@inproceedings{ecdd8f6a688c4751bef10a119ae708e8,
title = "Dual-channel convolutional neural network for change detection of multitemporal SAR images",
abstract = "This paper presents a new dual-channel convolutional neural network (CNN) which is designed to SAR image change detection to acquire higher detection accuracy and lower misclassification rate. This network model contains two parallel CNN structures, which can extract features from two multitemporal SAR images. Experimental evaluation on simulated datasets and real SAR images from different satellites shows a satisfying performance of the proposed model. It is the second-place winner when detecting the simulated images with Gamma noise, but it wins the top place when detecting the real flood images.",
keywords = "Change detection, Dual-channel CNN, SAR image",
author = "Tao Liu and Ying Li and Longhao Xu",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 International Conference on Orange Technologies, ICOT 2016 ; Conference date: 18-12-2016 Through 20-12-2016",
year = "2016",
month = jul,
day = "2",
doi = "10.1109/ICOT.2016.8278979",
language = "英语",
series = "2016 International Conference on Orange Technologies, ICOT 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "60--63",
booktitle = "2016 International Conference on Orange Technologies, ICOT 2016",
}