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Detecting changes on the ground in multitemporal Earth observation data is one of the key problems in remote sensing.
N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Transactions on Systems, Man, and Cybernetics , vol. 9, no. 1, pp. 62–66, 1979
1979
Earlier work this paper cites.
A. Singh, “Review article digital change detection techniques using remotely-sensed data,” International journal of remote sensing , vol. 10, no. 6, pp. 989–1003, 1989
1989
Earlier work this paper cites.
L. Bruzzone and D. F. Prieto, “Automatic analysis of the difference image for unsupervised change detection,” IEEE Transactions on Geoscience and Remote sensing , vol. 38, no. 3, pp. 1171–1182, 2000
2000
Earlier work this paper cites.
——, “A minimum-cost thresholding technique for unsupervised change detection,” International Journal of Remote Sensing , vol. 21, no. 18, pp. 3539–3544, 2000
2000
Earlier work this paper cites.
P. L. Rosin, “Unimodal thresholding,” Pattern recognition , vol. 34, no. 11, pp. 2083–2096, 2001
2001
Earlier work this paper cites.
F. Bovolo and L. Bruzzone, “A theoretical framework for unsupervised change detection based on change vector analysis in the polar domain,” IEEE Transactions on Geoscience and Remote Sensing , vol. 45, no. 1, pp. 218–236, 2006
2006
Earlier work this paper cites.
Y. Bazi, L. Bruzzone, and F. Melgani, “Image thresholding based on the em algorithm and the generalized gaussian distribution,” Pattern Recognition , vol. 40, no. 2, pp. 619–634, 2007
2007
Earlier work this paper cites.
F. Bovolo, “A multilevel parcel-based approach to change detection in very high resolution multitemporal images,” IEEE Geoscience and Remote Sensing Letters , vol. 6, no. 1, pp. 33–37, 2008
2008
Earlier work this paper cites.
M. Dalla Mura, J. A. Benediktsson, F. Bovolo, and L. Bruzzone, “An unsupervised technique based on morphological filters for change detection in very high resolution images,” IEEE Geoscience and Remote Sensing Letters , vol. 5, no. 3, pp. 433–437, 2008
2008
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 , vol. 6, no. 4, pp. 772–776, 2009
2009
Earlier work this paper cites.
M. Heikkilä, M. Pietikäinen, and C. Schmid, “Description of interest regions with local binary patterns,” Pattern recognition , vol. 42, no. 3, pp. 425–436, 2009
2009
Earlier work this paper cites.
L. Bruzzone and F. Bovolo, “A conceptual framework for change detection in very high resolution remote sensing images,” in 2010 IEEE International Geoscience and Remote Sensing Symposium . IEEE, 2010, pp. 2555–2558
2010
Earlier work this paper cites.
N. Coudray, J.-L. Buessler, and J.-P. Urban, “Robust threshold estimation for images with unimodal histograms,” Pattern Recognition Letters , vol. 31, no. 9, pp. 1010–1019, 2010
2010
Earlier work this paper cites.
D. Lu, E. Moran, and S. Hetrick, “Detection of impervious surface change with multitemporal landsat images in an urban–rural frontier,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 66, no. 3, pp. 298–306, 2011
2011
Earlier work this paper cites.
G. Chen and G. J. Hay, “An airborne lidar sampling strategy to model forest canopy height from quickbird imagery and geobia,” Remote Sensing of Environment , vol. 115, no. 6, pp. 1532–1542, 2011
2011
Earlier work this paper cites.
F. Bovolo, S. Marchesi, and L. Bruzzone, “A framework for automatic and unsupervised detection of multiple changes in multitemporal images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 50, no. 6, pp. 2196–2212, 2011
2011
Earlier work this paper cites.
M. Drusch, U. Del Bello, S. Carlier, O. Colin, V. Fernandez, F. Gascon, B. Hoersch, C. Isola, P. Laberinti, P. Martimort et al. , “Sentinel-2: Esa’s optical high-resolution mission for gmes operational services,” Remote sensing of Environment , vol. 120, pp. 25–36, 2012
2012
Earlier work this paper cites.
G.-A. Bilodeau, J.-P. Jodoin, and N. Saunier, “Change detection in feature space using local binary similarity patterns,” in 2013 International Conference on Computer and Robot Vision . IEEE, 2013, pp. 106–112
2013
Earlier work this paper cites.
