A Comparison of Seafloor Classification Methods Through the Use ...

A Comparison of Seafloor Classification Methods Through the Use 

of Multibeam Sonar, Error Matrices and 3-Dimensional 

3 

Visualization: Case Study of Nehalem Bank, Oregon 

Andrew S. Lanier 

Marine Resource Management Program 

College of Oceanic and Atmospheric Sciences 

Oregon State University 

July 17 th , 2006

Thesis Outline 

• Introduction 

– Background & Motivation 

– Multibeam Sonar Principles 

• Study Site and Data Set 

Description 

– Processing 

– Results 

• Benthic Habitat Classification 

– Background 

– Topographic Classification 

– Quantitative Assessment 

– Backscatter Classification 

• Discussion of Results 

• Conclusions and Future 

Implications 

• Acknowledgements 

Submersible 

dive images from 

Nehalem Bank 

July 17 th , 2006 MS Thesis Defense

Background 

Timeline of Significant Events 

Rockfish Conservation Area (May, 2006) 

MSA Reauthorized 

EFH evaluation mandated 

NMFS declares 9 groundfish 

species“Over-fished” –Implements 

Area Closures 

NMFS publishes Final EIS 

alternatives 

White = Regulatory 

Yellow = Non-Regulatory 

NMFS Litigation Re: EIS 

PEW Oceans Commission 

1 st EFH Maps published 

US Oceans Commission 

(Bellman et al, 2006) 


Active Tectonics and 

Seafloor Mapping Lab 

Motivation 

1 st Published EFH Map 

Thematic interpretation 

(Romsos, 2004) 

Data Gaps 

Scale and Resolution Issues 

My Role: To evaluate what 

kinds of analysis are possible 

when there is a high density 

of different types (and quality) 

of data available 

(Romsos, 2004) 


Motivation 

Study Goals 

Identify the limitations of the multibeam sonar data set. 

Use the Nehalem Bank study site to address the 

following questions: 

– Does the multibeam data provide an accurate depiction 

of the seafloor topography? What artifacts remain and 

propagate into the classified images and data layers? 

– How do you assess the quality of a benthic 

classification attempt when groundtruthing information 

is at a different scale of observation? 







Description 


– Results 








Implications 


Multibeam 

Sidescan 


Active Remote Sensing 

Multibeam Sonar Principles 

Motion Corrections 

Sound reflection characteristics 

Common Artifacts 






– Benthic Habitat Classifications 

• Methods and Schemes 

• Study Site and Data Set Description 


– Results 

• Methods Tests and Results 





• Conclusions and Future Implications 


Graphic Here of MB Survey 

+ Processing Steps 

(Base maps by J. Chaytor) 


Data Specifics: 

– Collected using University of 

Washington RV Thomas G. 

Thompson, using Kongsberg Simrad 

EM300 Sonar a 30 kHz frequency 

(August, 2002). 

– Collected over a period of 3 days and 

covers an area of approximately 375 

km 2 

– 135 one degree (1x1) acoustic beams 

(swath width ~1.5X water depth) 

– Effective EM300 depth range ~100m 

to ~3000m 

Topographic Processing Steps: 

MBedit MBvelocitytool MBtide 

Nehalem Bank Data 

Tide Corrections 

Scientist Cave 


Nehalem Bank Backscatter Data 

Nehalem Bank Backscatter Data 

Backscatter Processing 

• Angle filter to correct for the influence of grazing angle on the grey level reflectance values (MBbackanglecorrect) 







Description 


– Results 








Implications 


Model of Classification Procedures 

Topographic 

BTM 

BTM Classified 

Image 

DEM 

Qualitative 

Assessment 

Reference 

Points 

Error Matrices 

Quantitative 

Assessment 

SIM 

SIM Classified 

Image 

Backscatter 

Backscatter 

Image 

Dive 

Interpretation 

Textural 

Analysis 

Classified 

Image 

Qualitative 

Assessment 

Input Data Set 

Analysis Method 

Result 

Assessment 


Benthic Classification Methods 

Seafloor Classification efforts falls behind that of terrestrial classification because of the 

nature of collecting observational data under water (Difficult to collect, $$$, Time) 

Types of data available: 

– Sample collection, the acquisition of remotely sensed data (sidescan sonar, multibeam 

sonar, seismic), and observational video groundtruth data 

Common types of classification: 

By single variable 

– Depth 

– Substrate 

Image classification techniques: 

– Spatial analysis 

• Spatial filtering 

• Digital Elevation Model (DEM) derivatives (slope, aspect, plan, profile) 

Previous work on the Oregon seafloor Conducted @ Heceta Bank ( Nasby-Lucas et 

al., 2002; Whitmire, 2003). 


Benthic Terrain Modeler Classification 

Classification Process 

Definition of Zones 

Crests – 

•High points in the terrain 

•values greater than one standard deviation 

from the mean in the positive direction. 

• Depressions – 

•Low points in the terrain 

•Values greater than one standard deviation 

from the mean in the negative direction. 

• Flats – 

•Flat points in the terrain 

•values that are within one standard deviation 

of the mean. Flats have a slope that is 5. 

