Clustering-segmentation network: a parallel dual-branch synthetic aperture radar image change detection framework

References

• Aharon, M., M. Elad, and A. Bruckstein. 2006. “K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation.” IEEE Transactions on Signal Processing 54 (11): 4311–4322. doi:10.1109/tsp.2006.881199.
   Web of Science ®Google Scholar
• Ahmadi, S., and S. Homayouni. 2020. “A Novel Active Contours Model for Environmental Change Detection from Multitemporal Synthetic Aperture Radar Images.” Remote Sensing 12 (11): 1746. doi:10.3390/rs12111746.
   Web of Science ®Google Scholar
• Brunner, D., G. Lemoine, and L. Bruzzone. 2010. “Earthquake Damage Assessment of Buildings Using VHR Optical and SAR Imagery.” IEEE Transactions on Geoscience and Remote Sensing 48 (5): 2403–2420. doi:10.1109/tgrs.2009.2038274.
   Web of Science ®Google Scholar
• Camps-Valls, G., L. Gomez-Chova, J. Munoz-Mari, J. L. Rojo-Alvarez, and M. Martinez-Ramon. 2008. “Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection.” IEEE Transactions on Geoscience and Remote Sensing 46 (6): 1822–1835. doi:10.1109/tgrs.2008.916201.
   Web of Science ®Google Scholar
• Celik, T. 2009. “Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and K-Means Clustering.” IEEE Geoscience and Remote Sensing Letters 6 (4): 772–776. doi:10.1109/lgrs.2009.2025059.
   Web of Science ®Google Scholar
• Chen, H., and Z. Shi. 2020. “A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection.” Remote Sensing 12 (10): 1662. doi:10.3390/rs12101662.
   Web of Science ®Google Scholar
• Felzenszwalb, P. F., and D. P. Huttenlocher. 2004. “Efficient Graph-Based Image Segmentation.” International Journal of Computer Vision 59 (2): 167–181. doi:10.1023/b:visi.0000022288.19776.77.
   Web of Science ®Google Scholar
• Gao, F., J. Dong, B. Li, and X. Qizhi. 2016. “Automatic Change Detection in Synthetic Aperture Radar Images Based on Pcanet.” IEEE Geoscience and Remote Sensing Letters 13 (12): 1792–1796. doi:10.1109/lgrs.2016.2611001.
   Web of Science ®Google Scholar
• Gao, F., J. Dong, B. Li, X. Qizhi, and C. Xie. 2016. “Change Detection from Synthetic Aperture Radar Images Based on Neighborhood-Based Ratio and Extreme Learning Machine.” Journal of Applied Remote Sensing 10 (4): 046019. doi:10.1117/1.jrs.10.046019.
   Google Scholar
• Gao, F., X. Wang, Y. Gao, J. Dong, and S. Wang. 2019. “Sea Ice Change Detection in SAR Images Based on Convolutional-Wavelet Neural Networks.” IEEE Geoscience and Remote Sensing Letters 16 (8): 1240–1244. doi:10.1109/lgrs.2019.2895656.
   Web of Science ®Google Scholar
• Gong, M., Y. Cao, and W. Qiaodi. 2012. “A Neighborhood-Based Ratio Approach for Change Detection in SAR Images.” IEEE Geoscience and Remote Sensing Letters 9 (2): 307–311. doi:10.1109/lgrs.2011.2167211.
   Web of Science ®Google Scholar
• Jia, L., T. Zhang, J. Fang, and F. Dong. 2021. “Multiple Kernel Graph Cut for SAR Image Change Detection.” Remote Sensing 13 (4): 725. doi:10.3390/rs13040725.
   Web of Science ®Google Scholar
• Jimenez-Sierra, D. A., H. Darío Benítez-Restrepo, H. Darío Vargas-Cardona, and J. Chanussot. 2020. “Graph-Based Data Fusion Applied To: Change Detection and Biomass Estimation in Rice Crops.” Remote Sensing 12 (17): 2683. doi:10.3390/rs12172683.
