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Multisensor approach to automated classification of sea ice image data

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dc.contributor.author Bogdanov, Andrey V. eng
dc.contributor.author Alexandrov, Vitaly Y. eng
dc.contributor.author Sandven, Stein eng
dc.contributor.author Johannessen, Ola M. eng
dc.contributor.author Bobylev, Leonid P. eng
dc.date.accessioned 2005-09-12T11:44:12Z
dc.date.available 2005-09-12T11:44:12Z
dc.date.issued 2005-07 eng
dc.identifier.citation IEEE Transactions on Geoscience and Remote Sensing 43(7): 1648-1664 en
dc.identifier.issn 0196-2892 eng
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 eng
dc.format.mimetype application/pdf eng
dc.language.iso eng eng
dc.publisher IEEE Geoscience and Remote Sensing Society eng
dc.subject Geoscience eng
dc.subject Remote sensing eng
dc.title Multisensor approach to automated classification of sea ice image data eng
dc.type Journal article eng
dc.rights.holder Copyright 2005 IEEE
dc.type.version publishedVersion eng
bora.peerreviewed Peer reviewed eng
bora.journalTitle IEEE Transactions on Geoscience and Remote Sensing eng
bibo.volume 43 eng
bibo.issue 7 eng
bibo.pageStart 1648 eng
bibo.pageEnd 1664 eng
bibo.doi http://dx.doi.org/10.1109/TGRS.2005.846882 eng
dc.identifier.doi http://dx.doi.org/10.1109/TGRS.2005.846882


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