Development of a machine learning technique for automatic analysis of seafloor image data: Case example,Pogonophora coverage at mud volcanoes


Contact
Kerstin.Jerosch [ at ] awi.de

Abstract

Digital image processing provides powerful tools for fast and precise analysis of large image data sets in marine and geoscientific applications. Because of the increasing volume of georeferenced image and video data acquired by underwater platforms such as remotely operated vehicles, means of automatic analysis of the acquired image data are required. A new and fast-developing application is the combination of video imagery and mosaicking techniques for seafloor habitat mapping. In this article we introduce an approach to fully automatic detection and quantification of Pogonophora coverage in seafloor video mosaics from mud volcanoes. The automatic recognition is based on textural image features extracted from the raw image data and classification using machine learning techniques. Classification rates of up to 98.86% were achieved on the training data. The approach was extensively validated on a data set of more than 4000 seafloor video mosaics from the Haakon Mosby Mud Volcano.



Item Type
Article
Authors
Divisions
Programs
Peer revision
ISI/Scopus peer-reviewed
Publication Status
Published
Eprint ID
31304
DOI 10.1016/j.cageo.2011.06.020

Cite as
Lüdtke, A. , Jerosch, K. , Herzog, O. and Schlüter, M. (2012): Development of a machine learning technique for automatic analysis of seafloor image data: Case example,Pogonophora coverage at mud volcanoes , Computers and Geosciences, 39 , pp. 120-128 . doi: 10.1016/j.cageo.2011.06.020


Download
[img]
Preview
PDF
Luedtke_Jeorsch_et_al_2012.pdf

Download (1MB) | Preview
Cite this document as:

Share


Citation

Research Platforms

Campaigns


Actions
Edit Item Edit Item