Bathymetric Position Index 


Benthic Terrain Modeler Classification 

Classification Process 

Classification Dictionary 


SIM Classification 

Surface Interpretation Method 

Uses a randomly sited set of points 

interpreted in the 3D environment 

Model of Classification Process 

•1 st Calculation of spatial indices 

•2 nd Multiple derived spatial analysis indices 

are condensed using PCA and intersected 

with the interpreted points to create 

classification signatures. 

•3 rd The signatures are used in a maximum 

likelihood classification algorithm to group 

the values into classes. 


Topographic Classification Results 

BTM 

SIM 



Class 

Description 

Topographic Classified Area 

1 

Narrow 

Depression 

500 

3DSIM 

BTM Tool 

2 

3 

Depression Local 

Crest 

Depression 

Gentle Slope 

4 Crest Depression 

5 Crest Ridgetop 

6 Crest Plateau 

Area (10^9 meters) 

450 

400 

350 

300 

250 

200 

150 

30.00 

25.00 

20.00 

15.00 

10.00 

5.00 

7 Crest Hillslope 

8 

9 

10 

Open Slope 

Depression 

Open Slope 

Crest 

Open Shelf 

Slope 

100 

50 

0 

0.00 

1 2 3 4 5 6 7 8 9 

1 2 3 4 5 6 7 8 9 10 

Class 



Qualitative Comparison 

Summary 

BTM 

Strengths 

• Fine structural detail 

Weaknesses 

• Bank edges 

SIM 

Strengths 

• Fine structural detail 

• Bank edges 

Weaknesses 

• Sonar Artifacts 










– Results 









Topographic 

BTM 


Image 

DEM 

Qualitative 

Assessment 

Reference 

Points 


SIM 


Image 

Backscatter 

Backscatter 

Image 

Dive 


Textural 

Analysis 

Classified 

Image 

Qualitative 

Assessment 



Quantitative 

Assessment 

Result 

Assessment 


Quantitative Assessment Methods 


– Reference Data 

• Methods 

– Reference Points Created 

and Interpreted 

– Compared to the results 

from the classification 

methods. 

– An error matrix was 

completed for each 

classified grid. 

3D Perspective View of Nehalem Bank 


Quantitative Assessment Results 


Quantitative Assessment Results 

Error Matrices (Simplified Classification) 

Error Matrices Statistics 

KHAT









– Results 









Topographic 

BTM 


Image 

DEM 

Qualitative 

Assessment 

Reference 

Points 


SIM 


Image 

Backscatter 

Backscatter 

Image 

Dive 


Textural 

Analysis 

Classified 

Image 

Qualitative 

Assessment 



Quantitative 

Assessment 

Result 

Assessment 


Backscatter Image Classification 

Textural Analysis 

Nehalem Bank backscatter image documents an unusual 

phenomenon. 

Textural Analysis relies only on characteristic patterns of reflectance. 

Grey Level Co-occurrence Matrices (GLCM) 

– Used to calculate statistical indices 

Indices used in this study were: (based upon Cochrane & Lafferty, 2001; Diaz, 1999) 

– Homogeneity – How alike are the pixels within a neighborhood 

– Entropy – How ordered are the pixels within a neighborhood 


Textural Analysis Methods 


• Match submersible dive groundtruth data to the 

backscatter imagery (Overlaid on DEM) 

Submersible Dive Interpretation 

by Natalie Strom, (2006) 

• Each Habitat is characterized using a two 

letter code. The first letter represents > 50% 

of the field of view and the second letter must 

represent > 20%. 

•Each habitat section must occur for at least 

10 seconds of the video transect. 



Methods (cont.) 

Compute GLCM derived indices 

Create training sites 

Produce classification signatures 

Classify using the Maximum Likelihood 

clustering algorithm 


Strengths 

Rocky Ridges, Low Relief Mud, Mixed Class 

Weaknesses 

Shadows, Noise, Data Gaps Classified incorrectly 

Textural Analysis Results 

Strengths 

Rock Types Distinguished 

Weaknesses 

Low Relief Mud, Shadows 










– Results 









Topographic 

BTM 


Image 

DEM 

Qualitative 

Assessment 

Reference 

Points 


SIM 


Image 

Backscatter 

Backscatter 

Image 

Dive 


Textural 

Analysis 

Classified 

Image 

Qualitative 

Assessment 



Quantitative 

Assessment 

Result 

Assessment 


Discussion 

Data Set 

• Topographic Data 


–Results 

– Knowledge extent 

• Backscatter Data 


–Results 

– Knowledge extent 


Topographic 

Classification 

• Both methods 

– Semi-automated 

– Moderate success 

• Quality Assessments 

– Qualitative 

– Quantitative 

Discussion 


Backscatter Classification 

Discussion 

Dive interpretations 

– Correctly sited on landscape 

Textural Analysis 

– Using GLCM’s 

Mixed Results 

– worked well for Dive 3430 

– somewhat poorly for Dive 3431 

Sidescan imagery changes the 

picture, and our perception of 

the area 

Final interpretation 

– EM300 imagery is likely imaging 

subsurface seafloor characteristics 

EM300 Imagery AMS 150 Sidescan Imagery 


Conclusions 

Study Goals 

• Identify the limitations of the multibeam sonar data 

set. Use the Nehalem Bank study site to address the 

following questions: 

– 5m cell size resolution over the entire region, with ~ 2m 

cell size possible in shallow regions. 

