Affiliation:
1. Department of Computer Engineering, Kwangwoon University, Seoul 139701, Republic of Korea
Abstract
This study proposes a bilateral attention U-Net with a dissimilarity attention gate (DAG) for change detection on remote sensing imageries. The proposed network is designed with a bilateral dissimilarity encoding for the DAG calculation to handle reversible input images, resulting in high detection rates regardless of the order of the two input images for change detection. The DAG exploits all the combinations of joint features to avoid spectral information loss fed into an attention gate on the decoder side. The effectiveness of the proposed method was evaluated on the KOMPSAT-3 satellite images dataset and the aerial change detection dataset (CDD). Its performance was better than that of conventional methods (specifically, U-Net, ATTUNet, and Modified-UNet++) as it achieved average F1-score and kappa coefficient (KC) values of 0.68 and 66.93, respectively, for the KOMPSAT-3 dataset. For CDD, it achieved F1-score and KC values of 0.70 and 68.74, respectively, which are also better values than those achieved by conventional methods. In addition, we found that the proposed bilateral attention U-Net can provide the same changed map regardless of whether the image order is reversed.
Funder
Ministry of Science and Technology Infor-mation and Communication
Kwangwoon University in 2021 and the MSIT (Ministry of Science and ICT), Korea
IITP(Institute of Information & Communications Technology Planning & Evaluation
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference33 articles.
1. Singh, K.K., Mehrotra, A., Nigam, M.J., and Pal, K. (2013, January 12–14). Unsupervised change detection from remote sensing images using hybrid genetic FCM. Proceedings of the 2013 Students Conference on Engineering and Systems (SCES), Allahabad, India.
2. Bi, C., Wang, H., and Bao, R. (2014, January 27–29). SAR image change detection using regularized dictionary learning and fuzzy clustering. Proceedings of the 2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems, Shenzhen, China.
3. Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering;Gong;IEEE Trans. Image Process.,2012
4. Fuzzy clustering with a modified MRF energy function for change detection in synthetic aperture radar images;Gong;IEEE Trans. Fuzzy Syst.,2013
5. Unsupervised change detection using fuzzy c-means and MRF from remotely sensed images;Hao;Remote Sens. Lett.,2013
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Enhancing Change Detection in Spectral Images: Integration of UNet and ResNet Classifiers;2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI);2023-11-06