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Journal of Applied Remote Sensing

Change detection in multitemporal synthetic aperture radar images using dual-channel convolutional neural network
Author(s): Tao Liu; Ying Li; Ying Cao; Qiang Shen
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Paper Abstract

This paper proposes a model of dual-channel convolutional neural network (CNN) that is designed for change detection in SAR images, in an effort to acquire higher detection accuracy and lower misclassification rate. This network model contains two parallel CNN channels, which can extract deep features from two multitemporal SAR images. For comparison and validation, the proposed method is tested along with other change detection algorithms on both simulated SAR images and real-world SAR images captured by different sensors. The experimental results demonstrate that the presented method outperforms the state-of-the-art techniques by a considerable margin.

Paper Details

Date Published: 12 October 2017
PDF: 13 pages
J. Appl. Rem. Sens. 11(4) 042615 doi: 10.1117/1.JRS.11.042615
Published in: Journal of Applied Remote Sensing Volume 11, Issue 4
Show Author Affiliations
Tao Liu, Northwestern Polytechnical Univ. (China)
Ying Li, Northwestern Polytechnical Univ. (China)
Ying Cao, Northwestern Polytechnical Univ. (China)
Qiang Shen, Aberystwyth Univ. (United Kingdom)

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