The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection
Abstract
Change detection (CD) aims to identify surface changes from bitemporal images. In recent years, deep learning (DL)-based methods have made substantial breakthroughs in the field of CD. However, CD results can be easily affected by external factors, including illumination, noise, and scale, which leads to pseudo-changes and noise in the detection map. To deal with these problems and achieve more accurate results, a deeply supervised (DS) attention metric-based network (DSAMNet) is proposed in this article. A metric module is employed in DSAMNet to learn change maps by means of deep metric learning, in which convolutional block attention modules (CBAM) are integrated to provide more discriminative features. As an auxiliary, a DS module is introduced to enhance the feature extractor’s learning ability and generate more useful features. Moreover, another challenge encountered by data-driven DL algorithms is posed by the limitations in change detection datasets (CDDs). Therefore, we create a CD dataset, Sun Yat-Sen University (SYSU)-CD, for bitemporal image CD, which contains a total of 20 000 aerial image pairs of size$256\times256$. Experiments are conducted on both the CDD and the SYSU-CD dataset. Compared to other state-of-the-art methods, our network achieves the highest accuracy on both datasets, with an F1 of 93.69% on the CDD dataset and 78.18% on the SYSU-CD dataset.
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Cites
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Cited by
Liu, M., Chai, Z., Deng, H., & Liu, R. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3177235 · Cited by 409
Self-citationHan, C., Wu, C., Guo, H., Hu, M., & Chen, H. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3264802 · Cited by 259
Li, W., & Hsu, C.-Y. (2022) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi11070385 · Cited by 147
MSMatch: Semisupervised Multispectral Scene Classification With Few Labels
Gomez, P., & Meoni, G. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3126082 · Cited by 55
Metadata
- Authors
- Shi, Q., Liu, M., Li, S., Liu, X., Wang, F., & Zhang, L.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2022
- Volume
- 60
- Pages
- 1-16
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W3176330035
- Cited by
- 698
- References
- 49
- FWCI
- 45.49
- Type
- journal article
- Language
- en
- Open access
- closed
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- Shi, Q., Liu, M., Li, S., Liu, X., Wang, F., & Zhang, L. (2022). A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/tgrs.2021.3085870
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