A comparison of supervised classification methods for the prediction of substrate type using multibeam acoustic and legacy grain-size data.

ABSTRACT: Detailed seabed substrate maps are increasingly in demand for effective planning and management of marine ecosystems and resources. It has become common to use remotely sensed multibeam echosounder data in the form of bathymetry and acoustic backscatter in conjunction with ground-truth sampling data to inform the mapping of seabed substrates. Whilst, until recently, such data sets have typically been classified by expert interpretation, it is now obvious that more objective, faster and repeatable methods of seabed classification are required. This study compares the performances of a range of supervised classification techniques for predicting substrate type from multibeam echosounder data. The study area is located in the North Sea, off the north-east coast of England. A total of 258 grou

SUBMITTER: Stephens D 

PROVIDER: S-EPMC3974812 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature