Multiview deep learning for land-use classification

dc.contributor.authorLuus, Francois Pierre Sarel
dc.contributor.authorSalmon, Brian Paxton
dc.contributor.authorVan den Bergh, Frans
dc.contributor.authorMaharaj, Bodhaswar Tikanath Jugpershad
dc.date.accessioned2016-02-10T08:30:27Z
dc.date.available2016-02-10T08:30:27Z
dc.date.issued2015-12
dc.description.abstractA multiscale input strategy for multiview deep learning is proposed for supervised multispectral land-use classification and it is validated on a well-known dataset. The hypothesis that simultaneous multiscale views can improve compositionbased inference of classes containing size-varying objects compared to single-scale multiview is investigated. The end-to-end learning system learns a hierarchical feature representation with the aid of convolutional layers to shift the burden of feature determination from hand-engineering to a deep convolutional neural network. This allows the classifier to obtain problemspecific features that are optimal for minimizing the multinomial logistic regression objective, as opposed to user-defined features which trades optimality for generality. A heuristic approach to the optimization of the deep convolutional neural network hyperparameters is used, based on empirical performance evidence. It is shown that a single deep convolutional neural network can be trained simultaneously with multiscale views to improve prediction accuracy over multiple single-scale views. Competitive performance is achieved for the UC Merced dataset where the 93.48% accuracy of multiview deep learning outperforms the 85.37% accuracy of SIFT-based methods and the 90.26% accuracy of unsupervised feature learning.en_ZA
dc.description.librarianhb2015en_ZA
dc.description.sponsorshipNational Research Foundation (NRF) of South Africaen_ZA
dc.description.urihttp://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8859en_ZA
dc.identifier.citationLuus, FPS, Salmon, BP, Van Den Bergh, F & Maharaj, BTJ 2015, 'Multiview deep learning for land-use classification', IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 12, pp. 2448-2452.en_ZA
dc.identifier.issn1545-598X (print)
dc.identifier.issn1558-0571 (online)
dc.identifier.other10.1109/LGRS.2015.2483680
dc.identifier.urihttp://hdl.handle.net/2263/51310
dc.language.isoenen_ZA
dc.publisherInstitute of Electrical and Electronics Engineersen_ZA
dc.rights© 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.en_ZA
dc.subjectNeural network applicationsen_ZA
dc.subjectNeural network architectureen_ZA
dc.subjectFeature extractionen_ZA
dc.subjectUrban areasen_ZA
dc.subjectRemote sensingen_ZA
dc.titleMultiview deep learning for land-use classificationen_ZA
dc.typePostprint Articleen_ZA

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