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Image translation with convolutional neural networks has recently been used as an approach to multimodal change detection.
J. Cohen, “A coefficient of agreement for nominal scales,” Educational and Psychological Measurement , vol. 20, no. 1, pp. 37–46, 1960
1960
Earlier work this paper cites.
N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst. Man Cybern. , vol. 9, no. 1, pp. 62–66, 1979
1979
Earlier work this paper cites.
J. N. Kapur, P. K. Sahoo, and A. K. Wong, “A new method for gray-level picture thresholding using the entropy of the histogram,” Computer Vision, Graphics, and Image Processing , vol. 29, no. 3, pp. 273–285, 1985
1985
Earlier work this paper cites.
A. Singh, “Review article: Digital change detection techniques using remotely-sensed data,” Int. J. Remote Sens. , vol. 10, no. 6, pp. 989–1003, 1989
1989
Earlier work this paper cites.
A. G. Shanbhag, “Utilization of information measure as a means of image thresholding,” CVGIP: Graphical Models and Image Processing , vol. 56, no. 5, pp. 414–419, 1994
1994
Earlier work this paper cites.
M. P. Wand and M. C. Jones, Kernel Smoothing , ser. Monographs on Statistics and Applied Probability (Vol. 60). Chapman & Hall/CRC, 1995
1995
Earlier work this paper cites.
J.-C. Yen, F.-J. Chang, and S. Chang, “A new criterion for automatic multilevel thresholding,” IEEE Trans. Image Process. , vol. 4, no. 3, pp. 370–378, 1995
1995
Earlier work this paper cites.
F. R. Chung and F. C. Graham, Spectral Graph Theory . American Mathematical Society, 1997, no. 92
1997
Earlier work this paper cites.
J. Shi and J. Malik, “Normalized cuts and image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 22, no. 8, pp. 888–905, 2000
2000
Earlier work this paper cites.
H. L. V. Trees, Detection, Estimation, and Modulation Theory . John Wiley & Sons, 2001
2001
Earlier work this paper cites.
D. A. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing . John Wiley & Sons, 2005, vol. 29
2005
Earlier work this paper cites.
G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science , vol. 313, no. 5786, pp. 504–507, 2006
2006
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 Trans. Geosci. Remote Sens. , vol. 45, no. 1, pp. 218–236, 2007
2007
Earlier work this paper cites.
U. Von Luxburg, “A tutorial on spectral clustering,” Statistics and Computing , vol. 17, no. 4, pp. 395–416, 2007
2007
Earlier work this paper cites.
G. Mercier, G. Moser, and S. B. Serpico, “Conditional copulas for change detection in heterogeneous remote sensing images,” IEEE Trans. Geosci. Remote Sens. , vol. 46, no. 5, pp. 1428–1441, May 2008
2008
Earlier work this paper cites.
K. Koutroumbas and S. Theodoridis, Pattern Recognition . Elsevier Science, 2008. [Online]. Available: https://books.google.no/books?id=QgD-3Tcj8DkC
2008
Earlier work this paper cites.
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,” J. Mach. Learn. Res. , vol. 11, no. 110, pp. 3371–3408, 2010
2010
Earlier work this paper cites.
X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” Proc. Int. Conf. Artificial Intell. Statist. (AISTATS) , pp. 249–256, 2010
2010
Earlier work this paper cites.
P. Krähenbühl and V. Koltun, “Efficient inference in fully connected CRFs with Gaussian edge potentials,” Proc. Adv. Neural Inf. Process. Syst. , pp. 109–117, 2011
2011
Earlier work this paper cites.
J. N. Myhre and R. Jenssen, “Mixture weight influence on kernel entropy component analysis and semi-supervised learning using the Lasso,” Proc. IEEE Int. Workshop Mach. Learn. Signal Process. (MLSP) , pp. 1–6, 2012
2012
Earlier work this paper cites.
Y. A. LeCun, L. Bottou, G. B. Orr, and K.-R. Müller, Efficient backprop . Springer, 2012, pp. 9–48
2012
Earlier work this paper cites.
A. Moreira, P. Prats-Iraola, M. Younis, G. Krieger, I. Hajnsek, and K. P. Papathanassiou, “A tutorial on synthetic aperture radar,” IEEE Geosci. Remote Sens. Mag. , vol. 1, no. 1, pp. 6–43, 2013
2013
Earlier work this paper cites.
A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” Proc. Int. Conf. Mach. Learn. (ICML) , vol. 30, no. 1, p. 3, 2013
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Proc. Adv. Neural Inf. Process. Syst. (NIPS) , pp. 2672–2680, 2014
2014
Earlier work this paper cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. , vol. 15, no. 1, pp. 1929–1958, 2014
2014
Earlier work this paper cites.
