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 Type:	Refereed Article
Keywords:	feature extraction, neural network applications, neural network architecture, remote sensing, urban areas
Research Division:	Engineering
Research Group:	Electrical and Electronic 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:	73
Deposited By:	Engineering
Deposited On:	2016-01-15
Last Modified:	2017-11-06
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