Change Detection in Remote Sensing Data Using Attention Network

Document Type : Original Article

Authors

1 Ph.D. Student, Dep. of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

2 Assistant Prof., Dep. of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

Abstract

Introduction: Detecting changes in remote sensing data is a critical task in environmental sciences, natural resource management, urban planning, and disaster management. Despite recent advancements in this field, many existing methods only address specific challenges and are unable to provide a comprehensive solution for various types of data and applications. These limitations include the inability to handle multispectral, hyperspectral, and radar data and the lack of capability to deliver accurate and timely results using parallel processing and optimized computing resources. Additionally, current methods are often confined to binary change detection and cannot accurately identify the specific types of changes. Therefore, the primary aim of this research is to develop an innovative and comprehensive change detection method that can overcome these limitations and be effectively used in real-world applications.
Materials and Methods: In this study, we propose a novel approach based on the combination of a transformer network and an automated attention model, capable of processing and analyzing remote sensing data with high accuracy and efficiency. This method utilizes multispectral, hyperspectral, and radar data obtained from Sentinel-2, QuickBird, and TerraSAR-X satellites. These data are collected over different time periods and include diverse information such as vegetation changes, land use, and structural changes. The proposed method employs feature fusion techniques using convolutional and transformer layers to integrate information from the data, thereby enhancing change detection accuracy. Additionally, the use of spatial attention mechanisms helps identify spatial relationships between features, focusing on key areas to improve change detection accuracy. The transformer-based network, developed to determine similarity, is enhanced with automated attention mechanisms that capture complex relationships between features over temporal sequences. This capability is especially important for detecting subtle changes that may be overlooked by other methods. For operational implementation, the proposed method was deployed and evaluated on a high-performance system, including a 24-core Xeon E5-2697 v2 CPU, 28 GB of memory, a 200 GB SSD, and a powerful RTX 2080 Ti graphics card with 11 GB of RAM and CUDA 11.
Results and Discussion: The results obtained from this research indicate the superiority of the proposed method compared to existing methods. Evaluations were conducted using metrics such as Precision, Recall, F1-score, Overall Accuracy (OA), and Intersection over Union (IoU). The proposed method outperformed other methods across all these metrics. Notably, overall accuracy (OA) increased significantly, reaching over 95% on some datasets. These results indicate that the proposed method can not only accurately detect binary changes but also identify the types of changed features with high precision. These capabilities are achieved through the use of advanced deep learning techniques and parallel processing. Moreover, the implementation of the SoFRB(Enayati et al. 2023) framework has enhanced the efficiency of the proposed method, enabling the processing of large volumes of data in less time. Our analysis demonstrates that the proposed method has high adaptability with different datasets and can effectively operate under various conditions. Furthermore, this method can serve as an efficient tool for various applications, including environmental monitoring, urban planning, precision agriculture, and disaster management.
Conclusion: The proposed method integrates modern deep learning techniques and parallel processing to significantly improve the accuracy and efficiency of change detection in remote sensing data. The findings of this study show that the proposed method is reliable not only in experimental settings but also in practical applications. Specifically, this method can effectively monitor environmental changes, detect alterations in urban infrastructures, and manage natural and human-induced disasters. These results promise widespread applications of this method in various fields. Future research could include further improvements in different areas, such as model optimization, the use of more diverse and extensive datasets, and the exploration of the impact of newer deep learning and parallel processing techniques.

