@inproceedings{ee287a4b00634ad09b242b347d843633,
title = "Multi-scale Features Fusion Network for Unsupervised Change Detection in Heterogeneous Optical and SAR Images",
abstract = "Change detection (CD) in heterogeneous remote sensing image applications has become an issue of increasing concern in, as they cannot be compared directly with traditional homogenous CD methods. To solve feature loss problem and generating better representations to accommodate regions of various sizes in heterogeneous images CD, a multi-scale features fusion network (MFFN) is proposed. Firstly, multi-scale representative deep features can be extracted to distinguish difference in high-dimension feature space. Then, hierarchical features from the original image pairs can be fuse to generate a difference image with more explicit semantic information owing to the strategy of multi-scale features fusion, which can better adapt different scale of changes in heterogeneous remote sensing images. It is noteworthy that the experimental results on both heterogeneous and homogeneous data set confirm the effectiveness of the proposed method.",
keywords = "Change detection, Heterogeneous images, Multi-scale feature fusion, Neural network",
author = "Jiao Shi and Zeping Zhang and Tancheng Wu and Xiaoyang Li and Deyun Zhou and Yu Lei",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021 ; Conference date: 07-11-2021 Through 08-11-2021",
year = "2021",
doi = "10.1109/CCIS53392.2021.9754667",
language = "英语",
series = "Proceedings of 2021 7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "270--274",
editor = "Deyi Li and Mengqi Zhou and Weining Wang and Yaru Zou and Meng Luo and Qian Zhang",
booktitle = "Proceedings of 2021 7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021",
}