References
- Alcantarilla, P. F., S. Stent, G. Ros, R. Arroyo, and R. Gherardi. 2018. “Street-View Change Detection with Deconvolutional Networks.” Autonomous Robots 42 (7): 1–22. doi:https://doi.org/10.1007/s10514-018-9734-5.
- Badrinarayanan, V., A. Kendall, and R. Cipolla. 2017 “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.” IEEE Transactions on Pattern Analysis & Machine Intelligence, 1. https://ieeexplore.ieee.org/document/7803544/citations
- Bochkovskiy, A., C. Y. Wang, and H. Y. M. Liao. 2020. “Yolov4: Optimal Speed and Accuracy of Object Detection.” arXiv preprint arXiv:2004.10934, 2020. https://arxiv.org/abs/2004.10934
- Bruzzone, L. and D. F. Prieto. 2020. “Automatic Analysis of the Difference Image for Unsupervised Change Detection.” IEEE Transactions on Geoscience and Remote Sensing : A Publication of the IEEE Geoscience and Remote Sensing Society 38: 1171–1182. https://ieeexplore.ieee.org/document/843009
- Chen, L. C., G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. 2017. “Deeplab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected Crfs.” IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (4): 834–848. https://ieeexplore.ieee.org/document/7913730
- Chen, L. C., G. Papandreou, F. Schroff, and H. Adam 2017. “Rethinking Atrous Convolution for Semantic Image Segmentation”. arXiv preprint arXiv:1706.05587, 2017. https://arxiv.org/abs/1706.05587
- 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. https://www.mdpi.com/2072-4292/12/10/1662
- Chen, L., D. Zhang, P. Li, and P. Lv. 2020. “Change Detection of Remote Sensing Images Based on Attention Mechanism.” Computational Intelligence and Neuroscience 2020: 6430627. https://www.hindawi.com/journals/cin/2020/6430627/
- Cheng, W., Y. Zhang, X. Lei, W. Yang, G. Xia. 2020. “Semantic Change Pattern Analysis.” arXiv preprint arXiv:2003.03492, 2020. 2003.03492.pdf (arxiv.org).
- Chughtai, A. H., H. Abbasi, and I. R. Karas. 2021. “A Review on Change Detection Method and Accuracy Assessment for Land Use Land Cover.” Remote Sensing Applications: Society and Environment 22: 100482. doi:https://doi.org/10.1016/j.rsase.2021.100482.
- Daudt, R. C., B. Le Saux, and A. Boulch. 2018. “Fully Convolutional Siamese Networks for Change Detection.” Paper presented at the 25th IEEE International Conference on Image Processing (ICIP), 4063–4067. https://ieeexplore.ieee.org/document/8451652/citations#citations
- Fu, J., J. Liu, H. Tian, Y. Li, Y. Bao, Z. Fang, and H. Lu. 2019. “Dual Attention Network for Scene Segmentation.” Paper presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 3146–3154. https://ieeexplore.ieee.org/document/8953974
- Gil-Yepes, J. L., L. A. Ruiz, J. A. Recio, Á. Balaguer-Beser, and T. Hermosilla. 2016. “Description and Validation of a New Set of Object-Based Temporal Geostatistical Features for Land-Use/land-Cover Change Detection.” ISPRS Journal of Photogrammetry and Remote Sensing 121: 77–91. https://www.sciencedirect.com/science/article/abs/pii/S0924271616303434.
- He, K., X. Zhang, S. Ren, and J. Sun. 2016. “Deep Residual Learning for Image Recognition.” Paper presented at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. https://ieeexplore.ieee.org/document/7780459
- Howarth, P. J. and G. M. Wickware. 1981. “Procedures for Change Detection Using Landsat Digital Data.” International Journal of Remote Sensing 2 (3): 277–291. doi:https://doi.org/10.1080/01431168108948362.