Keywords

References

Afaq, Y. & Manocha, A., 2021, Analysis on Change Detection Techniques for Remote Sensing Applications: A Review, Ecological Informatics, 63, P. 101310. https://doi.org/ 10.1016/j.ecoinf.2021.101310.
Aleissaee, A.A., Kumar, A., Anwer, R.M., Khan, S., Cholakkal, H., Xia, G.-S. & Khan, F.S., 2023, Transformers in Remote Sensing: A Survey, Remote Sensing, 15(7), P. 1860. https://doi.org/10.3390/rs15071860.
Asokan, A. & Anitha, J., 2019, Change Detection Techniques for Remote Sensing Applications: A Survey, Earth Science Informatics, 12, PP. 143–160. https://doi.org/ 10.1007/s12145-019-00380-5.
Bai, T., Wang, L., Yin, D., Sun, K., Chen, Y., Li, W. & Li, D., 2023, Deep Learning for Change Detection in Remote Sensing: A Review, Geo-Spatial Information Science, 26(3), PP. 262–288. https://doi.org/10.1080/ 10095020.2022.2085633.
Boccardo, P. & Giulio Tonolo, F., 2015, Remote Sensing Role in Emergency Mapping for Disaster Response, In: Engineering Geology for Society and Territory-Volume 5, Urban Geology, Sustainable Planning and Landscape Exploitation. [place unknown], Springer, PP. 17–24. https://doi.org/10.1007/ 978-3-319-09048-1_3.
Boulila, W., Ghandorh, H., Masood, S., Alzahem, A., Koubaa, A., Ahmed, F., Khan, Z. & Ahmad, J., 2024, A Transformer-Based Approach Empowered by a Self-Attention Technique for Semantic Segmentation in Remote Sensing, Heliyon, 10(8). https://doi.org/10.1016/j.heliyon.2024. e29396.
Chen, H. & Shi, Z., 2020, A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection, Remote Sensing, 12(10), P. 1662. https://doi.org/10.3390/rs12101662.
Chen, L., Zhang, D., Li, P. & Lv, P., 2020, Change Detection of Remote Sensing Images Based on Attention Mechanism, Computational Intelligence and Neuroscience, 2020(1), P. 6430627. https://doi.org/10.1155/ 2020/6430627.
Chen, H., Qi, Z. & Shi, Z., 2021, Remote Sensing Image Change Detection with Transformers, IEEE Transactions on Geoscience and Remote Sensing, 60, PP. 1–14. https://doi.org/10.1109/TGRS.2021.3095166.
Chen, J., Chen, S., Fu, R., Li, D., Jiang, H., Wang, C., Peng, Y., Jia, K. & Hicks, B.J., 2022, Remote Sensing Big Data for Water Environment Monitoring: Current Status, Challenges, and Future Prospects, Earth’s Future, 10(2), P. e2021EF002289.
     https://doi.org/ 10.1029/2021EF002289.
Chi, M., Plaza, A., Benediktsson, J.A., Sun, Z., Shen, J. & Zhu, Y., 2016, Big Data for Remote Sensing: Challenges and Opportunities, Proceedings of the IEEE, 104(11), PP. 2207–2219. https://doi.org/ 10.1109/JPROC.2016.2598228.
Enayati, R., Ravanmehr, R. & Aghazarian, V., 2023, A Service-Oriented Framework for Remote Sensing Big Data Processing, Earth Science Informatics, 16(1), PP. 591–616. https://doi.org/10.1007/s12145-022-00900-w.
Fang, B., Pan, L. & Kou, R., 2019, Dual Learning-Based Siamese Framework for Change Detection Using Bi-Temporal VHR Optical Remote Sensing Images, Remote Sensing, 11(11), P. 1292. https://doi.org/10.3390/rs11111292.
Fingas, M., 2019, Remote Sensing for Marine Management, In: World Seas: An Environmental Evaluation, [place unknown], Elsevier, PP. 103–119. https://doi.org/10.1016/ B978-0-12-805052-1.00005-X.
Gong, M., Niu, X., Zhang, P. & Li, Z., 2017, Generative Adversarial Networks for Change Detection in Multispectral Imagery, IEEE Geoscience and Remote Sensing Letters, 14(12), PP.2310–2314. https://doi.org/10.1109/ LGRS.2017.2762694.
