Change Detection and Image Time Series Analysis 2. Группа авторов
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and Remote Sensing, 37(3), 1350–1359.
Burt, P. (1984). The Pyramid as a Structure for Efficient Computation. Springer, Berlin, Heidelberg.
Carvalho, M., Le Saux, B., Trouvé-Peloux, P., Champagnat, F., Almansa, A. (2019). Multitask learning of height and semantics from aerial images. IEEE Geoscience and Remote Sensing Letters, pp. 1391–1395.
Celeux, G., Chauveau, D., Diebolt, J. (1996). Stochastic versions of the EM algorithm: An experimental study in the mixture case. Journal of Statistical Computation and Simulation, 55(4), 287–314.
Chardin, A. (2000). Modèles énergétiques hiérarchiques pour la résolution des problèmes inverses en analyse d’images: application à la télédetection. PhD Thesis, University of Rennes 1, France.
Chen, Y., Li, C., Ghamisi, P., Jia, X., Gu, Y. (2017). Deep fusion of remote sensing data for accurate classification. IEEE Geoscience and Remote Sensing Letters, 14(8), 1253–1257.
Cheng, X., Zheng, Y., Zhang, J., Yang, Z. (2020). Multi-task multi-source deep correlation filter for remote sensing data fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 3723–3734.
Chibani, Y. and Houacine, A. (2003). Redundant versus orthogonal wavelet decomposition for multisensor image fusion. Pattern Recognition, 36(4), 879–887.
Choi, M., Kim, R., Nam, M.-R., Kim, H. (2005). Fusion of multispectral and panchromatic satellite images using the curvelet transform. IEEE Geoscience and Remote Sensing Letters, 2(2), 136–140.
Demirel, H. and Anbarjafari, G. (2010). Satellite image resolution enhancement using complex wavelet transform. IEEE Geoscience and Remote Sensing Letters, 7(1), 123–126.
Dousset, B. and Gourmelon, F. (2003). Satellite multi-sensor data analysis of urban surface temperatures and landcover. ISPRS Journal of Photogrammetry and Remote Sensing, 58(1), 43–54.
Forster, B., Van De Ville, D., Berent, J., Sage, D., Unser, M. (2004). Complex wavelets for extended depth-of-field: A new method for the fusion of multichannel microscopy images. Microscopy Research and Technique, 65(1–2), 33–42.
Fukunaga, K. (2013). Introduction to Statistical Pattern Recognition. Academic Press, Cambridge, MA.
Gamba, P., Lisini, G., Tomás, L., Almeida, C., Fonseca, L. (2011). Joint VHR-LIDAR classification framework in urban areas using a priori knowledge and post processing shape optimization. IEEE Urban Remote Sensing Event (JURSE), pp. 93–96.
Ghamisi, P., Höfle, B., Zhu, X.X. (2016). Hyperspectral and LiDAR data fusion using extinction profiles and deep convolutional neural network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(6), 3011–3024.
Gómez-Chova, L., Tuia, D., Moser, G., Camps-Valls, G. (2015). Multimodal classification of remote sensing images: A review and future directions. Proceedings of the IEEE, 103(9), 1560–1584.
Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep Learning. MIT Press, Cambridge, MA.
Graffigne, C., Heitz, F., Perez, P., Preteux, F., Sigelle, M., Zerubia, J. (1995). Hierarchical Markov random field models applied to image analysis: A review. International Symposium on Optical Science, Engineering, and Instrumentation (SPIE), International Society for Optics and Photonics, pp. 2–17.
Hall, D. and Llinas, J. (2001). Multisensor Data Fusion. CRC Press, Boca Raton, FL.
Hedhli, I., Moser, G., Zerubia, J., Serpico, S. (2014). New cascade model for hierarchical joint classification of multitemporal, multiresolution and multisensor remote sensing data. IEEE International Conference on Image Processing (ICIP).
Hedhli, I., Moser, G., Serpico, S., Zerubia, J. (2015). New hierarchical joint classification method of SAR-optical multiresolution remote sensing data. IEEE European Signal Processing Conference.
