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Mamba, with its advantages of global perception and linear complexity, has been widely applied to identify changes of the target regions within the remote sensing (RS) images captured under complex scenarios and varied conditions.
W. J. Todd, Urban and regional land use change detected by using landsat data, J. Res. US Geol. Surv 5 (5) (1977) 529–534
1977
Earlier work this paper cites.
E. P. Crist, A tm tasseled cap equivalent transformation for reflectance factor data, Remote sensing of Environment 17 (3) (1985) 301–306
1985
Earlier work this paper cites.
A. Singh, Change detection in the tropical forest environment of northeastern india using landsat, Remote sensing and tropical land management 44 (1986) 273–254
1986
Earlier work this paper cites.
A. Singh, Review article digital change detection techniques using remotely-sensed data, International journal of remote sensing 10 (6) (1989) 989–1003
1989
Earlier work this paper cites.
T. Celik, Unsupervised change detection in satellite images using principal component analysis and k k -means clustering, IEEE geoscience and remote sensing letters 6 (4) (2009) 772–776
2009
Earlier work this paper cites.
C. Marin, F. Bovolo, L. Bruzzone, Building change detection in multitemporal very high resolution sar images, IEEE transactions on geoscience and remote sensing 53 (5) (2014) 2664–2682
2014
Earlier work this paper cites.
M. Arjovsky, A. Shah, Y. Bengio, Unitary evolution recurrent neural networks, in: International conference on machine learning, PMLR, 2016, pp. 1120–1128
2016
Earlier work this paper cites.
S. Suthaharan, S. Suthaharan, Support vector machine, Machine learning models and algorithms for big data classification: thinking with examples for effective learning (2016) 207–235
2016
Earlier work this paper cites.
F. Milletari, N. Navab, S.-A. Ahmadi, V-net: Fully convolutional neural networks for volumetric medical image segmentation, in: 2016 fourth international conference on 3D vision (3DV), Ieee, 2016, pp. 565–571
2016
Earlier work this paper cites.
K. Nemoto, R. Hamaguchi, M. Sato, A. Fujita, T. Imaizumi, S. Hikosaka, Building change detection via a combination of cnns using only rgb aerial imageries, in: Remote sensing technologies and applications in urban environments II, Vol. 10431, SPIE, 2017, pp. 107–118
2017
Earlier work this paper cites.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, Grad-cam: Visual explanations from deep networks via gradient-based localization, in: Proceedings of the IEEE international conference on computer vision, 2017, pp. 618–626
2017
Earlier work this paper cites.
Y. Hu, Y. Dong, et al., An automatic approach for land-change detection and land updates based on integrated ndvi timing analysis and the cvaps method with gee support, ISPRS journal of photogrammetry and remote sensing 146 (2018) 347–359
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
D. K. Seo, Y. H. Kim, Y. D. Eo, M. H. Lee, W. Y. Park, Fusion of sar and multispectral images using random forest regression for change detection, ISPRS International Journal of Geo-Information 7 (10) (2018) 401
2018
Earlier work this paper cites.
M. Lebedev, Y. V. Vizilter, O. Vygolov, V. A. Knyaz, A. Y. Rubis, Change detection in remote sensing images using conditional adversarial networks, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42 (2018) 565–571
2018
Earlier work this paper cites.
M. Zhang, G. Xu, K. Chen, M. Yan, X. Sun, Triplet-based semantic relation learning for aerial remote sensing image change detection, IEEE Geoscience and Remote Sensing Letters 16 (2) (2018) 266–270
2018
Earlier work this paper cites.
R. C. Daudt, B. Le Saux, A. Boulch, Fully convolutional siamese networks for change detection, in: 2018 25th IEEE International Conference on Image Processing (ICIP), IEEE, 2018, pp. 4063–4067
2018
Earlier work this paper cites.
S. Ji, S. Wei, M. Lu, Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set, IEEE Transactions on Geoscience and Remote Sensing 57 (1) (2018) 574–586
2018
Earlier work this paper cites.
S. Mahdavi, B. Salehi, W. Huang, M. Amani, B. Brisco, A polsar change detection index based on neighborhood information for flood mapping, Remote Sensing 11 (16) (2019) 1854
2019
Earlier work this paper cites.
A. Voelker, I. Kajić, C. Eliasmith, Legendre memory units: Continuous-time representation in recurrent neural networks, Advances in neural information processing systems 32 (2019)
2019
Earlier work this paper cites.