Huang, Y., Chen, Z., Tao, Y., Huang, X. & Gu, X., 2018, Agricultural Remote Sensing Big Data: Management and Applications, Journal of Integrative Agriculture, 17(9), PP. 1915–1931. https://doi.org/10.1016/S2095-3119(17)61859-8.
Ismail, A., Bagula, B.A. & Tuyishimire, E., 2018, Internet-of-Things in Motion: A Uav Coalition Model for Remote Sensing in Smart Cities, Sensors, 18(7), P. 2184. https://doi.org/10.3390/s18072184.
Khan, S.H., He, X., Porikli, F. & Bennamoun, M., 2017, Forest Change Detection in Incomplete Satellite Images with Deep Neural Networks, IEEE Transactions on Geoscience and Remote Sensing, 55(9), PP. 5407–5423. https://doi.org/10.1109/TGRS.2017.2707528.
Lei, T., Zhang, Y., Lv, Z., Li, S., Liu, S. & Nandi, A.K., 2019, Landslide Inventory Mapping from Bitemporal Images Using Deep Convolutional Neural Networks, IEEE Geoscience and Remote Sensing Letters, 16(6), PP. 982–986. https://doi.org/ 10.1109/LGRS.2018.2889307.
Li, K., Wan, G., Cheng, G., Meng, L. & Han, J., 2020, Object Detection in Optical Remote Sensing Images: A Survey and a New Benchmark, ISPRS Journal of Photogrammetry and Remote Sensing, 159, pp. 296–307. https://doi.org/10.1016/j.isprsjprs. 2019.11.023.
Li, X., Du, Z., Huang, Y. & Tan, Z., 2021a, A Deep Translation (GAN) Based Change Detection Network for Optical and SAR Remote Sensing Images, ISPRS Journal of Photogrammetry and Remote Sensing, 179, PP. 14–34. https://doi.org/10.1016/j.isprsjprs. 2021.07.007.
Li, Y., Ma, J. & Zhang, Y., 2021b, Image Retrieval from Remote Sensing Big Data: A Survey, Information Fusion, 67, PP. 94–115. https://doi.org/10.1016/j.inffus.2020.10.008.
Li, Q., Yan, D. & Wu, W., 2021c, Remote Sensing Image Scene Classification Based on Global Self-Attention Module, Remote Sensing, 13(22), P. 4542. https://doi.org/ 10.3390/rs13224542.
Li, J., Zhu, S., Gao, Y., Zhang, G. & Xu, Y., 2022a, Change Detection for High-Resolution Remote Sensing Images Based on a Multi-Scale Attention Siamese Network, Remote Sensing, 14(14), P. 3464. https://doi.org/10.3390/rs14143464.
Li, H., Zhu, F., Zheng, X., Liu, M. & Chen, G., 2022b, MSCDUNet: A Deep Learning Framework for Built-up Area Change Detection Integrating Multispectral, SAR, and VHR Data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, PP. 5163–5176. https://doi.org/10.1109/JSTARS.2022.3181155.
Liu, P., 2015, A Survey of Remote-Sensing Big Data, Frontiers in Environmental Science, 3, P. 45. https://doi.org/10.3389/fenvs.2015.00045.
Lu, M., Pebesma, E., Sanchez, A. & Verbesselt, J., 2016, Spatio-Temporal Change Detection from Multidimensional Arrays: Detecting Deforestation from MODIS Time Series, ISPRS Journal of Photogrammetry and Remote Sensing, 117, PP. 227–236. https://doi.org/10.1016/j.isprsjprs.2016.03.007.
Lv, Z., Zhong, P., Wang, W., You, Z. & Falco, N., 2023, Multi-Scale Attention Network Guided with Change Gradient Image for Land Cover Change Detection Using Remote Sensing Images, IEEE Geoscience and Remote Sensing Letters. https://doi.org/ 10.1109/LGRS.2023.3267879.
Pande, C.B. & Moharir, K.N., 2023, Application of Hyperspectral Remote Sensing Role in Precision Farming and Sustainable Agriculture under Climate Change: A Review, Climate Change Impacts on Natural Resources, Ecosystems and Agricultural Systems, PP. 503–520. https://doi.org/ 10.1007/978-3-031-19059-9_21.
