References
- Lu, D., Mausel, P., Brondízio, E., Moran, E.: Change detection techniques. Int. J. Remote Sens. 25, 2365–2401 (2004). DOI: 10.1080/0143116031000139863.
- Bruzzone, L., Prieto, D.F.: An adaptive semi parametric and context-based approach to unsupervised change detection in multi-temporal remote-sensing images. IEEE Transactions on Image Processing. 11, 452–466 (2002). DOI: 10.1109/TIP.2002.999678.
- Gong, M., Zhao, J., Liu, J., Miao, Q., Jiao, L.: Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks. IEEE Trans Neural Network Learn Syst. 27, 125–138 (2016). DOI: 10.1109/TNNLS.2015.2435783.
- Gao, Y., Gao, F., Dong, J., Li, H.-C.: SAR Image Change Detection Based on Multiscale Capsule Network. IEEE Geoscience and Remote Sensing Letters. 18, 484–488 (2021). DOI: 10.1109/LGRS.2020.2977838.
- Cheng, Y., Zhao, L., Chen, S., Li, X.: Hyperspectral Unmixing Network Accounting for Spectral Variability Based on a Modified Scaled and a Perturbed Linear Mixing Model. Remote Sens (Basel). 15, 3890 (2023). DOI: 10.3390/rs15153890.
- Bazi, Y., Bruzzone, L., Melgani, F.: Automatic Identification of the Number and Values of Decision Thresholds in the Log-Ratio Image for Change Detection in SAR Images. IEEE Geoscience and Remote Sensing Letters. 3, 349–353 (2006). DOI: 10.1109/LGRS.2006.869973.
- Geng, J., Ma, X., Zhou, X., Wang, H.: Saliency-Guided Deep Neural Networks for SAR Image Change Detection. IEEE Transactions on Geoscience and Remote Sensing. 57, 7365–7377 (2019). DOI: 10.1109/TGRS.2019.2913095.
- Inglada, J., Mercier, G.: A New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis. IEEE Transactions on Geoscience and Remote Sensing. 45, 1432–1445 (2007). DOI: 10.1109/TGRS.2007.893568.
- Du, Y., Zhong, R., Li, Q., Zhang, F.: TransUNet++SAR: Change Detection with Deep Learning about Architectural Ensemble in SAR Images. Remote Sens (Basel). 15, 6 (2022). DOI: 10.3390/rs15010006.
- Samadi, F., Akbarizadeh, G., Kaabi, H.: Change detection in SAR images using deep belief net-work: a new training approach based on morphological images. IET Image Process. 13, 2255–2264 (2019). DOI: 10.1049/iet-ipr.2018.6248.
- Gong, M., Li, Y., Jiao, L., Jia, M., Su, L.: SAR change detection based on intensity and texture changes. ISPRS Journal of Photogrammetry and Remote Sensing. 93, 123–135 (2014). DOI: 10.1016/j.isprsjprs.2014.04.010.
- Adegun, A.A., Viriri, S., Tapamo, J.-R.: Review of deep learning methods for remote sensing satellite images classification: experimental survey and comparative analysis. J Big Data. 10, 93 (2023). DOI: 10.1186/s40537-023-00772-x.
- Wang, P., Zhang, H., Patel, V.M.: SAR Image Despeckling Using a Convolutional Neural Network. IEEE Signal Process Lett. 24, 1763–1767 (2017). DOI: 10.1109/LSP.2017.2758203.
- Ramos, L.P., Costa, R.F. da, Medeiros, D. da S. de, Silva, P.B. da, Alves, D.I., Machado, R.: On the Effect of Imperfect Reference Images in SAR Change Detection Based on Bayes' Theorem. In: Anais do XL Simpósio Brasileiro de Telecomunicações e Processamento de Sinais. Sociedade Brasileira de Telecomunicações (2022). DOI: 10.14209/sbrt.2022.1570813069.
- Pollisetty Pravallika: Ship Tracking and Detection in SAR images using Deep Learning model. International Journal of Creative Research Thoughts. Vol. 10, (2022)
- Huiqin Chen, Fujun Zhao, Zeyuan Gu: SAR Image Change Detection Re-search: A Review. clausiuspress-Geoscience and Remote Sensing. Vol. 5, (2022).
- Shafique, A., Cao, G., Khan, Z., Asad, M., Aslam, M.: Deep Learning-Based Change Detection in Remote Sensing Images: A Review. Remote Sens (Basel). 14, 871 (2022). DOI: 10.3390/rs14040871.
- Bai, T., Wang, L., Yin, D., Sun, K., Chen, Y., Li, W., Li, D.: Deep learning for change detection in remote sensing: a review. Geo-spatial Information Science. 26, 262–288 (2023). DOI: 10.1080/10095020.2022.208 5633.
- Khelifi, L., Mignotte, M.: Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis. IEEE Access. 8, 126385–126400 (2020). DOI: 10.1109/ACCESS.2020.3008036.
- Li, L., Ma, H., Zhang, X., Zhao, X., Lv, M., Jia, Z.: Synthetic Aperture Radar Image Change Detection Based on Principal Component Analysis and Two-Level Clustering. Remote Sens (Basel). 16, 1861 (2024). DOI: 10.3390/rs16111861.
- Zhan, T., Dang, Q., Zhu, Y.: Neighborhood Difference-Based Self-Supervised Network for Detecting Small Changes From Synthetic Aperture Radar Images. IEEE Geoscience and Remote Sensing Letters. 21, 1–5 (2024). DOI: 10.1109/LGRS.2024.3415819.