Artificial neural network textural Artificial neural network textural image analysis of medium- to deep-image analysis of medium- to deep-water backscatter mosaics based on water backscatter mosaics based on

MatlabMatlab

The University of Sydney Institute of Marine ScienceThe University of Sydney Institute of Marine Science

R. Dietmar MR. Dietmar Müüller and Michael Hughesller and Michael Hughes

The University of Sydney Institute of Marine ScienceThe University of Sydney Institute of Marine Science

Centre for Ecological Impacts of Coastal CitiesCentre for Ecological Impacts of Coastal CitiesSpecial Research Centre for offshore foundation systemsSpecial Research Centre for offshore foundation systems

Coastal studies groupCoastal studies groupOcean technology groupOcean technology group

Marine geophysics and geodynamics groupMarine geophysics and geodynamics groupSpatial Science Innovation Unit Spatial Science Innovation Unit

(marine geographic information systems)(marine geographic information systems)Australian Ocean Drilling officeAustralian Ocean Drilling office

Mapping of sMapping of seafloor geology and habitats eafloor geology and habitats in in medium-deep water dependsmedium-deep water depends on remotely on remotely sensed multibeam images and a limited sensed multibeam images and a limited

number of seafloor samplesnumber of seafloor samples

Simrad EM 12D Simrad EM 12D

medium-deep medium-deep water systemwater system

2 adjoining sonars 2 adjoining sonars with 81 beams each.with 81 beams each.

Effectively 152 beams Effectively 152 beams due to overlapping.due to overlapping.

EM12 backscatter data off SE EM12 backscatter data off SE AustraliaAustralia

MethodologyMethodology

Data pre-processingData pre-processing Feature extractionFeature extraction Selection of a classification Selection of a classification

algorithm and classifier trainingalgorithm and classifier training ClassificationClassification

Data Processing with Data Processing with “Caraibes” software “Caraibes” software

(Ifremer)(Ifremer)

Raw image file (.IM).

Navigation file (.nvi).

Bathymetric file (.mbb).

EREAMO

EPREMO

mosaic image file (.imo).

Georeferencing file (.geo_imo).

Caraibes Modules

Backscatter as a function of grazing Backscatter as a function of grazing angleangle

Interpolated Backscatter Interpolated Backscatter ImageImage

Artefacts:Specular reflections near nadirStripes across trackData “holes”Incomplete coverage due to course changes

Great Australian Bight

OtwayBasin

BassBasin

Seafloor Backscatter Seafloor Backscatter Image Image from GAB Marine Park from GAB Marine Park

Depths range from 4.5km in the south to 0.5km in the north.

Artefacts:Specular reflections near nadirStripes across trackData “holes”Incomplete coverage due to course changes

Closeup in GAB Marine Closeup in GAB Marine ParkPark

Foraminiferal Ooze Sandy Ooze

Muddy/Clayey Ooze

Lithology identificationLithology identification

128 pixels

Sand, Mud and RockSand, Mud and Rock

Outcrop

Sand/Gravel Mud

Classes of Seabed Classes of Seabed

Typical classes on continental Typical classes on continental shelf:shelf:– Foraminiferal oozeForaminiferal ooze– Sandy oozeSandy ooze– Muddy/Clayey oozeMuddy/Clayey ooze– Sand/GravelSand/Gravel– MudMud– Hard rock outcropHard rock outcrop

Texture AnalysisTexture Analysis

Frequency Domain Features Frequency Domain Features

(e.g. power spectrum)(e.g. power spectrum) Space Domain Features:Space Domain Features:

– Grey Level Run LengthGrey Level Run Length– Spatial Grey Level DependenceSpatial Grey Level Dependence– Grey Level DifferenceGrey Level Difference

4 Directions (0º, 45º, 90º, 135º)4 Directions (0º, 45º, 90º, 135º)

Grey Level Run LengthGrey Level Run Length0 1 2 3

0 2 3 3

2 1 1 1

3 0 3 00º 1 2 3 4

0 4 0 0 0

1 1 0 1 0

2 3 0 0 0

3 3 1 0 0

Spatial Grey Level DependenceSpatial Grey Level Dependence

0 1 2 3

0 2 3 3

2 1 1 1

3 0 3 0 0º 0 1 2 3

0 0 1 1 3

1 1 4 2 0

2 1 2 0 2

3 3 0 2 2

Grey Level Difference VectorsGrey Level Difference Vectors0 1 2 3

0 2 3 3

2 1 1 1

3 0 3 00º

0 3

1 5

2 1

3 3

Sub-sampling images centered on seabed Sub-sampling images centered on seabed samplessamples

Sample images = 128x128 pixelsSample images = 128x128 pixels Divided these up in to 32x32 pixelsDivided these up in to 32x32 pixels Sub-sample images overlap by 16 pixels Sub-sample images overlap by 16 pixels This increases the number of training This increases the number of training

images, even though they are not images, even though they are not statistically independentstatistically independent

32x32

2x2 km

128x128 (8x8 km)

Neural NetworksNeural NetworksAdvantages:

• 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 LithologiesFour Lithologies

Neural Network TrainingNeural Network Training Typical network is trained with an Typical network is trained with an

architecture as follows: architecture as follows: Network layers 16-12-12-5Network layers 16-12-12-5 45 training samples 45 training samples 23 validation samples23 validation samples 22 test samples22 test samples

Training Success

97 93 84 88 100

Test Success 95 93 82 86 100

Sandy Ooze Clayey Ooze Sand-Gravel Outcrop

GeneralisationGeneralisation

Early Stopping prevents the Early Stopping prevents the network from over fitting the datanetwork from over fitting the data

Implement a validation set of Implement a validation set of samples that monitors the samples that monitors the performance of the network as it performance of the network as it evolvesevolves

Final Network ResultsFinal Network Results

The network was trained with an The network was trained with an architecture: architecture:

16-12-12-516-12-12-5 45 training samples 45 training samples 23 validation samples23 validation samples 22 test samples.22 test samples.

Training Training SuccessSuccess

9797 9393 8484 8888 100100

Test Test successsuccess

9595 9393 8282 8686 100100

4 facies classification for South Tasman 4 facies classification for South Tasman RiseRise

STINCT

Sharp. No sub-bottoms. Flat to undulating.

hardgrounds,sibly manganesests and nodule fields

Sharp. Continuous parallel

sub-bottoms. Little or no relief

undisturbed pelagic hemipelagic ooze,sibly mud turbidites.

INDISTINCT

IIA

- Sharp discontinuousparallel sub-bottoms.

- Flat to undulatinge.g. fine distal turbiditeswith minor pelagics,often disturbed.

IIB

- Semi-prolonged toprolonged, “fuzzy”.

- No sub-bottoms.- Often marked relief.e.g. sand/silt turbidites,minor reworked pelagics,current winnowed sand.

HYPERBOLAE

IIIA

- Irregular overlapping hyperbolae.Varying vertex elevations. Larger thanIIIC.e.g. Extreme topography, scarps andseamounts.

IIIC

- Regular overlapping hyperbolae.Variable vertex elevation andhyperbolae size.- Semi-prolonged to prolongede.g. marked topography, scarps,canyons and basement outcrop.

IIID

- Numerous regular hyperbolae tangentialto seafloor.e.g. rare. Small regular bedforms.

Seismic Seismic Facies Facies (3.5 kHz(3.5 kHzsub-bottomsub-bottomprofiler)profiler)

3.5 kHz (left) vs. backscatter (right) 3.5 kHz (left) vs. backscatter (right) classificationclassification

From Whitmore & Belton, AJES, 1997)

Increase classes of Seabed Increase classes of Seabed

6 classes:6 classes:– Foraminiferal oozeForaminiferal ooze– Sandy oozeSandy ooze– Muddy/Clayey ooze. (203)Muddy/Clayey ooze. (203)– Sand/Gravel. (154)Sand/Gravel. (154)– Mud. (156)Mud. (156)– Outcrop. (175)Outcrop. (175)

• MudstoneMudstone• VolcanicsVolcanics

Results From 6 ClassesResults From 6 Classes

The training accuracies were low The training accuracies were low for foraminiferal ooze and sandy for foraminiferal ooze and sandy ooze ~ 50%.ooze ~ 50%.

The network was unstable. The network was unstable. The classes were too acoustically The classes were too acoustically

similar to be distinguished similar to be distinguished accurately.accurately.

ConclusionsConclusions Our methodology can consistently produce Our methodology can consistently produce

robust classifiers that can accurately classify robust classifiers that can accurately classify 4 lithologies of seafloor4 lithologies of seafloor

VValidation and regularisation techniques in alidation and regularisation techniques in neural network classification are important in neural network classification are important in producing a well-trained network that producing a well-trained network that generalises well and is not “over-trained”generalises well and is not “over-trained”

BBackscatter intensity ackscatter intensity must be must be corrected for corrected for grazing-angle. If not, then the mean grazing-angle. If not, then the mean intensity cannot be used well for recognising intensity cannot be used well for recognising particular seafloor lithologies, reducing particular seafloor lithologies, reducing network training successnetwork training success

Amplitude as a function of grazing angle Amplitude as a function of grazing angle not corrected, therefore image is difficult not corrected, therefore image is difficult to classifyto classify

Software is usually expensive, and data Software is usually expensive, and data formats are not standardised, ie it is not formats are not standardised, ie it is not straightforward for an individual straightforward for an individual researcher to perform data post-researcher to perform data post-processingprocessing

Great Australian Bight

OtwayBasin

BassBasin

Seafloor Backscatter Seafloor Backscatter Image Image from GAB Marine Park from GAB Marine Park

Future outlookFuture outlook When data collection is outsourced it is extremely When data collection is outsourced it is extremely

important to verify beforehand that data will be fully important to verify beforehand that data will be fully processed (usually not the case …)processed (usually not the case …)

The Southern Surveyor will provide a suitable The Southern Surveyor will provide a suitable platform in Australia to collect both multibeam and platform in Australia to collect both multibeam and sub-bottom profiling datasub-bottom profiling data

Correlations between multibeam and 3.5Hz data may Correlations between multibeam and 3.5Hz data may provide a way of ground-truthing without acquiring provide a way of ground-truthing without acquiring vast numbers of sediment samplesvast numbers of sediment samples

Need more testing of different approaches for Need more testing of different approaches for classification classification and groundtruthingand groundtruthing of backscatter data of backscatter data

Large field of application from seabed-habitat Large field of application from seabed-habitat mapping to defencemapping to defence