Paper Info
Title
A new approach to the automated mapping of pockmarks in multi-beam bathymetry
Abstract
Seabed pockmarks are of great interest to geologists and the marine geotechnical community. Identifying and mapping pockmarks rendered in multi-beam bathymetry data is an important but expensive manual process. In this paper, a new Machine Learning approach to automating the task is presented. Useful, low-dimensional feature vectors yielding very good classification accuracies are established. Overall process efficacy is subsequently evaluated by comparing counts of individual objects identified by the machine and a human analyst. Highest agreement (96.7%) occurs where there is a strong visual contrast between the pockmarks and the surrounding terrain. In low-contrast areas, our machine approach identifies several more objects than the human. Further, our process maps the boundaries of ≈ 2000 pockmarks in seconds - a task which would take days for a human to complete.
Year
DOI
Venue
2011
10.1109/ICIP.2011.6116246
Image Processing
Keywords
Field
DocType
bathymetry,geophysics computing,image recognition,learning (artificial intelligence),human analyst,low dimensional feature vector,machine learning,multibeam bathymetry,pockmark automated mapping,seabed pockmarks,Ball Vector Machine,Feature selection,Filter,Wrapper
Computer vision,Feature vector,Visual contrast,Seabed,Pattern recognition,Computer science,Terrain,Bathymetry,Artificial intelligence
Conference
ISSN
ISBN
Citations 
1522-4880 E-ISBN : 978-1-4577-1302-6
978-1-4577-1302-6
0
PageRank 
References 
Authors
0.34
2
4
Name
Order
Citations
PageRank
Richard Harrison100.34
Valerie Bellec200.34
Dave Mann300.34
Wenjia Wang4579.12