The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection
Open accessAbstract
Remote sensing image change detection (CD) is done to identify desired significant changes between bitemporal images. Given two co-registered images taken at different times, the illumination variations and misregistration errors overwhelm the real object changes. Exploring the relationships among different spatial–temporal pixels may improve the performances of CD methods. In our work, we propose a novel Siamese-based spatial–temporal attention neural network. In contrast to previous methods that separately encode the bitemporal images without referring to any useful spatial–temporal dependency, we design a CD self-attention mechanism to model the spatial–temporal relationships. We integrate a new CD self-attention module in the procedure of feature extraction. Our self-attention module calculates the attention weights between any two pixels at different times and positions and uses them to generate more discriminative features. Considering that the object may have different scales, we partition the image into multi-scale subregions and introduce the self-attention in each subregion. In this way, we could capture spatial–temporal dependencies at various scales, thereby generating better representations to accommodate objects of various sizes. We also introduce a CD dataset LEVIR-CD, which is two orders of magnitude larger than other public datasets of this field. LEVIR-CD consists of a large set of bitemporal Google Earth images, with 637 image pairs (1024 × 1024) and over 31 k independently labeled change instances. Our proposed attention module improves the F1-score of our baseline model from 83.9 to 87.3 with acceptable computational overhead. Experimental results on a public remote sensing image CD dataset show our method outperforms several other state-of-the-art methods.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631
Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.660 · Cited by 16,438
A feature-integration theory of attention.
Treisman, A. M., & Gelade, G. (1980) · Cognitive Psychology · doi:10.1016/0010-0285(80)90005-5 · Cited by 12,366
Wang, X., Girshick, R., Gupta, A., & He, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00813 · Cited by 11,596 · cited 3×
“The non-local neural networks [35] extended the self-attention mechanism in many computer vision tasks, such as video classification, object detection and pose estimation.”
BackgroundResultInfluential
Research works printed but not linked: 21
- Huang, 2013, Building change detection from multitemporal high-resolution remotely sensed images based on a morphological building index, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 7, 105, 10.1109/JSTARS.2013.2252423 · doi:10.1109/jstars.2013.2252423
- Mahdavi, S., Salehi, B., Huang, W., Amani, M., and Brisco, B. (2019). A PolSAR Change Detection Index Based on Neighborhood Information for Flood Mapping. Remote Sens., 11. · doi:10.3390/rs11161854
- Chen, 2013, Multi-Decadal Mangrove Forest Change Detection and Prediction in Honduras, Central America, with Landsat Imagery and a Markov Chain Model, Remote Sens., 5, 6408, 10.3390/rs5126408 · doi:10.3390/rs5126408
- Liu, B., Chen, J., Chen, J., and Zhang, W. (2018). Land Cover Change Detection Using Multiple Shape Parameters of Spectral and NDVI Curves. Remote Sens., 10. · doi:10.3390/rs10081251
- Johnson, 1998, Change vector analysis: A technique for the multispectral monitoring of land cover and condition, Int. J. Remote Sens., 19, 411, 10.1080/014311698216062 · doi:10.1080/014311698216062
- Gapper, J.J., El-Askary, H.M., Linstead, E., and Piechota, T. (2019). Coral Reef Change Detection in Remote Pacific Islands Using Support Vector Machine Classifiers. Remote Sens., 11. · doi:10.3390/rs11131525
- Kasetkasem, 2002, An image change detection algorithm based on Markov random field models, IEEE Trans. Geosci. Remote Sens., 40, 1815, 10.1109/TGRS.2002.802498 · doi:10.1109/tgrs.2002.802498
- Zong, 2019, Building change detection from remotely sensed images based on spatial domain analysis and Markov random field, J. Appl. Remote Sens., 13, 1, 10.1117/1.JRS.13.024514 · doi:10.1117/1.jrs.13.024514
- Malpica, 2013, Change detection of buildings from satellite imagery and lidar data, Int. J. Remote Sens., 34, 1652, 10.1080/01431161.2012.725483 · doi:10.1080/01431161.2012.725483
- Bachofer, F., Braun, A., Adamietz, F., Murray, S., d’Angelo, P., Kyazze, E., Mumuhire, A.P., and Bower, J. (2019). Building Stock and Building Typology of Kigali, Rwanda. Int. Conf. Data Technol. Appl., 4. · doi:10.3390/data4030105
- Liu, Y., Pang, C., Zhan, Z., Zhang, X., and Yang, X. (2019). Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Model. arXiv.
- He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, CA, USA.
- Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv.
- Castelluccio, M., Poggi, G., Sansone, C., and Verdoliva, L. (2015). Land use classification in remote sensing images by convolutional neural networks. arXiv.
- Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv.
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, MIT Press.
- Chen, 2012, A semisupervised context-sensitive change detection technique via gaussian process, IEEE Geosci. Remote Sens. Lett., 10, 236, 10.1109/LGRS.2012.2199279 · doi:10.1109/lgrs.2012.2199279
- Qingqing, H., Yu, M., Jingbo, C., Anzhi, Y., and Lei, L. (2017, January 23–28). Landslide change detection based on spatio-temporal context. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA. · doi:10.1109/igarss.2017.8127147
- Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (2017, January 4–9). Automatic differentiation in PyTorch. Proceedings of the 2017 Neural Information Processing Systems, Long Beach, CA, USA.
- Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv.
- Huo, 2016, Learning relationship for very high resolution image change detection, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 9, 3384, 10.1109/JSTARS.2016.2569598 · doi:10.1109/jstars.2016.2569598
Cited by
Shi, Q., Liu, M., Li, S., Liu, X., Wang, F., & Zhang, L. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3085870 · Cited by 698
Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation
Yang, L., Qi, L., Feng, L., Zhang, W., & Shi, Y. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00699 · Cited by 580
RemoteCLIP: A Vision Language Foundation Model for Remote Sensing
Liu, F., Chen, D., Guan, Z., Zhou, X., Zhu, J., Ye, Q., Fu, L., & Zhou, J. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3390838 · Cited by 507
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
RingMo: A Remote Sensing Foundation Model With Masked Image Modeling
Sun, X., Wang, P., Lu, W., Zhu, Z., Lu, X., He, Q., Li, J., Rong, X., Yang, Z., Chang, H., et al. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3194732 · Cited by 323
Metadata
- Authors
- Chen, H., & Shi, Z.
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2020-05-22
- Volume
- 12
- Issue
- 10
- Pages
- 1662
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W3027225766
- Cited by
- 1,903
- References
- 54
- FWCI
- 77.73
- Type
- journal article
- Language
- en
- Open access
- gold
- Funders
- the National Key R&D Programof China (2017YFC1405605)
- the Beijing Natural Science Foundation (4192034)
- Shanghai Association for Science and Technology (SAST2018096)
- National Natural Science Foundation of China (61671037)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection. https://app.cochat.ai/papers/10.3390/rs12101662
- The paper
- Chen, H., & Shi, Z. (2020). A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection. Remote Sensing. https://doi.org/10.3390/rs12101662
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
