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Multiview deep learning for land-use classification
Citation
Luus, FPS and Salmon, BP and van den Bergh, F and Maharaj, BTJ, Multiview deep learning for land-use classification, IEEE Geoscience and Remote Sensing Letters, 12, (12) Article 7307121. ISSN 1545-598X (2015) [Refereed Article]
Copyright Statement
Copyright 2015 IEEE
DOI: doi:10.1109/LGRS.2015.2483680
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 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.
Item Details
Item Type: | Refereed Article |
---|---|
Keywords: | feature extraction, neural network applications, neural network architecture, remote sensing, urban areas |
Research Division: | Engineering |
Research Group: | Communications engineering |
Research Field: | Signal processing |
Objective Division: | Expanding Knowledge |
Objective Group: | Expanding knowledge |
Objective Field: | Expanding knowledge in engineering |
UTAS Author: | Salmon, BP (Dr Brian Salmon) |
ID Code: | 105817 |
Year Published: | 2015 |
Web of Science® Times Cited: | 180 |
Deposited By: | Engineering |
Deposited On: | 2016-01-15 |
Last Modified: | 2017-11-06 |
Downloads: | 0 |
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