M. Dalla Mura, S. Prasad, F. Pacifici, P. Gamba, J. Chanussot, and J. A. Benediktsson, “Challenges and opportunities of multimodality and data fusion in remote sensing,” Proc. IEEE , vol. 103, no. 9, pp. 1585–1601, 2015
2015
Cited alongside, same era.
J. Prendes, M. Chabert, F. Pascal, A. Giros, and J.-Y. Tourneret, “A new multivariate statistical model for change detection in images acquired by homogeneous and heterogeneous sensors,” IEEE Trans. Image Process. , vol. 24, no. 3, pp. 799–812, 2015
2015
Cited alongside, same era.
L. Gomez-Chova, D. Tuia, G. Moser, and G. Camps-Valls, “Multimodal classification of remote sensing images: A review and future directions,” Proceedings of the IEEE , vol. 103, no. 9, 2015
2015
Cited alongside, same era.
F. Bovolo and L. Bruzzone, “The time variable in data fusion: A change detection perspective,” IEEE Geosci. Remote Sens. Mag. , vol. 3, no. 3, pp. 8–26, 2015
2015
Cited alongside, same era.
X. Niu, M. Gong, T. Zhan, and Y. Yang, “A conditional adversarial network for change detection in heterogeneous images,” IEEE Geosci. Remote Sens. Lett. , vol. 16, no. 1, pp. 45–49, 2018
2018
Later among the works it cites.
L. Mou, L. Bruzzone, and X. X. Zhu, “Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery,” IEEE Trans. Geosci. Remote Sens. , vol. 57, no. 2, pp. 924–935, 2018
2018
Later among the works it cites.
T. Zhan, M. Gong, X. Jiang, and S. Li, “Log-based transformation feature learning for change detection in heterogeneous images,” IEEE Geosci. Remote Sens. Lett. , vol. 15, no. 9, pp. 1352–1356, 2018
2018
Later among the works it cites.
R. Touati and M. Mignotte, “An energy-based model encoding nonlocal pairwise pixel interactions for multisensor change detection,” IEEE Trans. Geosci. Remote Sens. , vol. 56, no. 2, pp. 1046–1058, 2018
2018
Later among the works it cites.
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M. Volpi, G. Camps-Valls, and D. Tuia, “Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis,” ISPRS J. Photogram. Remote Sens. , vol. 107, pp. 50–63, 2015
2015
Cited alongside, same era.
2015
Cited alongside, same era.
J. Liu, M. Gong, K. Qin, and P. Zhang, “A deep convolutional coupling network for change detection based on heterogeneous optical and radar images,” IEEE Trans. Neural Netw. Learn. Syst. , vol. 29, no. 3, pp. 545–559, 2016
2016
Cited alongside, same era.
M. Gong, J. Zhao, J. Liu, Q. Miao, and L. Jiao, “Change detection in synthetic aperture radar images based on deep neural networks,” IEEE Trans. Neural Netw. Learn. Syst. , vol. 27, no. 1, pp. 125–138, 2016
2016
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 Sens. , vol. 8, no. 6, p. 506, 2016
2016
Cited alongside, same era.
M. Gong, P. Zhang, L. Su, and J. Liu, “Coupled dictionary learning for change detection from multisource data,” IEEE Trans. Geosci. Remote Sens. , vol. 54, no. 12, pp. 7077–7091, 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 J. Photogram. Remote Sens. , vol. 116, pp. 24–41, 2016
2016
Cited alongside, same era.
M. Maire, T. Narihira, and S. X. Yu, “Affinity cnn: Learning pixel-centric pairwise relations for figure/ground embedding,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 174–182
2016
Cited alongside, same era.
N. Audebert, B. Le Saux, and S. Lefèvre, “Beyond rgb: Very high resolution urban remote sensing with multimodal deep networks,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 140, pp. 20–32, 2018
2018
Later among the works it cites.
P. Benedetti, D. Ienco, R. Gaetano, K. Ose, R. G. Pensa, and S. Dupuy, “M3 fusion: A deep learning architecture for multiscale multimodal multitemporal satellite data fusion,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 11, no. 12, pp. 4939–4949, 2018
2018
Later among the works it cites.
N. Yokoya, P. Ghamisi, J. Xia, S. Sukhanov, R. Heremans, I. Tankoyeu, B. Bechtel, B. Le Saux, G. Moser, and D. Tuia, “Open data for global multimodal land use classification: Outcome of the 2017 ieee grss data fusion contest,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 11, no. 5, pp. 1363–1377, 2018
2018
Later among the works it cites.
