Fetching the paper…
Reading the bibliography…
Detecting changed regions in paired satellite images plays a key role in many remote sensing applications.
W. A. Malila, “Change vector analysis: an approach for detecting forest changes with landsat,” in LARS symposia
1980
Earlier work this paper cites.
T. Blaschke and G. J. Hay, “Object-oriented image analysis and scale-space: Theory and methods for modeling and evaluating multi-scale landscape structure,” ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
2001
Earlier work this paper cites.
P. C. Ng and S. Henikoff, “Sift: predicting amino acid changes that affect protein function,” Nucleic Acids Research
2003
Earlier work this paper cites.
D. Lu, P. Mausel, and E. F. Moran, “Change detection techniques,” International Journal of Remote Sensing
2004
Earlier work this paper cites.
R. J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, “Image change detection algorithms: a systematic survey,” IEEE Trans. Image Process
2005
Earlier work this paper cites.
P. Gamba, F. Dell’Acqua, and G. Lisini, “Change detection of multitemporal SAR data in urban areas combining feature-based and pixel-based techniques,” IEEE Trans. Geosci. Remote. Sens
2006
Earlier work this paper cites.
G. Moser and S. B. Serpico, “Generalized minimum-error thresholding for unsupervised change detection from SAR amplitude imagery,” IEEE Trans. Geosci. Remote. Sens
2006
Earlier work this paper cites.
G. Camps-Valls, L. Gómez-Chova, J. Muñoz-Marí, J. L. Rojo-Álvarez, and M. Martínez-Ramón, “Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection,” IEEE Trans. Geosci. Remote. Sens
2008
Earlier work this paper cites.
J. Im, J. Jensen, and J. Tullis, “Object-based change detection using correlation image analysis and image segmentation,” International Journal of Remote Sensing
2008
Earlier work this paper cites.
T. Çelik, “Unsupervised change detection in satellite images using principal component analysis and k -means clustering,” IEEE Geosci. Remote. Sens. Lett
2009
Earlier work this paper cites.
N. Longbotham, F. Pacifici, T. C. Glenn, A. Zare, M. Volpi, D. Tuia, E. Christophe, J. Michel, J. Inglada, J. Chanussot, and Q. Du, “Multi-modal change detection, application to the detection of flooded areas: Outcome of the 2009-2010 data fusion contest,” IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens
2012
Earlier work this paper cites.
G. Chen, G. J. Hay, L. M. T. De Carvalho, and M. A. Wulder, “Object-based change detection,” International Journal of Remote Sensing
2012
Earlier work this paper cites.
L. Giustarini, R. Hostache, P. Matgen, G. J. Schumann, P. D. Bates, and D. C. Mason, “A change detection approach to flood mapping in urban areas using terrasar-x,” IEEE Trans. Geosci. Remote. Sens
2013
Earlier work this paper cites.
C. Wu, B. Du, and L. Zhang, “Slow feature analysis for change detection in multispectral imagery,” IEEE Trans. Geosci. Remote. Sens
2013
Earlier work this paper cites.
H. Chen, F. Tang, P. Tiño, and X. Yao, “Model-based kernel for efficient time series analysis,” in The 19th ACM International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA, August 11-14
2013
Earlier work this paper cites.
F. Pacifici, N. Longbotham, and W. J. Emery, “The importance of physical quantities for the analysis of multitemporal and multiangular optical very high spatial resolution images,” IEEE Trans. Geosci. Remote. Sens
2014
Earlier work this paper cites.
B. Zhou, À. Lapedriza, J. Xiao, A. Torralba, and A. Oliva, “Learning deep features for scene recognition using places database,” in Annual Conference on Neural Information Processing Systems 2014, Montreal, Quebec, Canada, December 8-13
2014
Earlier work this paper cites.
B. Zhou, À. Lapedriza, J. Xiao, A. Torralba, and A. Oliva, “Learning deep features for scene recognition using places database,” in Annual Conference on Neural Information Processing Systems, Montreal, Quebec, Canada, December 8-13
2014
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,” in Annual Conference on Neural Information Processing Systems, Montreal, Quebec, Canada, December 8-13
2014
Earlier work this paper cites.
M. Mirza and S. Osindero, “Conditional generative adversarial nets,” CoRR
2014
Cited alongside, same era.
H. Chen, P. Tiño, A. Rodan, and X. Yao, “Learning in the model space for cognitive fault diagnosis,” IEEE Trans. Neural Networks Learn. Syst
2014
Cited alongside, same era.
C. Marin, F. Bovolo, and L. Bruzzone, “Building change detection in multitemporal very high resolution SAR images,” IEEE Trans. Geosci. Remote. Sens
2015
Cited alongside, same era.
C. Marin, F. Bovolo, and L. Bruzzone, “Building change detection in multitemporal very high resolution SAR images,” IEEE Trans. Geosci. Remote. Sens
2015
Cited alongside, same era.
