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Multiview deep learning for land-use classification
journal contribution
posted on 2023-05-18, 15:55 authored by Luus, FPS, Brian SalmonBrian Salmon, van den Bergh, F, Maharaj, BTJA multiscale input strategy for multiview deep learning is proposed for supervised multispectral land-use classification, and it is validated on a well-known data set. The hypothesis that simultaneous multiscale views can improve composition-based 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 (DCNN). This allows the classifier to obtain problem-specific features that are optimal for minimizing the multinomial logistic regression objective, as opposed to user-defined features which trade optimality for generality. A heuristic approach to the optimization of the DCNN hyperparameters is used, based on empirical performance evidence. It is shown that a single DCNN can be trained simultaneously with multiscale views to improve prediction accuracy over multiple single-scale views. Competitive performance is achieved for the UC Merced data set, 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.
History
Publication title
IEEE Geoscience and Remote Sensing LettersVolume
12Issue
12Article number
7307121Number
7307121Pagination
2448-2452ISSN
1545-598XDepartment/School
School of EngineeringPublisher
IEEE-Inst Electrical Electronics Engineers IncPlace of publication
United StatesRights statement
Copyright 2015 IEEERepository Status
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