Automated Mapping of Marine Habitats
from Marine Sonar
MEPF Ref No: MEPF 09/P107
Note to reader: These PowerPoint slides accompany the final report for project ref
MEPF09/P107, entitled: ‘Automated mapping of marine habitats from marine sonar’ and the
separate summary recommendations report . The final report and recommendations report
are available at www.alsf-mepf.org.uk.
The main objective of the project was a feasibility study that aimed to outline ways in which
aspects of sidescan sonar data processing and interpretation could be automated using novel
software techniques.
The slides show two example methods in which the mapping of benthic environments using
sidescan sonar data can be, in part, automatically conducted by novel software methods has
been produced. The PowerPoint slides illustrate just one way in which these demonstrations
could appear on a website, where users may click to reveal an image, title and description for
each stage of the automated processes.
Crown Copyright 2010
First Published by the MALSF
Automated Mapping of Marine Habitats
from Marine Sonar
MEPF Ref No: MEPF 09/P107
Bream Nest Example
Acquisition
Sidescan sonar
imagery is obtained
from the survey vessel.
Contrast
Adjustment
Imagery is contrast
adjusted to highlight
subtle features.
Thresholding
High contrast areas are
isolated, and dull areas,
that are likely not to
contain any features of
interest, are discounted.
Object
Isolation
Analysis &
Classification
The size and
connectedness of each
object is measured and
objects smaller than a
certain size are ignored,
since they are probably
background noise.
The remaining objects are
counted and their size and
shape is measured. Those
that appear round, of a
particular size and in close
proximity to other similar
objects are classified as
bream nests and verified
by a human expert.
Automated Mapping of Marine Habitats
from Marine Sonar
MEPF Ref No: MEPF 09/P107
Neural Network & Mussel Bed Example
Acquisition
Sidescan sonar imagery is
obtained from the survey vessel. It
shows an area with two different
kinds of seabed environments.
Training
The neural network is trained to
recognize common acoustic
signatures using example data.
Classification
Using its knowledge base, the
neural network classifies sidescan
sonar imagery by comparing the
likeness of the real data to the
examples that it knows. In this case
white areas are classified as mussel
bed.
Boundary Definition
The outer boundary of the area
classified as mussel bed is
automatically calculated and
georeferenced, making the mussel
bed habitat ready for plotting on a
map.