Papadomanolaki, M., Verma, S., Vakalopoulou, M., Gupta, S. & Karantzalos, K., 2019, Detecting Urban Changes with Recurrent Neural Networks from Multitemporal Sentinel-2 Data, In: IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. [Place Unknown], IEEE, PP. 214–217. https://doi.org/10.1109/ IGARSS.2019.8900330.
Peng. D., Bruzzone, L., Zhang, Y., Guan, H., Ding, H. & Huang, X., 2020, SemiCDNet: A Semisupervised Convolutional Neural Network for Change Detection in High Resolution Remote-Sensing Images, IEEE Transactions on Geoscience and Remote Sensing, 59(7), PP. 5891–5906. https://doi.org/ 10.1109/TGRS.2020.3011913.
Rathee, G., Kerrache, C.A., Calafate, C.T., Bilal, M. & Song, H., 2024, SMART: A Secure Remote Sensing Solution for Smart Cities’ Urban Areas, IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2024.3362377.
Sefrin, O., Riese, F.M. & Keller, S., 2020, Deep Learning for Land Cover Change Detection, Remote Sensing, 13(1), P. 78. https://doi.org/10.3390/rs13010078.
Shafique, A., Cao, G., Khan, Z., Asad, M. & Aslam, M., 2022, Deep Learning-Based Change Detection in Remote Sensing Images: A Review, Remote Sensing, 14(4), P. 871. https://doi.org/10.3390/rs14040871.
Shen, Q., Huang, J., Wang, M., Tao, S., Yang, R. & Zhang, X., 2022, Semantic Feature-Constrained Multitask Siamese Network for Building Change Detection in High-Spatial-Resolution Remote Sensing Imagery, ISPRS Journal of Photogrammetry and Remote Sensing, 189, PP. 78–94. https://doi.org/10.1016/j.isprsjprs.2022.05.001.
Shi, W., Zhang, M., Zhang, R., Chen, S. & Zhan, Z., 2020, Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges, Remote Sensing, 12(10), P. 1688. https://doi.org/10.3390/rs12101688.
Shi, Q., Liu, M., Li, S., Liu, X., Wang, F. & Zhang, L., 2021, 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, 60, PP. 1–16. https://doi.org/10.1109/ TGRS.2021.3085870.
Temenos, A., Temenos, N., Tzortzis, I.N., Rallis, I., Doulamis, A. & Doulamis, N., 2024, C2A-DC: A Context-Aware Adaptive Data Cube Framework for Environmental Monitoring and Climate Change Crisis Management, Remote Sensing Applications: Society and Environment, 34, P. 101171. https://doi.org/10.1016/j.rsase.2024.101171.
Toth, C. & Jóźków, G., 2016, Remote Sensing Platforms and Sensors: A Survey, ISPRS Journal of Photogrammetry and Remote Sensing, 115, PP. 22–36. https://doi.org/ 10.1016/j.isprsjprs.2015.10.004.
Vance, T.C., Huang, T. & Butler, K.A., 2024, Big Data in Earth Science: Emerging Practice and Promise, Science, 383(6688), P. eadh9607. https://doi.org/10.1126/science. adh9607.
Viana, C.M., Oliveira, S., Oliveira, S.C. & Rocha, J., 2019, Land Use/Land Cover Change Detection and Urban Sprawl Analysis, In: Spatial Modeling in GIS and R for Earth and Environmental Sciences, [Place Unknown], Elsevier, PP. 621–651. https://doi.org/10.1016/B978-0-12-815226-3.00029-6.
Wang, R. & Gamon, J.A., 2019, Remote Sensing of Terrestrial Plant Biodiversity, Remote Sensing of Environment, 231, P. 111218. https://doi.org/10.1016/j.rse.2019.111218.
Wang, Z., Peng, C., Zhang, Y., Wang, N. & Luo, L., 2021, Fully Convolutional Siamese Networks Based Change Detection for Optical Aerial Images with Focal Contrastive Loss, Neurocomputing, 457, PP. 155–167. https://doi.org/10.1016/j.neucom.2021.06.059.