- Hu, D. 2019. “An Introductory Survey on Attention Mechanisms in NLP Problems.” Paper presented at the Proceedings of SAI Intelligent Systems Conference, 432–448. https://link.springer.com/chapter/10.1007%2F978-3-030-29513-4_31
- Huang, H., L. Lin, R. Tong, H. Hu, Q. Zhang, Y. Iwamoto, and J. Wu. 2020. “Unet 3+: A Full-Scale Connected Unet for Medical Image Segmentation.” Paper presented at ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1055–1059. https://arxiv.org/abs/2004.08790
- Huang, G., Z. Liu, L. Van Der Maaten, and K. Q. Weinberger. 2017. “Densely Connected Convolutional Networks.” Paper presented at the IEEE conference on computer vision and pattern recognition, 4700–4708. https://arxiv.org/abs/1608.06993
- Isaienkov, K., M. Yushchuk, V. Khramtsov, and O. Seliverstov. 2020. “Deep Learning for Regular Change Detection in Ukrainian Forest Ecosystem with Sentinel-2.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, no. 99: 1. https://ieeexplore.ieee.org/document/9241044
- Jiang, H., X. Hu, K. Li, J. Zhang, J. Gong, and M. Zhang. 2020. “PGA-SiamNet: Pyramid Feature-Based Attention-Guided Siamese Network for Remote Sensing Orthoimagery Building Change Detection.” Remote Sensing 12 (3): 484. https://www.mdpi.com/2072-4292/12/3/484
- Lee, H., K. Lee, J. H. Kim, Y. Na, J. Park, J. P. Choi, and J. Y. Hwang. 2021. “Local Similarity Siamese Network for Urban Land Change Detection on Remote Sensing Images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14: 4139–4149. doi:https://doi.org/10.1109/JSTARS.2021.3069242.
- Li, L., C. Wang, H. Zhang, B. Zhang, and F. Wu. 2019. “Urban Building Change Detection in SAR Images Using Combined Differential Image and Residual U-Net Network.” Remote Sensing 11 (9): 1091. https://www.mdpi.com/2072-4292/11/9/1091
- Luo, H., C. Liu, C. Wu, and X. Guo. 2018. “Urban Change Detection Based on Dempster–Shafer Theory for Multitemporal Very High-Resolution Imagery.” Remote Sensing 10 (7): 980. https://www.mdpi.com/2072-4292/10/7/980
- Milletari, F., N. Navab, and S. A. Ahmadi. 2016. “V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation.” Paper presented at the IEEE 2016 fourth international conference on 3D vision (3DV), 565–571. https://ieeexplore.ieee.org/document/7785132
- Peng, D. and Y. Zhang. 2017. “Object-Based Change Detection from Satellite Imagery by Segmentation Optimization and Multi-Features Fusion.” International Journal of Remote Sensing 38 (13): 3886–3905. doi:https://doi.org/10.1080/01431161.2017.1308033.
- Peng, D., Y. Zhang, and H. Guan. 2019. “End-To-End Change Detection for High Resolution Satellite Images Using Improved Unet++.” Remote Sensing 11 (11): 1382. https://www.mdpi.com/2072-4292/11/11/1382.
- Qiao, H., X. Wan, Y. Wan, S. Li, and W. Zhang. 2020. “A Novel Change Detection Method for Natural Disaster Detection and Segmentation from Video Sequence.” Sensors 20 (18): 5076. doi:https://doi.org/10.3390/s20185076.
- Redmon, J., and A. Farhadi. 2018. “Yolov3: An Incremental Improvement.” arXiv e-prints, 2018: arXiv: 1804.02767. https://arxiv.org/abs/1804.02767
- Ronneberger, O., P. Fischer, and T. Brox. 2015. “U-Net: Convolutional Networks for Biomedical Image Segmentation.” Paper presented at the International Conference on Medical image computing and computer-assisted intervention, 234–241. Cham: Springer. https://arxiv.org/abs/1505.04597
- Singh, A. 1986. “Change Detection in the Tropical Forest Environment of Northeastern India Using Landsat.” Remote Sensing and Tropical Land Management 44: 254–273.
- Singh, S. and R. Talwar. 2015. “Assessment of Different CVA Based Change Detection Techniques Using MODIS Dataset.” Mausam 66 (1): 77–86. https://www.mendeley.com/catalogue/c44be9d0-8098-3c65-a731-eb8778ed2b98/
- Su, Y., R. John, Z. Zhe, and E. J. Ronald. 2021. “A Near-Real-Time Approach for Monitoring Forest Disturbance Using Landsat Time Series: Stochastic Continuous Change Detection.” Remote Sensing of Environment 2021: 252. https://www.sciencedirect.com/science/article/pii/S003442572030540X
- Sun, K., B. Xiao, D. Liu, and J. Wang. 2019. “Deep High-Resolution Representation Learning for Human Pose Estimation.” Paper presented at the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 5693–5703 . (CVPR 2019 Open Access Repository (thecvf.com)).
