Change detection in synthetic aperture radar images using spatial fuzzy clustering based on the similarity matrix

[1] Lu, D., Mausel, P., Brondízio, E., Moran, E.: Change detection techniques. Int. J. Remote Sens. 25, 2365–2401 (2004). DOI: 10.1080/0143116031000139863. LuD. MauselP. BrondízioE. MoranE. Change detection techniques Int. J. Remote Sens 25 2365 2401 2004 10.1080/0143116031000139863 Open DOISearch in Google Scholar

[2] 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. BruzzoneL. PrietoD.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 10.1109/TIP.2002.999678 Open DOISearch in Google Scholar

[3] 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. GongM. ZhaoJ. LiuJ. MiaoQ. JiaoL. Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks IEEE Trans Neural Network Learn Syst 27 125 138 2016 10.1109/TNNLS.2015.2435783 Open DOISearch in Google Scholar

[4] 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. GaoY. GaoF. DongJ. LiH.-C. SAR Image Change Detection Based on Multiscale Capsule Network IEEE Geoscience and Remote Sensing Letters 18 484 488 2021 10.1109/LGRS.2020.2977838 Open DOISearch in Google Scholar

[5] 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. ChengY. ZhaoL. ChenS. LiX. Hyperspectral Unmixing Network Accounting for Spectral Variability Based on a Modified Scaled and a Perturbed Linear Mixing Model Remote Sens (Basel) 15 3890 2023 10.3390/rs15153890 Open DOISearch in Google Scholar

[6] 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. BaziY. BruzzoneL. MelganiF. 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 10.1109/LGRS.2006.869973 Open DOISearch in Google Scholar

[7] 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. GengJ. MaX. ZhouX. WangH. Saliency-Guided Deep Neural Networks for SAR Image Change Detection IEEE Transactions on Geoscience and Remote Sensing 57 7365 7377 2019 10.1109/TGRS.2019.2913095 Open DOISearch in Google Scholar

[8] 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. IngladaJ. MercierG. 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 10.1109/TGRS.2007.893568 Open DOISearch in Google Scholar

[9] 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. DuY. ZhongR. LiQ. ZhangF. TransUNet++SAR: Change Detection with Deep Learning about Architectural Ensemble in SAR Images Remote Sens (Basel) 15 6 2022 10.3390/rs15010006 Open DOISearch in Google Scholar

[10] 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. SamadiF. AkbarizadehG. KaabiH. 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 10.1049/iet-ipr.2018.6248 Open DOISearch in Google Scholar

[11] 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. GongM. LiY. JiaoL. JiaM. SuL. SAR change detection based on intensity and texture changes ISPRS Journal of Photogrammetry and Remote Sensing 93 123 135 2014 10.1016/j.isprsjprs.2014.04.010 Open DOISearch in Google Scholar

[12] 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. AdegunA.A. ViririS. TapamoJ.-R. Review of deep learning methods for remote sensing satellite images classification: experimental survey and comparative analysis J Big Data 10 93 2023 10.1186/s40537-023-00772-x Open DOISearch in Google Scholar

[13] 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. WangP. ZhangH. PatelV.M. SAR Image Despeckling Using a Convolutional Neural Network IEEE Signal Process Lett 24 1763 1767 2017 10.1109/LSP.2017.2758203 Open DOISearch in Google Scholar

[14] 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. RamosL.P. CostaR.F. da MedeirosD. da S. de SilvaP.B. da AlvesD.I. MachadoR. 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 10.14209/sbrt.2022.1570813069 Open DOISearch in Google Scholar

[15] Pollisetty Pravallika: Ship Tracking and Detection in SAR images using Deep Learning model. International Journal of Creative Research Thoughts. Vol. 10, (2022) Pollisetty Pravallika Ship Tracking and Detection in SAR images using Deep Learning model International Journal of Creative Research Thoughts 10 2022 Search in Google Scholar

[16] Huiqin Chen, Fujun Zhao, Zeyuan Gu: SAR Image Change Detection Re-search: A Review. clausiuspress-Geoscience and Remote Sensing. Vol. 5, (2022). ChenHuiqin ZhaoFujun GuZeyuan SAR Image Change Detection Re-search: A Review clausiuspress-Geoscience and Remote Sensing 5 2022 Search in Google Scholar

[17] 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. ShafiqueA. CaoG. KhanZ. AsadM. AslamM. Deep Learning-Based Change Detection in Remote Sensing Images: A Review Remote Sens (Basel) 14 871 2022 10.3390/rs14040871 Open DOISearch in Google Scholar

[18] 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. BaiT. WangL. YinD. SunK. ChenY. LiW. LiD. Deep learning for change detection in remote sensing: a review Geo-spatial Information Science 26 262 288 2023 10.1080/10095020.2022.208 5633 Open DOISearch in Google Scholar

[19] 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. KhelifiL. MignotteM. Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis IEEE Access 8 126385 126400 2020 10.1109/ACCESS.2020.3008036 Open DOISearch in Google Scholar

[20] 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. LiL. MaH. ZhangX. ZhaoX. LvM. JiaZ. Synthetic Aperture Radar Image Change Detection Based on Principal Component Analysis and Two-Level Clustering Remote Sens (Basel) 16 1861 2024 10.3390/rs16111861 Open DOISearch in Google Scholar

[21] 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. ZhanT. DangQ. ZhuY. 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 10.1109/LGRS.2024.3415819 Open DOISearch in Google Scholar