BORA - UiB

Bergen Open Research Archive

Multisensor approach to automated classification of sea ice image data

Bergen Open Research Archive

Show simple item record

dc.contributor.author Bogdanov, Andrey V.
dc.contributor.author Alexandrov, Vitaly Y.
dc.contributor.author Sandven, Stein
dc.contributor.author Johannessen, Ola M.
dc.contributor.author Bobylev, Leonid P.
dc.date.accessioned 2005-09-12T11:44:12Z
dc.date.available 2005-09-12T11:44:12Z
dc.date.issued 2005-07
dc.identifier.citation IEEE Transactions on Geoscience and Remote Sensing 43(7): 1648-1664 en
dc.identifier.issn 0196-2892
dc.identifier.uri http://dx.doi.org/10.1109/TGRS.2005.846882
dc.identifier.uri http://hdl.handle.net/1956/787
dc.description.abstract A multisensor data fusion algorithm based on a multilayer neural network is presented for sea ice classification in the winter period. The algorithm uses European Remote Sensing (ERS), RADARSAT synthetic aperture radar (SAR), and low-resolution television camera images and image texture features. Based on a set of in situ observations made at the Kara Sea, a neural network is trained, and its structure is optimized using a pruning method. The performance of the algorithm with different combinations of input features (sensors) is assessed and compared with the performance of a linear discriminant analysis (LDA)-based algorithm. We show that for both algorithms a substantial improvement can be gained by fusion of the three different types of data (91.2% for the neural network) as compared with single-source ERS (66.0%) and RADARSAT (70.7%) SAR image classification. Incorporation of texture increases classification accuracy. This positive effect of texture becomes weaker with increasing number of sensors (from 8.4 to 6.4 percent points for the use of two and three sensors, respectively). In view of the short training time and smaller number of adjustable parameters, this result suggests that semiparametric classification methods can be considered as a good alternative to the neural networks and traditional parametric statistical classifiers applied for the sea ice classification. en
dc.format.extent 2417340 bytes
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher IEEE Geoscience and Remote Sensing Society en
dc.rights Copyright 2005 IEEE
dc.subject Geoscience en
dc.subject Remote sensing en
dc.title Multisensor approach to automated classification of sea ice image data en
dc.type Journal article en
dc.type Peer reviewed
dc.type.version publishedVersion
bora.peerreviewed Peer reviewed
bora.journalTitle IEEE Transactions on Geoscience and Remote Sensing
bibo.volume 43
bibo.issue 7
bibo.pageStart 1648
bibo.pageEnd 1664


Files in this item

 

This item appears in the following Collection(s)

Show simple item record

Search BORA


Browse

My Account