• Questions addressed at beginning: 

– 1. Does the MBES data provide an accurate depiction of 

the seafloor topography? What artifacts remain and 

propagate into the classified images and data layers? 

• The multibeam imagery provided a detailed view of the 

landscape down to the Macroscale habitat features. 

• The artifacts that remain in the final image include nadir regions, 

swath overlap, turn artifacts. 


Conclusions 

Study Goals 

2. How do you assess the quality of a 

benthic classification attempt when 

groundtruthing information is at a 

different scale of observation? 

– Qualitative evaluation is possible 

– 3D GIS environment 

– Error matrices 

• can be used to iteratively evaluate 

the methods and results. 

– Limit the assessment to the areas 

where groundtruth information is 

available 


Future Implications and Research Directions 

For Sonar Image Classification 

– Automated classification a possibility? Not yet. 

– Provided a method for the assessment of topographic accuracy that can 

be used on any multibeam data set. 

For Marine Resource Management 

– The presence of the Nehalem Bank topographic classification and 

bathymetric data set will play a large role in the future investigations in 

benthic ecology by dictating the locations where the further exploration 

is needed. 

• Future investigations of EFH at a smaller scale is possible using the data 

sets produced from this study 

– Future Research Questions: 

• Where have the impacts of an activity like trawling been concentrated in 

the Nehalem Bank region? 

• Does the structural diversity of topographic habitats in the bank top region 

provide a natural refuge from those actions, and if so, what is the true 

population of fish in those areas? 


• Major Advisor: Chris Goldfinger 

• Committee Members: Dawn 

Wright, Waldo Wakefield, Skip 

Roechefort 

• Funding Agencies: 

NOAA NWFSC 

CIMRS 

NOAA Coastal Services Center 

• Active Tectonics and Seafloor 

Mapping Crew 

• Fellow COAS students 

• My Family 

• Fiancé: Jessica Cardinal 

Acknowledgments 


Misc Info 

• Error Matrix Statistics 


Benthic Classification 

Benthic habitat: is a seafloor region that can be characterized using the physical gradients of several 

variables, including depth, substrate, slope, roughness, and morphology, (may also include oceanographic 

factors such as temperature, salinity, nutrient flux, and biogenic content). 

Habitat Classification Schemes 

By Scale 

Using Nautical charts and existing 

geological maps (Greene, 2005) 

Towards Process 

An idealized scheme to reflect influence 

of environmental conditions (Valentine et 

al., 2004) 

Habitat classes were categorized into either 

of four groups: 

• megahabitats (10’s of km’s – 1km) 

• mesohabitats (1km - 10’s of meters) 

• macrohabitats (10’s of meters to 1m) 

• microhabitats (1m to 1cm). 

A hierarchy of attributes can be associated 

with a single seafloor region 

Their scheme recognized the eight 

themes of: 

– seabed topography 

– sediment dynamics 

– texture 

– grain size 

– Roughness 

– fauna and flora 

– habitat association and usage 

– habitat recovery from disturbance 

Those themes are further attributed by a 

sequence of modifiers that address habitat 

characteristics in increasing levels of detail 


Discussion 

Final Classification Scheme 

• Greene et al. (2005) scheme 

was chosen to define broad 

zones in the landscape. 

Based upon Mega-Habitat 

definitions. 

– Previous EFH maps use it 

– No process information is 

available (Valentine et al., 

2004) 

• Intersect the fine scale 

structures delineated using the 

BTM tool 


What are the benefits to classification of using a 3D perspective? Is it 

possible to quantitatively compare two different classifications of the 

data using observations in a 3D environment? 

Can the texture of multibeam sonar backscatter imagery be used to 

classify homogeneous regions of substrate on the seafloor given the 

Nehalem Bank data set and the available groundtruthing data 

sources? 


Study Goal 

3. What are the benefits to classification 

of using a 3D perspective? Is it 

possible to quantitatively compare two 

different classifications of the data 

using observations in a 3D 

environment? 

Conclusions 

Calculating error matrix statistics allows for 

the evaluation of each result individually, 

while also making it possible for comparison 

among different results. 

Dives were correctly placed in the landscape 

where they could be used to guide the 

textural classification of backscatter 

imagery. 

Use of the 3D environment helped to colocate 

different data sets on the bathymetry, 

while also providing an opportunity to 

perform quantitative assessments through the 

use of error matrices. 


4. Can the texture of multibeam sonar 

backscatter imagery be used to classify 

homogeneous regions of substrate on the 

seafloor given the Nehalem Bank data set 

and the avaailable groundtruthing data 

sources? 

– The presence of two sources of 

imagery helped to evaluate what 

was actually being imaged in the 

environment. 

Conclusions

A Comparison of Seafloor Classification Methods Through the Use ...

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