Wang, S., Han, W., Huang, X., Zhang, X., Wang, L. & Li, J., 2024, Trustworthy Remote Sensing Interpretation: Concepts, Technologies, and Applications, ISPRS Journal of Photogrammetry and Remote Sensing, 209, PP. 150–172. https://doi.org/ 10.1016/j.isprsjprs.2024.02.003.
Wu, Y., Li, J., Yuan, Y., Qin, A., Miao, Q.-G. & Gong, M.-G., 2021, Commonality Autoencoder: Learning Common Features for Change Detection from Heterogeneous Images, IEEE Transactions on Neural Networks and Learning Systems, 33(9), PP. 4257–4270. https://doi.org/10.1109/TNNLS.2021.3056238.
Yang, J., Gong, P., Fu, R., Zhang, M., Chen, J., Liang, S., Xu, B., Shi, J. & Dickinson, R., 2013, The Role of Satellite Remote Sensing in Climate Change Studies, Nature Climate Change, 3(10), PP. 875–883. https://doi.org/ 10.1038/nclimate1908.
Yang, L., Chen, Y., Song, S., Li, F. & Huang, G., 2021, Deep Siamese Networks Based Change Detection with Remote Sensing Images, Remote Sensing, 13(17), P. 3394. https://doi.org/10.3390/rs13173394.
Yusufovich, G.Y. & Yokubov, Sh.Sh.o., 2023, The Use of Remote Sensing Technologies in the Design of Maps of Agricultural Land, Texas Journal of Agriculture and Biological Sciences, 23, PP. 17–21.
Zhan, Y., Fu, K., Yan, M., Sun, X., Wang, H. & Qiu, X., 2017, Change Detection Based on Deep Siamese Convolutional Network for Optical Aerial Images, IEEE Geoscience and Remote Sensing Letters, 14(10), PP. 1845–1849. https://doi.org/10.1109/LGRS.2017.2738149.
Zhang, J., Huang, Y., Pu, R., Gonzalez-Moreno, P., Yuan, L., Wu, K. & Huang, W., 2019, Monitoring Plant Diseases and Pests through Remote Sensing Technology: A Review, Computers and Electronics in Agriculture, 165, P. 104943. https://doi.org/ 10.1016/j.compag.2019.104943.
Zhang, H., Lin, M., Yang, G. & Zhang, L., 2021, ESCNet: An End-to-End Superpixel-Enhanced Change Detection Network for Very-High-Resolution Remote Sensing Images, IEEE Transactions on Neural Networks and Learning Systems.
     https://doi.org/10.1109/TNNLS.2021.3089332.
Zhang, M., Liu, Z., Feng, J., Liu, L. & Jiao, L., 2023, Remote Sensing Image Change Detection Based on Deep Multi-Scale Multi-Attention Siamese Transformer Network, Remote Sensing, 15(3), P. 842. https://doi.org/10.3390/rs15030842.
Zheng, Z., Wan, Y., Zhang, Y., Xiang, S., Peng, D. & Zhang, B., 2021, CLNet: Cross-Layer Convolutional Neural Network for Change Detection in Optical Remote Sensing Imagery, ISPRS Journal of Photogrammetry and Remote Sensing, 175, PP. 247–267. https://doi.org/10.1016/j.isprsjprs.2021.03.005.
Zhu, Q., Guo, X., Deng, W., Shi, S., Guan, Q., Zhong, Y., Zhang, L. & Li, D., 2022, Land-Use/Land-Cover Change Detection Based on a Siamese Global Learning Framework for High Spatial Resolution Remote Sensing Imagery, ISPRS Journal of Photogrammetry and Remote Sensing, 184, PP. 63–78. https://doi.org/10.1016/j.isprsjprs. 2021.12.005.
Zhu, S., Song, Y., Zhang, Y. & Zhang, Y., 2023, ECFNet: A Siamese Network with Fewer FPs and Fewer FNs for Change Detection of Remote-Sensing Images, IEEE Geoscience and Remote Sensing Letters, 20, PP. 1–5. https://doi.org/10.1109/LGRS.2023.3238553.
Zhu, Q., Guo, X., Li, Z. & Li, D., 2024, A Review of Multi-Class Change Detection for Satellite Remote Sensing Imagery, Geo-spatial Information Science, 27(1), PP. 1–15. https://doi.org/10.1080/10095020.2022.2128902.

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