The University of Sydney Institute of Marine Science

1. Artificial neural network textural image analysis of medium- to deep-water backscatter mosaics based on Matlab R. Dietmar Müller and Michael Hughes The University of Sydney Institute of Marine Science

2. The University of Sydney Institute of Marine Science Centre for Ecological Impacts of Coastal Cities Special Research Centre for offshore foundation systems Coastal studies group Ocean technology group Marine geophysics and geodynamics group Spatial Science Innovation Unit (marine geographic information systems) Australian Ocean Drilling office

3. Mapping of seafloor geology and habitats in medium-deep water depends on remotely sensed multibeam images and a limited number of seafloor samples

4. Simrad EM 12D medium-deep water system 2 adjoining sonars with 81 beams each. • Effectively 152 beams due to overlapping.

5. EM12 backscatter data off SE Australia

6. Methodology • Data pre-processing • Feature extraction • Selection of a classification algorithm and classifier training • Classification

7. Data Processing with “Caraibes” software (Ifremer) Raw image file (.IM). mosaic image file (.imo). EPREMO EREAMO Navigation file (.nvi). Georeferencing file (.geo_imo). Bathymetric file (.mbb). Caraibes Modules

8. Backscatter as a function of grazing angle

9. Interpolated Backscatter Image Artefacts: Specular reflections near nadir Stripes across track Data “holes” Incomplete coverage due to course changes

10. Great Australian Bight Otway Basin Bass Basin Seafloor Backscatter Image from GAB Marine Park Depths range from 4.5km in the south to 0.5km in the north. Artefacts: Specular reflections near nadir Stripes across track Data “holes” Incomplete coverage due to course changes

11. Closeup in GAB Marine Park

12. Lithology identification 128 pixels Foraminiferal Ooze Sandy Ooze Muddy/Clayey Ooze

13. Sand, Mud and Rock Sand/Gravel Mud Outcrop

14. Classes of Seabed • Typical classes on continental shelf: • Foraminiferal ooze • Sandy ooze • Muddy/Clayey ooze • Sand/Gravel • Mud • Hard rock outcrop

15. Texture Analysis • Frequency Domain Features (e.g. power spectrum) • Space Domain Features: • Grey Level Run Length • Spatial Grey Level Dependence • Grey Level Difference • 4 Directions (0º, 45º, 90º, 135º)

16. Grey Level Run Length

17. Spatial Grey Level Dependence

18. Grey Level Difference Vectors

19. Sub-sampling images centered on seabed samples • Sample images = 128x128 pixels • Divided these up in to 32x32 pixels • Sub-sample images overlap by 16 pixels • This increases the number of training images, even though they are not statistically independent 128x128 (8x8 km) 32x32 2x2 km

20. Neural Networks • Advantages: • No a priori assumptions are made • about data distributions • High tolerance to noise • Integrate information from multiple • sources • Allow the incorporation of new • features without penalising prior • learning • The efficiency of neural network classifiers is high in terms of parallel processing once the classifiers have been properly trained. These classifiers, however, require a carefully chosen training set, which has sufficient information to represent all classes to be distinguished

21. Four Lithologies

22. Neural Network Training • Typical network is trained with an architecture as follows: • Network layers 16-12-12-5 • 45 training samples • 23 validation samples • 22 test samples Sandy OozeClayey OozeSand-GravelOutcrop

23. Generalisation • Early Stopping prevents the network from over fitting the data • Implement a validation set of samples that monitors the performance of the network as it evolves

24. Final Network Results • The network was trained with an architecture: • 16-12-12-5 • 45 training samples • 23 validation samples • 22 test samples.

25. 4 facies classification for South Tasman Rise

26. Seismic Facies (3.5 kHz sub-bottom profiler)

27. 3.5 kHz (left) vs. backscatter (right) classification From Whitmore & Belton, AJES, 1997)

28. Increase classes of Seabed • 6 classes: • Foraminiferal ooze • Sandy ooze • Muddy/Clayey ooze. (203) • Sand/Gravel. (154) • Mud. (156) • Outcrop. (175) • Mudstone • Volcanics

29. Results From 6 Classes • The training accuracies were low for foraminiferal ooze and sandy ooze ~ 50%. • The network was unstable. • The classes were too acoustically similar to be distinguished accurately.

30. Conclusions • Our methodology can consistently produce robust classifiers that can accurately classify 4 lithologies of seafloor • Validation and regularisation techniques in neural network classification are important in producing a well-trained network that generalises well and is not “over-trained” • Backscatter intensity must be corrected for grazing-angle. If not, then the mean intensity cannot be used well for recognising particular seafloor lithologies, reducing network training success

31. Great Australian Bight Otway Basin Bass Basin Seafloor Backscatter Image from GAB Marine Park • Amplitude as a function of grazing angle not corrected, therefore image is difficult to classify • Software is usually expensive, and data formats are not standardised, ie it is not straightforward for an individual researcher to perform data post-processing

32. Future outlook • When data collection is outsourced it is extremely important to verify beforehand that data will be fully processed (usually not the case …) • The Southern Surveyor will provide a suitable platform in Australia to collect both multibeam and sub-bottom profiling data • Correlations between multibeam and 3.5Hz data may provide a way of ground-truthing without acquiring vast numbers of sediment samples • Need more testing of different approaches for classification and groundtruthing of backscatter data • Large field of application from seabed-habitat mapping to defence