Multiview deep learning for land-use classification
dc.contributor.author | Luus, Francois Pierre Sarel | |
dc.contributor.author | Salmon, Brian Paxton | |
dc.contributor.author | Van den Bergh, Frans | |
dc.contributor.author | Maharaj, Bodhaswar Tikanath Jugpershad | |
dc.date.accessioned | 2016-02-10T08:30:27Z | |
dc.date.available | 2016-02-10T08:30:27Z | |
dc.date.issued | 2015-12 | |
dc.description.abstract | A 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.librarian | hb2015 | en_ZA |
dc.description.sponsorship | National Research Foundation (NRF) of South Africa | en_ZA |
dc.description.uri | http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8859 | en_ZA |
dc.identifier.citation | Luus, 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.issn | 1545-598X (print) | |
dc.identifier.issn | 1558-0571 (online) | |
dc.identifier.other | 10.1109/LGRS.2015.2483680 | |
dc.identifier.uri | http://hdl.handle.net/2263/51310 | |
dc.language.iso | en | en_ZA |
dc.publisher | Institute of Electrical and Electronics Engineers | en_ZA |
dc.rights | © 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. | en_ZA |
dc.subject | Neural network applications | en_ZA |
dc.subject | Neural network architecture | en_ZA |
dc.subject | Feature extraction | en_ZA |
dc.subject | Urban areas | en_ZA |
dc.subject | Remote sensing | en_ZA |
dc.title | Multiview deep learning for land-use classification | en_ZA |
dc.type | Postprint Article | en_ZA |