R. J. Radke, S. Andra, O. Al-kofahi, and B. Roysam, Image change detection algorithms: a systematic survey, IEEE Transactions on Image Processing, vol.14, issue.3, 2005.

M. Vakalopoulou, K. Karatzalos, N. Komodakis, and N. Paragios, Simultaneous registration and change detection in multitemporal, very high resolution remote sensing data, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2015.
URL : https://hal.archives-ouvertes.fr/hal-01264072

L. Mou, L. Bruzzone, and X. Zhu, Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery, CoRR, 2018.

R. C. Daudt, B. L. Saux, and A. Boulch, Fully convolutional siamese networks for change detection, ICIP, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01824557

O. Ronneberger, P. Fischer, and T. Brox, U-net: Convolutional networks for biomedical image segmentation, Medical Image Computing and Computer-Assisted Intervention -MIC-CAI 2015, 2015.

S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural computation, vol.9, issue.8, 1997.

R. C. Daudt, B. L. Saux, A. Boulch, and Y. Gousseau, Urban change detection for multispectral earth observation using convolutional neural networks, IEEE International Geoscience and Remote Sensing Symposium, IGARSS. IEEE, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01899024

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang et al., Automatic differentiation in pytorch, 2017.