@article{1517db08b9dd440584c2614366acd2fa,
title = "Change detection in multitemporal synthetic aperture radar images using dual-channel convolutional neural network",
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",
keywords = "SAR image, change detection, deep learning, dual-channel convolutional neural network",
author = "Tao Liu and Ying Li and Ying Cao and Qiang Shen",
note = "Funding Information: This work was supported by the National Key Research and Development Program of China (Grant No. 2016YFB0502502), Foundation Project for Advanced Research Field (Grant No. 614023804016HK03002), and Shannxi International Scientific and Technological Cooperation Project (Grant No. 2017KW-006). The authors are grateful to the editor and reviewers for their constructive comments that have helped to improve this work significantly. All the authors made significant contributions to this work. Professor Ying Li and Tao Liu devised the approach and analyzed the data; Professor Qiang Shen helped design the experiments and provided advice for the preparation and revision of the work; Ying Cao performed the experiments and provided detailed revisions for this work especially in the training of DC-CNN. Publisher Copyright: {\textcopyright} 2017 Society of Photo-Optical Instrumentation Engineers (SPIE).",
year = "2017",
month = oct,
day = "12",
doi = "10.1117/1.JRS.11.042615",
language = "English",
volume = "11",
journal = "Journal of Applied Remote Sensing",
issn = "1931-3195",
publisher = "SPIE",
number = "4",
}