Y. T. S. Correa, F. Bovolo, and L. Bruzzone, “Change detection in very high resolution multisensor images,” in Image and Signal Processing for Remote Sensing XX , vol. 9244. International Society for Optics and Photonics, 2014, p. 924410
2014
Earlier work this paper cites.
K. Rokni, A. Ahmad, K. Solaimani, and S. Hazini, “A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques,” International Journal of Applied Earth Observation and Geoinformation , vol. 34, pp. 226 – 234, 2015. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0303243414001780
2015
Cited alongside, same era.
M. Zanetti, F. Bovolo, and L. Bruzzone, “Rayleigh-rice mixture parameter estimation via em algorithm for change detection in multispectral images,” IEEE Transactions on Image Processing , vol. 24, no. 12, pp. 5004–5016, 2015
2015
Cited alongside, same era.
H. Lyu, H. Lu, and L. Mou, “Learning a transferable change rule from a recurrent neural network for land cover change detection,” Remote Sensing , vol. 8, no. 6, p. 506, 2016
2016
Cited alongside, same era.
P. Zhang, M. Gong, L. Su, J. Liu, and Z. Li, “Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 116, pp. 24–41, 2016
M. Li, M. Li, P. Zhang, Y. Wu, W. Song, and L. An, “Sar image change detection using pcanet guided by saliency detection,” IEEE Geoscience and Remote Sensing Letters , vol. 16, no. 3, pp. 402–406, 2018
2018
Later among the works it cites.
N. Gupta, G. V. Pillai, and S. Ari, “Change detection in optical satellite images based on local binary similarity pattern technique,” IEEE Geoscience and Remote Sensing Letters , vol. 15, no. 3, pp. 389–393, 2018
2018
Later among the works it cites.
R. Caye Daudt, B. Le Saux, A. Boulch, and Y. Gousseau, “Urban change detection for multispectral earth observation using convolutional neural networks,” in IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , July 2018
2018
Later among the works it cites.
S. Ji, Y. Shen, M. Lu, and Y. Zhang, “Building instance change detection from large-scale aerial images using convolutional neural networks and simulated samples,” Remote Sensing , vol. 11, no. 11, p. 1343, 2019
2019
Later among the works it cites.
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2016
Cited alongside, same era.
F. Gao, J. Dong, B. Li, and Q. Xu, “Automatic change detection in synthetic aperture radar images based on pcanet,” IEEE Geoscience and Remote Sensing Letters , vol. 13, no. 12, pp. 1792–1796, 2016
2016
Cited alongside, same era.
F. Thonfeld, H. Feilhauer, M. Braun, and G. Menz, “Robust change vector analysis (rcva) for multi-sensor very high resolution optical satellite data,” International Journal of Applied Earth Observation and Geoinformation , vol. 50, pp. 131–140, 2016
2016
Cited alongside, same era.
L. Li, X. Li, Y. Zhang, L. Wang, and G. Ying, “Change detection for high-resolution remote sensing imagery using object-oriented change vector analysis method,” in 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) . IEEE, 2016, pp. 2873–2876
2016
Cited alongside, same era.
N. Falco, G. Cavallaro, P. R. Marpu, and J. A. Benediktsson, “Unsupervised change detection analysis to multi-channel scenario based on morphological contextual analysis,” in 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) . IEEE, 2016, pp. 3374–3377
2016
Cited alongside, same era.
D. Wang, D. W. Hogg, D. Foreman-Mackey, and B. Schölkopf, “A causal, data-driven approach to modeling the kepler data,” Publications of the Astronomical Society of the Pacific , vol. 128, no. 967, p. 094503, 2016
2016
Cited alongside, same era.
B. Schölkopf, D. W. Hogg, D. Wang, D. Foreman-Mackey, D. Janzing, C.-J. Simon-Gabriel, and J. Peters, “Modeling confounding by half-sibling regression,” Proceedings of the National Academy of Sciences , vol. 113, no. 27, pp. 7391–7398, 2016
2016
Cited alongside, same era.
X. X. Zhu, D. Tuia, L. Mou, G.-S. Xia, L. Zhang, F. Xu, and F. Fraundorfer, “Deep learning in remote sensing: A comprehensive review and list of resources,” IEEE Geoscience and Remote Sensing Magazine , vol. 5, no. 4, pp. 8–36, 2017
2017
Cited alongside, same era.
