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

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

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

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

EM12 backscatter data off SE Australia

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

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

Backscatter as a function of grazing angle

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

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

Closeup in GAB Marine Park

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

Sand, Mud and Rock Outcrop Sand/GravelMud

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

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

Grey Level Run Length º

Spatial Grey Level Dependence º

Grey Level Difference Vectors º

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

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

Four Lithologies

Neural Network Training n Typical network is trained with an architecture as follows: n Network layers n 45 training samples n 23 validation samples n 22 test samples Training Success Test Success Sandy Ooze Clayey Ooze Sand-Gravel Outcrop

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

Final Network Results n The network was trained with an architecture: n n 45 training samples n 23 validation samples n 22 test samples. Training Success Test success

4 facies classification for South Tasman Rise

SeismicFacies (3.5 kHz sub-bottomprofiler)

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

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

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

Conclusions n Our methodology can consistently produce robust classifiers that can accurately classify 4 lithologies of seafloor n Validation and regularisation techniques in neural network classification are important in producing a well-trained network that generalises well and is not “over-trained” n 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

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

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