Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77445
DC FieldValueLanguage
dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorLiang, P-
dc.creatorShi, W-
dc.creatorZhang, X-
dc.date.accessioned2018-08-28T01:32:23Z-
dc.date.available2018-08-28T01:32:23Z-
dc.identifier.urihttp://hdl.handle.net/10397/77445-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Liang, P., Shi, W., & Zhang, X. (2018). Remote sensing image classification based on stacked denoising autoencoder. Remote Sensing, 10(1), (Suppl. ), 16, - is available athttps://dx.doi.org/10.3390/rs10010016en_US
dc.subjectBack Propagation neural networken_US
dc.subjectDeep learningen_US
dc.subjectLand cover classificationen_US
dc.subjectStacked denoising autoencoderen_US
dc.titleRemote sensing image classification based on stacked denoising autoencoderen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume10-
dc.identifier.issue1-
dc.identifier.doi10.3390/rs10010016-
dcterms.abstractFocused on the issue that conventional remote sensing image classification methods have run into the bottlenecks in accuracy, a new remote sensing image classification method inspired by deep learning is proposed, which is based on Stacked Denoising Autoencoder. First, the deep network model is built through the stacked layers of Denoising Autoencoder. Then, with noised input, the unsupervised Greedy layer-wise training algorithm is used to train each layer in turn for more robust expressing, characteristics are obtained in supervised learning by Back Propagation (BP) neural network, and the whole network is optimized by error back propagation. Finally, Gaofen-1 satellite (GF-1) remote sensing data are used for evaluation, and the total accuracy and kappa accuracy reach 95.7% and 0.955, respectively, which are higher than that of the Support Vector Machine and Back Propagation neural network. The experiment results show that the proposed method can effectively improve the accuracy of remote sensing image classification.-
dcterms.bibliographicCitationRemote sensing, Jan. 2018, v. 10, no. 1, 16, p. 1-12-
dcterms.isPartOfRemote sensing-
dcterms.issued2018-
dc.identifier.isiWOS:000424092300015-
dc.identifier.scopus2-s2.0-85040666806-
dc.identifier.eissn2072-4292-
dc.identifier.artn16-
dc.identifier.rosgroupid2017002522-
dc.description.ros2017-2018 > Academic research: refereed > Publication in refereed journal-
dc.description.validate201808 bcrc-
dc.description.oapublished_final-
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