S. Saha, F. Bovolo, L. Bruzzone, Unsupervised deep change vector analysis for multiple-change detection in vhr images, IEEE Transactions on Geoscience and Remote Sensing 57 (6) (2019) 3677–3693
2019
Earlier work this paper cites.
D. Peng, Y. Zhang, H. Guan, End-to-end change detection for high resolution satellite images using improved unet++, Remote Sensing 11 (11) (2019) 1382
2019
Earlier work this paper cites.
R. Liu, M. Kuffer, C. Persello, The temporal dynamics of slums employing a cnn-based change detection approach, Remote sensing 11 (23) (2019) 2844
2019
Earlier work this paper cites.
P. P. De Bem, O. A. de Carvalho Junior, R. Fontes Guimarães, R. A. Trancoso Gomes, Change detection of deforestation in the brazilian amazon using landsat data and convolutional neural networks, Remote Sensing 12 (6) (2020) 901
2020
Earlier work this paper cites.
C. Zhang, P. Yue, D. Tapete, L. Jiang, B. Shangguan, L. Huang, G. Liu, 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 (2020) 183–200
2020
Earlier work this paper cites.
Y. Liu, C. Pang, Z. Zhan, X. Zhang, X. Yang, Building change detection for remote sensing images using a dual-task constrained deep siamese convolutional network model, IEEE Geoscience and Remote Sensing Letters 18 (5) (2020) 811–815
2020
Earlier work this paper cites.
W. Shi, M. Zhang, R. Zhang, S. Chen, Z. Zhan, Change detection based on artificial intelligence: State-of-the-art and challenges, Remote Sensing 12 (10) (2020) 1688
2020
Earlier work this paper cites.
J. Chen, Z. Yuan, J. Peng, L. Chen, H. Huang, J. Zhu, Y. Liu, H. Li, Dasnet: Dual attentive fully convolutional siamese networks for change detection in high-resolution satellite images, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2020) 1194–1206
2020
Earlier work this paper cites.
H. Chen, Z. Shi, A spatial-temporal attention-based method and a new dataset for remote sensing image change detection, Remote Sensing 12 (10) (2020) 1662
2020
Cited alongside, same era.
M. Zhang, W. Shi, A feature difference convolutional neural network-based change detection method, IEEE Transactions on Geoscience and Remote Sensing 58 (10) (2020) 7232–7246
2020
Cited alongside, same era.
A. Katharopoulos, A. Vyas, N. Pappas, F. Fleuret, Transformers are rnns: Fast autoregressive transformers with linear attention, in: International conference on machine learning, PMLR, 2020, pp. 5156–5165
2020
Cited alongside, same era.
A. Gu, T. Dao, S. Ermon, A. Rudra, C. Ré, Hippo: Recurrent memory with optimal polynomial projections, Advances in neural information processing systems 33 (2020) 1474–1487
2020
Cited alongside, same era.
E. Nguyen, K. Goel, A. Gu, G. Downs, P. Shah, T. Dao, S. Baccus, C. Ré, S4nd: Modeling images and videos as multidimensional signals with state spaces, Advances in neural information processing systems 35 (2022) 2846–2861
2022
Later among the works it cites.
Y. Sun, L. Lei, X. Tan, D. Guan, J. Wu, G. Kuang, Structured graph based image regression for unsupervised multimodal change detection, ISPRS Journal of Photogrammetry and Remote Sensing 185 (2022) 16–31
2022
Later among the works it cites.
C. Zhang, L. Wang, S. Cheng, Y. Li, Swinsunet: Pure transformer network for remote sensing image change detection, IEEE Transactions on Geoscience and Remote Sensing 60 (2022) 1–13
2022
Later among the works it cites.
Z. Li, C. Tang, L. Wang, A. Y. Zomaya, Remote sensing change detection via temporal feature interaction and guided refinement, IEEE Transactions on Geoscience and Remote Sensing 60 (2022) 1–11
2022
Later among the works it cites.
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Y. Liu, C. Pang, Z. Zhan, X. Zhang, X. Yang, Building change detection for remote sensing images using a dual-task constrained deep siamese convolutional network model, IEEE Geoscience and Remote Sensing Letters 18 (5) (2020) 811–815
2020
Cited alongside, same era.
C. Zhang, P. Yue, D. Tapete, L. Jiang, B. Shangguan, L. Huang, G. Liu, 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 (2020) 183–200
2020
Cited alongside, same era.
