Change detection in multitemporal synthetic aperture radar images using dual-channel convolutional neural network

Tao Liu; Ying Li; Ying Cao; Qiang Shen

doi:10.1117/1.JRS.11.042615

12 October 2017 Change detection in multitemporal synthetic aperture radar images using dual-channel convolutional neural network

Tao Liu, Ying Li, Ying Cao, Qiang Shen

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 4, 042615 (October 2017). https://doi.org/10.1117/1.JRS.11.042615

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.

Citation Download Citation

Tao Liu, Ying Li, Ying Cao, and Qiang Shen "Change detection in multitemporal synthetic aperture radar images using dual-channel convolutional neural network," Journal of Applied Remote Sensing 11(4), 042615 (12 October 2017). https://doi.org/10.1117/1.JRS.11.042615

Received: 31 March 2017; Accepted: 22 September 2017; Published: 12 October 2017

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