N. Merkle, S. Auer, R. Müller, and P. Reinartz, “Exploring the potential of conditional adversarial networks for optical and sar image matching,” IEEE J. Select. Topics Appl. Earth Obs. Remote Sens. , vol. 11, no. 6, pp. 1811–1820, 2018
2018
Later among the works it cites.
Z. Murez, S. Kolouri, D. Kriegman, R. Ramamoorthi, and K. Kim, “Image to image translation for domain adaptation,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recogn. (CVPR) , pp. 4500–4509, 2018
2018
Later among the works it cites.
X. Cheng, P. Wang, and R. Yang, “Depth estimation via affinity learned with convolutional spatial propagation network,” in European Conference on Computer Vision (ECCV) , 2018, pp. 103–119
2018
Later among the works it cites.
S. J. Reddi, S. Kale, and S. Kumar, “On the convergence of adam and beyond,” Proc. Int. Conf. Learn. Represent. (ICLR) , 2018
2018
Later among the works it cites.
M. Caron, P. Bojanowski, A. Joulin, and M. Douze, “Deep clustering for unsupervised learning of visual features,” pp. 132–149, 2018
2018
Later among the works it cites.
D. Ulyanov, A. Vedaldi, and V. Lempitsky, “Deep image prior,” pp. 9446–9454, 2018
2018
Later among the works it cites.
R. Touati, M. Mignotte, and M. Dahmane, “Change detection in heterogeneous remote sensing images based on an imaging modality-invariant MDS representation,” in Proc. IEEE Int. Conf. Image Process. (ICIP) , 2018, pp. 3998–4002
2018
Later among the works it cites.
D. Solarna, G. Moser, and S. B. Serpico, “A markovian approach to unsupervised change detection with multiresolution and multimodality sar data,” Remote Sens. , vol. 10, no. 11, 2018
2018
Later among the works it cites.
P. Ghamisi, B. Rasti, N. Yokoya, Q. Wang, B. Hofle, L. Bruzzone, F. Bovolo, M. Chi, K. Anders, and R. Gloaguen, “Multisource and multitemporal data fusion in remote sensing: A comprehensive review of the state of the art,” IEEE Geosci. Remote Sens. Mag. , vol. 7, no. 1, pp. 6–39, 2019
2019
Later among the works it cites.
M. Gong, X. Niu, T. Zhan, and M. Zhang, “A coupling translation network for change detection in heterogeneous images,” Int. J. Remote Sens. , vol. 40, no. 9, pp. 3647–3672, 2019
2019
Later among the works it cites.
L. T. Luppino, F. M. Bianchi, G. Moser, and S. N. Anfinsen, “Unsupervised image regression for heterogeneous change detection,” IEEE Trans. Geosci. Remote Sens. , vol. 57, no. 12, pp. 9960–9975, 2019
2019
Later among the works it cites.
V. Ferraris, N. Dobigeon, Y. Cavalcanti, T. Oberlin, and M. Chabert, “Coupled dictionary learning for unsupervised change detection between multimodal remote sensing images,” Comput. Vis. Im. Und. , vol. 189, p. 102817, 2019
2019
Later among the works it cites.
R. Touati, M. Mignotte, and M. Dahmane, “A reliable mixed-norm-based multiresolution change detector in heterogeneous remote sensing images,” IEEE J. Select. Topics Appl. Earth Obs. Remote Sens. , vol. 12, no. 9, pp. 3588–3601, 2019
2019
Later among the works it cites.
M. Mignotte, “A fractal projection and markovian segmentation-based approach for multimodal change detection,” IEEE Trans. Geosci. Remote Sens. , 2020
2020
Closest in time.
D. Hong, L. Gao, N. Yokoya, J. Yao, J. Chanussot, Q. Du, and B. Zhang, “More diverse means better: Multimodal deep learning meets remote-sensing imagery classification,” IEEE Transactions on Geoscience and Remote Sensing , pp. 1–15, 2020
2020
Closest in time.
B. Rasti, D. Hong, R. Hang, P. Ghamisi, X. Kang, J. Chanussot, and J. A. Benediktsson, “Feature extraction for hyperspectral imagery: The evolution from shallow to deep (overview and toolbox),” IEEE Geoscience and Remote Sensing Magazine , pp. 0–0, 2020
2020
Closest in time.
R. Touati, M. Mignotte, and M. Dahmane, “Anomaly feature learning for unsupervised change detection in heterogeneous images: A deep sparse residual model,” IEEE J. Select. Topics Appl. Earth Obs. Remote Sens. , vol. 13, pp. 588–600, 2020
2020
Closest in time.