X. Peng, R. Zhong, Z. Li, Q. Li, Optical remote sensing image change detection based on attention mechanism and image difference, IEEE Transactions on Geoscience and Remote Sensing 59 (9) (2020) 7296–7307
2020
Cited alongside, same era.
C. Zhang, P. Yue, D. Tapete, L. Jiang, B. Shangguan, L. Huang, G. Liu, 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 (2020) 183–200
2020
Cited alongside, same era.
Z. Zheng, Y. Zhong, J. Wang, A. Ma, Foreground-aware relation network for geospatial object segmentation in high spatial resolution remote sensing imagery, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 4096–4105
2020
Cited alongside, same era.
S. Fang, K. Li, J. Shao, Z. Li, Snunet-cd: A densely connected siamese network for change detection of vhr images, IEEE Geoscience and Remote Sensing Letters 19 (2021) 1–5
2021
Cited alongside, same era.
H. Chen, Z. Qi, Z. Shi, Remote sensing image change detection with transformers, IEEE Transactions on Geoscience and Remote Sensing 60 (2021) 1–14
2021
Cited alongside, same era.
2021
Cited alongside, same era.
X. Song, Z. Hua, J. Li, Remote sensing image change detection transformer network based on dual-feature mixed attention, IEEE Transactions on Geoscience and Remote Sensing 60 (2022) 1–16
2022
Later among the works it cites.
Y. Feng, H. Xu, J. Jiang, H. Liu, J. Zheng, Icif-net: Intra-scale cross-interaction and inter-scale feature fusion network for bitemporal remote sensing images change detection, IEEE Transactions on Geoscience and Remote Sensing 60 (2022) 1–13
2022
Later among the works it cites.
T. Liu, M. Gong, D. Lu, Q. Zhang, H. Zheng, F. Jiang, M. Zhang, Building change detection for vhr remote sensing images via local–global pyramid network and cross-task transfer learning strategy, IEEE Transactions on Geoscience and Remote Sensing 60 (2022) 1–17
2022
Later among the works it cites.
S. Ren, D. Zhou, S. He, J. Feng, X. Wang, Shunted self-attention via multi-scale token aggregation, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 10853–10862
2022
Later among the works it cites.
M. Liu, Z. Chai, H. Deng, R. Liu, A cnn-transformer network with multi-scale context aggregation for fine-grained cropland change detection, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2022)
2022
Later among the works it cites.
X. Song, Z. Hua, J. Li, Remote sensing image change detection transformer network based on dual-feature mixed attention, IEEE Transactions on Geoscience and Remote Sensing 60 (2022) 1–16
2022
Later among the works it cites.
Y. Feng, J. Jiang, H. Xu, J. Zheng, Change detection on remote sensing images using dual-branch multilevel intertemporal network, IEEE Transactions on Geoscience and Remote Sensing 61 (2023) 1–15
2023
Later among the works it cites.
T. Lei, X. Geng, H. Ning, Z. Lv, M. Gong, Y. Jin, A. K. Nandi, Ultralightweight spatial–spectral feature cooperation network for change detection in remote sensing images, IEEE Transactions on Geoscience and Remote Sensing 61 (2023) 1–14
2023
Later among the works it cites.
Y. Ye, M. Wang, L. Zhou, G. Lei, J. Fan, Y. Qin, Adjacent-level feature cross-fusion with 3d cnn for remote sensing image change detection, IEEE Transactions on Geoscience and Remote Sensing (2023)
2023
Later among the works it cites.
C.-P. Chen, J.-W. Hsieh, P.-Y. Chen, Y.-K. Hsieh, B.-S. Wang, Saras-net: scale and relation aware siamese network for change detection, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37, 2023, pp. 14187–14195
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
M. Noman, M. Fiaz, H. Cholakkal, S. Narayan, R. M. Anwer, S. Khan, F. S. Khan, Remote sensing change detection with transformers trained from scratch, IEEE Transactions on Geoscience and Remote Sensing (2024)
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
M. Zhao, M. Ma, X. Li, X. Ma, W. Zhang, S. Song, Supervised detail-guided multi-scale state space model for pan-sharpening, IEEE Transactions on Geoscience and Remote Sensing (2024)
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
doi:10.3390/rs16050804
L. Wang, M. Zhang, X. Gao, W. Shi, Advances and challenges in deep learning-based change detection for remote sensing images: A review through various learning paradigms , Remote Sensing 16 (5) (2024) · 2072
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