Hedhli, I., Moser, G., Zerubia, J., Serpico, S.B. (2016). A new cascade model for the hierarchical joint classification of multitemporal and multiresolution remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 54(11), 6333–6348.
Huang, B. and Song, H. (2012). Spatiotemporal reflectance fusion via sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 50(10), 3707–3716.
Iqbal, M., Ghafoor, A., Siddiqui, A. (2013). Satellite image resolution enhancement using dual-tree complex wavelet transform and nonlocal means. IEEE Geoscience and Remote Sensing Letters, 10(3), 451–455.
Kato, Z. and Zerubia, J. (2012). Markov Random Fields in Image Segmentation. Now Publishers Inc, Hanover, MA.
Krylov, V., Moser, G., Serpico, S., Zerubia, J. (2013). On the method of logarithmic cumulants for parametric probability density function estimation. IEEE Transactions on Image Processing, 22(10), 3791–3806.
Laferté, J.-M., Pérez, P., Heitz, F. (2000). Discrete Markov image modeling and inference on the quadtree. IEEE Transactions on Image Processing, 9(3), 390–404.
Landgrebe, D. (2003). Signal Theory Methods in Multispectral Remote Sensing, vol. 29. John Wiley & Sons, Hoboken, NJ.
Li, S. (2009). Markov Random Field Modeling in Image Analysis. Springer Science & Business Media, Berlin, Heidelberg.
Li, H., Manjunath, B., Mitra, S. (1995). Multisensor image fusion using the wavelet transform. Graphical Models and Image Processing, 57(3), 235–245.
Li, H.-C., Hong, W., Wu, Y.-R., Fan, P.-Z. (2011). On the empirical-statistical modeling of SAR images with generalized Gamma distribution. IEEE Journal of Selected Topics in Signal Processing, 5(3), 386–397.
Mallat, S. (2008). A Wavelet Tour of Signal Processing, 3rd edition. Academic Press, Cambridge, MA.
Mas, J. and Flores, J. (2008). The application of artificial neural networks to the analysis of remotely sensed data. International Journal of Remote Sensing, 29(3), 617–663.
Minh, D.H.T., Ienco, D., Gaetano, R., Lalande, N., Ndikumana, E., Osman, F., Maurel, P. (2018). Deep recurrent neural networks for winter vegetation quality mapping via multitemporal SAR sentinel-1. IEEE Geoscience and Remote Sensing Letters, 15(3), 464–468.
Moser, G. and Serpico, S. (2009). Unsupervised change detection from multichannel SAR data by Markovian data fusion. IEEE Transactions on Geoscience and Remote Sensing, 47(7), 2114–2128.
Muñoz-Marí, J., Bovolo, F., Gómez-Chova, L., Bruzzone, L., Camp-Valls, G. (2010). Semisupervised one-class support vector machines for classification of remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 48(8), 3188–3197.
Nelson, J.D.B., Gibberd, A.J., Nafornita, C., Kingsbury, N. (2018). The locally stationary dual-tree complex wavelet model. Statistics and Computing, 28(6), 1139–1154.
Nencini, F., Garzelli, A., Baronti, S., Alparone, L. (2007). Remote sensing image fusion using the curvelet transform. Information Fusion, 8(2), 143–156.
Nguyen, N., Nasrabadi, N., Tran, T. (2011). Robust multi-sensor classification via joint sparse representation. International Conference on Information Fusion.
Pan, S., Wu, J., Zhu, X., Zhang, C., Philip, S.Y. (2015). Joint structure feature exploration and regularization for multi-task graph classification. IEEE Transactions on Knowledge and Data Engineering, 28(3), 715–728.
Pérez, P. (1993). Champs markoviens et analyse multirésolution de l’image : application à l’analyse du mouvement. PhD Thesis, University of Rennes 1, France.
Piella, G. (2003). Adaptive wavelets and their applications to image fusion and compression. Thesis, PhD Thesis, University of Amsterdam.
Pohl, C. and van Genderen,