Y. Zhan, K. Fu, M. Yan, X. Sun, H. Wang, and X. Qiu, “Change detection based on deep siamese convolutional network for optical aerial images,” IEEE Geoscience and Remote Sensing Letters , vol. 14, no. 10, pp. 1845–1849, 2017
2017
Cited alongside, same era.
Y. Gao, F. Gao, J. Dong, and S. Wang, “Transferred deep learning for sea ice change detection from synthetic-aperture radar images,” IEEE Geoscience and Remote Sensing Letters , vol. 16, no. 10, pp. 1655–1659, 2019
2019
Later among the works it cites.
R. Gupta, B. Goodman, N. Patel, R. Hosfelt, S. Sajeev, E. Heim, J. Doshi, K. Lucas, H. Choset, and M. Gaston, “Creating xbd: A dataset for assessing building damage from satellite imagery,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2019, pp. 10–17
2019
Later among the works it cites.
S. Saha, F. Bovolo, and L. Bruzzone, “Unsupervised deep change vector analysis for multiple-change detection in vhr images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 57, no. 6, pp. 3677–3693, 2019
2019
Later among the works it cites.
M. Gong, Y. Yang, T. Zhan, X. Niu, and S. Li, “A generative discriminatory classified network for change detection in multispectral imagery,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 12, no. 1, pp. 321–333, 2019
2019
Later among the works it cites.
F. Gao, X. Wang, Y. Gao, J. Dong, and S. Wang, “Sea ice change detection in sar images based on convolutional-wavelet neural networks,” IEEE Geoscience and Remote Sensing Letters , vol. 16, no. 8, pp. 1240–1244, 2019
2019
Later among the works it cites.
L. Moya, A. Muhari, B. Adriano, S. Koshimura, E. Mas, L. R. Marval-Perez, and N. Yokoya, “Detecting urban changes using phase correlation and l1-based sparse model for early disaster response: A case study of the 2018 sulawesi indonesia earthquake-tsunami,” Remote Sensing of Environment , vol. 242, p. 111743, 2020
2020
Later among the works it cites.
S. Saha, L. Mou, X. X. Zhu, F. Bovolo, and L. Bruzzone, “Semisupervised change detection using graph convolutional network,” IEEE Geoscience and Remote Sensing Letters , 2020
2020
Later among the works it cites.
S. Saha, L. Mou, C. Qiu, X. X. Zhu, F. Bovolo, and L. Bruzzone, “Unsupervised deep joint segmentation of multitemporal high-resolution images,” IEEE Transactions on Geoscience and Remote Sensing , 2020
2020
Later among the works it cites.
T. Zhan, M. Gong, X. Jiang, and M. Zhang, “Unsupervised scale-driven change detection with deep spatial-spectral features for vhr images,” IEEE Transactions on Geoscience and Remote Sensing , 2020
2020
Later among the works it cites.
S. Saha, Y. T. Solano-Correa, F. Bovolo, and L. Bruzzone, “Unsupervised deep transfer learning-based change detection for hr multispectral images,” IEEE Geoscience and Remote Sensing Letters , 2020
2020
Later among the works it cites.
Y. Sun, L. Lei, X. Li, X. Tan, and G. Kuang, “Patch similarity graph matrix-based unsupervised remote sensing change detection with homogeneous and heterogeneous sensors,” IEEE Transactions on Geoscience and Remote Sensing , 2020
2020
Later among the works it cites.
T. D. Gebhard, M. J. Bonse, S. P. Quanz, and B. Schölkopf, “Physically constrained causal noise models for high-contrast imaging of exoplanets,” 10 2020
2020
Later among the works it cites.
A. Song, Y. Kim, and Y. Han, “Uncertainty analysis for object-based change detection in very high-resolution satellite images using deep learning network,” Remote Sensing , vol. 12, no. 15, p. 2345, 2020
2020
Later among the works it cites.
H. Dong, W. Ma, Y. Wu, J. Zhang, and L. Jiao, “Self-supervised representation learning for remote sensing image change detection based on temporal prediction,” Remote Sensing , vol. 12, no. 11, p. 1868, 2020
2020
Later among the works it cites.