Show simple item record

dc.contributor.authorBogdanov, Andrey V.eng
dc.contributor.authorAlexandrov, Vitaly Y.eng
dc.contributor.authorSandven, Steineng
dc.contributor.authorJohannessen, Ola M.eng
dc.contributor.authorBobylev, Leonid P.eng
dc.date.accessioned2005-09-12T11:44:12Z
dc.date.available2005-09-12T11:44:12Z
dc.date.issued2005-07eng
dc.identifier.citationIEEE Transactions on Geoscience and Remote Sensing 43(7): 1648-1664en
dc.identifier.issn0196-2892eng
dc.identifier.urihttp://hdl.handle.net/1956/787
dc.description.abstractA 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.extent2417340 byteseng
dc.format.mimetypeapplication/pdfeng
dc.language.isoengeng
dc.publisherIEEE Geoscience and Remote Sensing Societyeng
dc.subjectGeoscienceeng
dc.subjectRemote sensingeng
dc.titleMultisensor approach to automated classification of sea ice image dataeng
dc.typeJournal articleeng
dc.rights.holderCopyright 2005 IEEE
dc.type.versionpublishedVersioneng
bora.peerreviewedPeer reviewedeng
bora.journalTitleIEEE Transactions on Geoscience and Remote Sensingeng
bibo.volume43eng
bibo.issue7eng
bibo.pageStart1648eng
bibo.pageEnd1664eng
bibo.doihttp://dx.doi.org/10.1109/TGRS.2005.846882eng
dc.identifier.doihttp://dx.doi.org/10.1109/TGRS.2005.846882


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record