a common framework in order to analyse potential correlations and shared characteristics.
ArcMap GIS software
Schematic of current manual marine habitat identification process
Sand ripples
5.3 Approach 3 – Sonar mosaic analysis
Documentation of the habitat identification process highlights important issues such as identification certainty and the significant number of hours that are spent manually scrolling through data in order to exclude areas rather than identifying areas of interest. Explicit definition of the current identification process and creation of the feature and algorithm catalogues reveals three possible approaches where automated image analysis may prove advantageous. These three approaches form the core feasibility investigations for this pilot project and are outlined below and are currently still ongoing.
Survey data collection (1) High freq. sonar (2) Low freq. sonar (too noisy) (3) Bathymetric data (sometimes) (4) Grab samples (sometimes) (5) Seabed imagery (sometimes)
Report Tables, images and maps, expert analysis
Dredging scars
Automatic filtering of sonar mosaics to exclude areas that are not of interest has the potential to reduce the amount of data that is manually scanned at the moment by up to 80%.
3. Manual Analysis Process
Preprocessing (*.xtf files) (1) Geolocation (2) Time varying gain adjustment (3) Noise reduction (4) Overlapping data
Sonar feed with sand ripples
input image
Filtering and thresholding
morphological opening
output image with nest count (188)
A multiple feature identification approach could provide novel combinations of methods or the development of new image analysis methods tailored to sea bed survey. Given the scale of the sea floor and how little is known about this important environment, developing these methods will provide new tools for science to investigate the ecology of the sea floor environment. OS4, Z5, EGU2010-4536
