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eCognition User Forum | Southampton | 1 October 2013 | facilitated by KOREC Group

Applying object-based image analysis to acoustic seabed imagery Dr Markus Diesing Cefas, Lowestoft, United Kingdom

Seabed and habitat mapping in Cefas • Centre for Environment, Fisheries and Aquaculture Science • Executive agency of Defra • Our purpose is to work alongside government and other agencies to play a vital role in securing healthy marine and freshwater environments for everyone’s wellbeing, health and prosperity. • Four science divisions – AHH, MPM, F, EE • Environment and Ecosystems Division – Marine Environmental Assessment Group – Habitat Mapping and Human Activities Team

Mapping the seabed • We use acoustics rather than light to map the seabed. • Light is rapidly attenuated in water. • Acoustics are an efficient way of imaging the seabed, water column and subsurface.

Iz = I0 exp(-kZ) k: attenuation coefficient Blue Lake, NZ (Credit: Klaus Thymann / Project Pressure)

Multibeam sonar • Can be seen as an extension of singlebeam echosounder • Transmits and receives a fan of beams (100 – 200) with small individual apertures (1 – 3°) across the axis of the ship.

Lurton (2002) L – swath width θL – longitudinal aperture θ T – transversal aperture θ M – max. beam tilt angle (half the total aperture)


Backscatter • Multibeam sonars do not only measure depth but also reflectivity (backscatter) • Most of the energy arriving at the seabed is reflected away from the sonar • A small portion is lost in the ground • A small portion is scattered back to the sonar

Backscatter • Backscatter strength is affected in decreasing order of importance by: – The geometry of the sonar-target system (e.g. incidence angle, slope) – The physical characteristics of the surface (e.g. microscale roughness) – The intrinsic nature of the surface (e.g. composition, density)

Spatial data for habitat mapping

Brown et al. (2011). Estuarine, Coastal and Shelf Science

Barrett Reef: Acoustic data Backscatter



Data: Ground-truthing Initial Class


Final Class

Rock/boulders (11)

Rock/hard substrate (13)

Continuous bedrock (9) Large boulder (1) Small boulder (1)

2 (rgh high)

Cobble (6)

Cobble (6)

Gravel (10)

Gravel (10)

Coarse sand (7)

Coarse sand (7)

Fine sand (10)

Fine sand (10)

4 (rgh low) Coarse sediment (21)

Fine sand (10)

Segmentation Segmentation carried out in 3 steps: Multi-resolution segmentation on bs and rgh (Scale parameter: 5, shape: 0.1, compactness: 0.5) 277,014 objects Multiple object difference conditions-based fusion (Δbs ≤ 15 DN, Δrgh ≤ 0.25) 186,947 objects Remove objects (Area ≤ 100 pxl) 3,459 objects

Knowledge based classification Making use of general and site specific knowledge:

• •

Rock has high roughness and occurs in shallow water.

Fine sand has low backscatter and low standard deviation of backscatter.

Coarse sediment has high backscatter and low/moderate roughness.

Results: Barrett Reef

Final classification Coarse sediment


Coarse sediment

Fine sand

Future directions • Knowledge based classification – no formal use of sampling data • Sample based classification – use of machine learning classifiers • Conditional Inference Trees (Hothorn et al., 2004) – – – –

Binary recursive partitioning Utilises a conditional inference framework (Strasser and Weber, 1999) Tests the statistical significance of splits No pruning or cross-validation is needed

Future directions

Future directions

Future directions

Conclusions • Acoustic data are different from terrestrial remote sensing data • OBIA can be successfully employed to map seabed habitats • Knowledge based classification generally works well • Great potential in linking OBIA with machine learning We are still at the beginning. We would like to learn from the terrestrial community.

Acknowledgements • KOREC group and Trimble for the invitation • Shallow Survey 2012 Organising Committee for provision of Barret Reef acoustic data set • Alix Laferriere (Victoria University of Wellington) for Barret Reef ground-truthing data • British Geological Survey for providing PSA data – (BGS Legacy Particle Size Analysis uncontrolled data export (2011), British Geological Survey,

• UKHO/MCA for providing MBES data Any questions?

Markus diesing cefas  
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