DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images
Change detection is a basic task of remote sensing image processing. The research objective is to identify the change information of interest and filter out the irrelevant change information as interference factors. Recently, the rise in deep learning has provided new tools for change detection, whi...
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| Veröffentlicht in: | IEEE journal of selected topics in applied earth observations and remote sensing Jg. 14; S. 1194 - 1206 |
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| Hauptverfasser: | , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Piscataway
IEEE
2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Schlagworte: | |
| ISSN: | 1939-1404, 2151-1535 |
| Online-Zugang: | Volltext |
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| Abstract | Change detection is a basic task of remote sensing image processing. The research objective is to identify the change information of interest and filter out the irrelevant change information as interference factors. Recently, the rise in deep learning has provided new tools for change detection, which have yielded impressive results. However, the available methods focus mainly on the difference information between multitemporal remote sensing images and lack robustness to pseudochange information. To overcome the lack of resistance in current methods to pseudochanges, in this article, we propose a new method, namely, dual attentive fully convolutional Siamese networks, for change detection in high-resolution images. Through the dual attention mechanism, long-range dependencies are captured to obtain more discriminant feature representations to enhance the recognition performance of the model. Moreover, the imbalanced sample is a serious problem in change detection, i.e., unchanged samples are much more abundant than changed samples, which is one of the main reasons for pseudochanges. We propose the weighted double-margin contrastive loss to address this problem by punishing attention to unchanged feature pairs and increasing attention to changed feature pairs. The experimental results of our method on the change detection dataset and the building change detection dataset demonstrate that compared with other baseline methods, the proposed method realizes maximum improvements of 2.9% and 4.2%, respectively, in the F1 score. Our PyTorch implementation is available at https://github.com/lehaifeng/DASNet. |
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| AbstractList | Change detection is a basic task of remote sensing image processing. The research objective is to identify the change information of interest and filter out the irrelevant change information as interference factors. Recently, the rise in deep learning has provided new tools for change detection, which have yielded impressive results. However, the available methods focus mainly on the difference information between multitemporal remote sensing images and lack robustness to pseudochange information. To overcome the lack of resistance in current methods to pseudochanges, in this article, we propose a new method, namely, dual attentive fully convolutional Siamese networks, for change detection in high-resolution images. Through the dual attention mechanism, long-range dependencies are captured to obtain more discriminant feature representations to enhance the recognition performance of the model. Moreover, the imbalanced sample is a serious problem in change detection, i.e., unchanged samples are much more abundant than changed samples, which is one of the main reasons for pseudochanges. We propose the weighted double-margin contrastive loss to address this problem by punishing attention to unchanged feature pairs and increasing attention to changed feature pairs. The experimental results of our method on the change detection dataset and the building change detection dataset demonstrate that compared with other baseline methods, the proposed method realizes maximum improvements of 2.9% and 4.2%, respectively, in the F 1 score. Our PyTorch implementation is available at https://github.com/lehaifeng/DASNet . Change detection is a basic task of remote sensing image processing. The research objective is to identify the change information of interest and filter out the irrelevant change information as interference factors. Recently, the rise in deep learning has provided new tools for change detection, which have yielded impressive results. However, the available methods focus mainly on the difference information between multitemporal remote sensing images and lack robustness to pseudochange information. To overcome the lack of resistance in current methods to pseudochanges, in this article, we propose a new method, namely, dual attentive fully convolutional Siamese networks, for change detection in high-resolution images. Through the dual attention mechanism, long-range dependencies are captured to obtain more discriminant feature representations to enhance the recognition performance of the model. Moreover, the imbalanced sample is a serious problem in change detection, i.e., unchanged samples are much more abundant than changed samples, which is one of the main reasons for pseudochanges. We propose the weighted double-margin contrastive loss to address this problem by punishing attention to unchanged feature pairs and increasing attention to changed feature pairs. The experimental results of our method on the change detection dataset and the building change detection dataset demonstrate that compared with other baseline methods, the proposed method realizes maximum improvements of 2.9% and 4.2%, respectively, in the F1 score. Our PyTorch implementation is available at https://github.com/lehaifeng/DASNet. |
| Author | Liu, Yu Li, Haifeng Chen, Jie Chen, Li Yuan, Ziyang Huang, Haozhe Zhu, Jiawei Peng, Jian |
| Author_xml | – sequence: 1 givenname: Jie orcidid: 0000-0002-3864-9265 surname: Chen fullname: Chen, Jie email: cj2011@csu.edu.cn organization: School of Geosciences and Info-Physics, Central South University, Changsha, China – sequence: 2 givenname: Ziyang surname: Yuan fullname: Yuan, Ziyang email: yuanziyang@csu.edu.cn organization: School of Geosciences and Info-Physics, Central South University, Changsha, China – sequence: 3 givenname: Jian surname: Peng fullname: Peng, Jian email: PengJ2017@csu.edu.cn organization: School of Geosciences and Info-Physics, Central South University, Changsha, China – sequence: 4 givenname: Li surname: Chen fullname: Chen, Li email: vchenlil@csu.edu.cn organization: School of Geosciences and Info-Physics, Central South University, Changsha, China – sequence: 5 givenname: Haozhe surname: Huang fullname: Huang, Haozhe email: hz_huang@csu.edu.cn organization: School of Geosciences and Info-Physics, Central South University, Changsha, China – sequence: 6 givenname: Jiawei surname: Zhu fullname: Zhu, Jiawei email: jw_zhu@csu.edu.cn organization: School of Geosciences and Info-Physics, Central South University, Changsha, China – sequence: 7 givenname: Yu orcidid: 0000-0002-3914-1252 surname: Liu fullname: Liu, Yu email: jasonyuliu@nudt.edu.cn organization: Department of Systems Engineering, National University of Defense Technology, Changsha, China – sequence: 8 givenname: Haifeng orcidid: 0000-0003-1173-6593 surname: Li fullname: Li, Haifeng email: lihaifeng@csu.edu.cn organization: School of Geosciences and Info-Physics, Central South University, Changsha, China |
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| SubjectTerms | Change detection Data mining Datasets Deep learning Detection dual attention Feature extraction High resolution high-resolution images Image processing Image resolution Methods Remote sensing Resolution Robustness Satellite imagery Siamese network Spaceborne remote sensing Task analysis Wavelength division multiplexing weighted double-margin contrastive (WDMC) loss |
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| Title | DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images |
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