   Web of Science ®Google Scholar
• Kaiming, H., X. Zhang, S. Ren, and J. Sun. 2016. “Deep Residual Learning for Image Recognition.” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 10.1109/cvpr.2016.90.
   Google Scholar
• Moser, G., and S. B. Serpico. 2006. “Generalized Minimum-Error Thresholding for Unsupervised Change Detection from SAR Amplitude Imagery.” IEEE Transactions on Geoscience and Remote Sensing 44 (10): 2972–2982. doi:10.1109/tgrs.2006.876288.
   Web of Science ®Google Scholar
• Otsu, N. 1979. “A Threshold Selection Method from Gray-Level Histograms.” IEEE Transactions on Systems, Man, and Cybernetics 9 (1): 62–66. doi:10.1109/tsmc.1979.4310076.
   Web of Science ®Google Scholar
• Pati, Y. C., R. Rezaiifar, and P. S. Krishnaprasad. n.d. “Orthogonal Matching Pursuit: Recursive Function Approximation with Applications to Wavelet Decomposition.” Proceedings of 27th Asilomar Conference on Signals, Systems and Computers. 10.1109/acssc.1993.342465.
   Google Scholar
• Shu, Y., L. Wei, M. Yang, P. Cheng, and S. Han. 2021. “Patch-Based Change Detection Method for SAR Images with Label Updating Strategy.” Remote Sensing 13 (7): 1236. doi:10.3390/rs13071236.
   Web of Science ®Google Scholar
• Wang, J., Y. Wang, and H. Liu. 2022. “Hybrid Variability Aware Network (Hvanet): A Self-Supervised Deep Framework for Label-Free SAR Image Change Detection.” Remote Sensing 14 (3): 734. doi:10.3390/rs14030734.
   Web of Science ®Google Scholar
• Woo, S., J. Park, J.Y. Lee, and I. So Kweon. 2018. “CBAM: Convolutional Block Attention Module.” Computer Vision – ECCV 2018 3–19. doi:10.1007/978-3-030-01234-2_1.
   Google Scholar
• Xiaofan, Q., F. Gao, J. Dong, D. Qian, and L. Heng-Chao. 2022. “Change Detection in Synthetic Aperture Radar Images Using a Dual-Domain Network.” IEEE Geoscience and Remote Sensing Letters 19: 1–5. doi:10.1109/lgrs.2021.3073900.
   Web of Science ®Google Scholar
• Yousif, O., and Y. Ban. 2013. “Improving Urban Change Detection from Multitemporal SAR Images Using PCA-NLM.” IEEE Transactions on Geoscience and Remote Sensing 51 (4): 2032–2041. doi:10.1109/tgrs.2013.2245900.
   Web of Science ®Google Scholar
• Zhang, X., L. Hongfeng, and L. Jiao. 2009. “A Change Detection Algorithm Based on Object Feature for SAR Image.” 2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar. 10.1109/apsar.2009.5374109.
   Google Scholar
• Zhang, H., L. Lei, N. Weiping, T. Tang, W. Junzheng, D. Xiang, and G. Kuang. 2022a. “Explore Better Network Framework for High-Resolution Optical and SAR Image Matching.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–18. doi:10.1109/tgrs.2021.3126939.
   Web of Science ®Google Scholar
• Zhang, H., L. Lei, N. Weiping, T. Tang, W. Junzheng, D. Xiang, and G. Kuang. 2022b. “Optical and SAR Image Matching Using Pixelwise Deep Dense Features.” IEEE Geoscience and Remote Sensing Letters 19: 1–5. doi:10.1109/lgrs.2020.3039473.
   Web of Science ®Google Scholar
• Zhuang, H., H. Fan, K. Deng, and Y. Yang. 2018. “An Improved Neighborhood-Based Ratio Approach for Change Detection in SAR Images.” European Journal of Remote Sensing 51 (1): 723–738. doi:10.1080/22797254.2018.1482523.
   Web of Science ®Google Scholar