- Tan, M., and Q. V. Le. 2021. “Efficientnetv2: Smaller Models and Faster Training.” arXiv preprint arXiv:2104.00298, 2021. https://arxiv.org/abs/2104.00298
- Wang, B., S.-K. Choi, Y.-K. Han, S.-K. Lee, and J.-W. Choi. 2015. “Application of IR-MAD Using Synthetically Fused Images for Change Detection in Hyperspectral Data.” Remote Sensing Letters 6 (8): 578–586. doi:https://doi.org/10.1080/2150704X.2015.1062155.
- Wang, X., S. Liu, P. Du, H. Liang, J. Xia, and Y. Li. 2018. “Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning.” Remote Sensing 10 (2): 276. https://www.mdpi.com/2072-4292/10/2/276
- Wang, Q., B. Wu, P. Zhu, P. Li, W. Zuo, and Q. Hu. 2020. “ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks.” Paper presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. https://ieeexplore.ieee.org/document/9156697
- Wang, Q., Z. Yuan, Q. Du, and X. Li. 2018. “GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection.” IEEE Transactions on Geoscience and Remote Sensing 57 (1): 3–13. https://arxiv.org/abs/1905.01662
- Wang, M., H. Zhang, W. Sun, S. Li, F. Wang, and G. Yang. 2020. “A Coarse-to-Fine Deep Learning Based Land Use Change Detection Method for High-Resolution Remote Sensing Images.” Remote Sensing 12 (12): 1933. doi:https://doi.org/10.3390/rs12121933.
- Wei, H., H. Jinliang, W. Lihui, H. Yanxia, and H. Pengpeng. 2016. “Remote Sensing Image Change Detection Based on Change Vector Analysis of PCA Component.” Remote Sensing for Land & Resources 28 (1): 22–27. http://www.gtzyyg.com/EN/Y2016/V28/I1/22.
- Xie, S., R. Girshick, P. Dollár, Z. Tu, and K. He. 2017. “Aggregated Residual Transformations for Deep Neural Networks.” Paper presented at the IEEE conference on computer vision and pattern recognition, 1492–1500 . (CVPR 2017 Open Access Repository (thecvf.com)).
- Yang, K., G. S. Xia, Z. Liu, B. Du, W. Yang, and M. Pelillo. 2020. “Asymmetric Siamese Networks for Semantic Change Detection.” ArXiv Preprint ArXiv:2010.05687, 2020. https://arxiv.org/abs/2010.05687
- Yi Quan, W., C. Zhao Qing, and T. Fei Xiang. 2016. “Change Detection of Multi-Temporal Remote Sensing Images Based on Contourlet Transform and ICA.” Chinese Journal of Geophysics 59 (4): 1284–1292. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/cjg2.20231.
- Zeyer, A., K. Irie, R. Schluter, and H. Ney. 2018. “Improved Training of End-to-End Attention Models for Speech Recognition.” Paper presented at Interspeech 2018, 7–11. https://arxiv.org/abs/1805.03294
- Zhang, Y., D. Peng, and X. Huang. 2018. “Object-Based Change Detection for VHR Images Based on Multiscale Un- Certainty Analysis.” IEEE Geoscience and Remote Sensing Letters 15: 13–17. https://ieeexplore.ieee.org/document/8197369.
- Zhang, C., P. Yue, D. Tapete, L. Jiang, B. Shangguan, L. Huang, and G. Liu. 2020. “A Deeply Supervised Image Fusion Network for Change Detection in High Resolution Bi-Temporal Remote Sensing Images.” ISPRS Journal of Photogrammetry and Remote Sensing 166: 183–200. doi:https://doi.org/10.1016/j.isprsjprs.2020.06.003.
- Zhao, W., X. Chen, X. Ge, and J. Chen. 2020. “Using Adversarial Network for Multiple Change Detection in Bitemporal Remote Sensing Imagery.” IEEE Geoscience and Remote Sensing Letters, no. 99: 1–5. https://ieeexplore.ieee.org/document/9258979.
- Zhao, H., J. Shi, X. Qi, X. Wang, and J. Jia. 2017. “Pyramid Scene Parsing Network.” Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition, 2881–2890. https://arxiv.org/abs/1612.01105v1
- Zheng, Y., X. Zhang, B. Hou, and G. Liu. 2013. “Using Combined Difference Image and K-Means Clustering for SAR Image Change Detection.” IEEE Geoscience and Remote Sensing Letters 11 (3): 691–695. https://ieeexplore.ieee.org/document/6587802.
- Zhou, Z., M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang. 2018. “Unet++: A Nested U-Net Architecture for Medical Image Segmentation.” In Paper Presented at the Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 3–11. Cham: Springer. https://link.springer.com/chapter/10.1007%2F978-3-030-00889-5_1