H. Chen, F. Tang, P. Tiño, A. G. Cohn, and X. Yao, “Model metric co-learning for time series classification,” in Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina, July 25-31
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, “Improved training of wasserstein gans,” in Annual Conference on Neural Information Processing Systems, Long Beach, CA, USA, 4-9 December
2017
Later among the works it cites.
P. Isola, J. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, July 21-26
2017
Later among the works it cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” in Annual Conference on Neural Information Processing Systems, Long Beach, CA, USA, December 4-9
2017
Later among the works it cites.
Y. T. S. Correa, F. Bovolo, and L. Bruzzone, “An approach for unsupervised change detection in multitemporal VHR images acquired by different multispectral sensors,” Remote. Sens
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2015
Cited alongside, same era.
L. A. Gatys, A. S. Ecker, and M. Bethge, “Texture synthesis and the controlled generation of natural stimuli using convolutional neural networks,” in Bernstein Conference
2015
Cited alongside, same era.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in 3rd International Conference on Learning Representations, San Diego, CA, USA, May 7-9
2015
Cited alongside, same era.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proceedings of the 32nd International Conference on Machine Learning, Lille, France, July 6-11
2015
Cited alongside, same era.
G. Cao, X. Li, and L. Zhou, “Unsupervised change detection in high spatial resolution remote sensing images based on a conditional random field model,” Eur. J. Remote Sens
2016
Cited alongside, same era.
F. Thonfeld, H. Feilhauer, M. H. Braun, and G. Menz, “Robust change vector analysis (RCVA) for multi-sensor very high resolution optical satellite data,” Int. J. Appl. Earth Obs. Geoinformation
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 IEEE International Geoscience and Remote Sensing Symposium, Beijing, China, July 10-15
2016
Cited alongside, same era.
S. Nowozin, B. Cseke, and R. Tomioka, “f-gan: Training generative neural samplers using variational divergence minimization,” in Annual Conference on Neural Information Processing Systems, Barcelona, Spain, December 5-10
2016
Cited alongside, same era.
M. Lebedev, Y. V. Vizilter, O. Vygolov, V. Knyaz, and A. Y. Rubis, “Change detection in remote sensing images using conditional adversarial networks.,” International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences
2018
Later among the works it cites.
X. Wang, S. Liu, P. Du, H. Liang, J. Xia, and Y. Li, “Object-based change detection in urban areas from high spatial resolution images based on multiple features and ensemble learning,” Remote. Sens
2018
Later among the works it cites.
N. Lv, C. Chen, T. Qiu, and A. K. Sangaiah, “Deep learning and superpixel feature extraction based on contractive autoencoder for change detection in SAR images,” IEEE Trans. Ind. Informatics
2018
Later among the works it cites.
T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida, “Spectral normalization for generative adversarial networks,” in 6th International Conference on Learning Representations, Vancouver, BC, Canada, April 30 - May 3
2018
Later among the works it cites.
J. Wu, Z. Huang, J. Thoma, D. Acharya, and L. Van Gool, “Wasserstein divergence for gans,” in 15th European Conference, Munich, Germany, September 8-14
2018
Later among the works it cites.
J. Su, “GAN-QP: A novel GAN framework without gradient vanishing and lipschitz constraint,” CoRR
2018
Later among the works it cites.
U. Demir and G. B. Ünal, “Patch-based image inpainting with generative adversarial networks,” CoRR
2018
Later among the works it cites.
A. Valada, N. Radwan, and W. Burgard, “Deep auxiliary learning for visual localization and odometry,” in IEEE International Conference on Robotics and Automation, Brisbane, Australia, May 21-25
2018
Later among the works it cites.
D. Peng, Y. Zhang, and H. Guan, “End-to-end change detection for high resolution satellite images using improved unet++,” Remote. Sens
2019
Later among the works it cites.
G. Liu, Y. Gousseau, and F. Tupin, “A contrario comparison of local descriptors for change detection in very high spatial resolution satellite images of urban areas,” IEEE Trans. Geosci. Remote. Sens
2019
Later among the works it cites.
B. Du, L. Ru, C. Wu, and L. Zhang, “Unsupervised deep slow feature analysis for change detection in multi-temporal remote sensing images,” IEEE Trans. Geosci. Remote. Sens
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 Trans. Geosci. Remote. Sens
2019
Later among the works it cites.
R. Zhang, “Making convolutional networks shift-invariant again,” in Proceedings of the 36th International Conference on Machine Learning, Long Beach, California, USA, June 9-15
2019
Later among the works it cites.
Z. Gong, H. Chen, B. Yuan, and X. Yao, “Multiobjective learning in the model space for time series classification,” IEEE Trans. Cybern
2019
Later among the works it cites.
J. Liu, M. Gong, A. K. Qin, and K. C. Tan, “Bipartite differential neural network for unsupervised image change detection,” IEEE Trans. Neural Networks Learn. Syst
2020
Closest in time.