So Sánh Giữa Các Kiến Trúc Tương Đương Của Mạng Nơ-ron Giá Trị Phức và Giá Trị Thực - Ứng Dụng Trong Phân Đoạn Ảnh SAR Phân cực

Barrachina, J. A., Ren, C., Vieillard, G., Morisseau, C., & Ovarlez, J.-P. (2021). About the equivalence between complex-valued and real-valued fully connected neural networks - application to Polinsar images. In 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1–6). Chen, S., Wang, H., Xu, F., & Jin, Y.-Q. (2016). Target classification using the deep convolutional networks for SAR images. IEEE Transactions on Geoscience and Remote Sensing, 54(8), 4806–4817. Hou, B., Kou, H., & Jiao, L. (2016). Classification of polarimetric SAR images using multilayer autoencoders and superpixels. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(7), 3072–3081. Zhou, Y., Wang, H., Xu, F., & Jin, Y.-Q. (2016). Polarimetric SAR image classification using deep convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, 13(12), 1935–1939. Bassey, J., Qian, L., & Li, X. (2021). A survey of complex-valued neural networks. arXiv:2101.12249 Hirose, A., & Yoshida, S. (2012). Generalization characteristics of complex-valued feedforward neural networks in relation to signal coherence. IEEE Transactions on Neural Networks and learning systems, 23(4), 541–551. Hänsch, R., & Hellwich, O. (2009). Classification of polarimetric SAR data by complex valued neural networks. In ISPRS Workshop High-resolution Earth Imaging for Geospatial Information (vol. 38, pp. 4–7). Zhao, J., Datcu, M., Zhang, Z., Xiong, H., & Yu, W. (2019). Contrastive-regulated CNN in the complex domain: A method to learn physical scattering signatures from flexible PolSAR images. IEEE Transactions on Geoscience and Remote Sensing, 57(12), 10116–10135. Hirose, A. (2013). Complex-valued neural networks: Advances and applications. Hoboken, New Jersey: John Wiley & Sons. Hänsch, R. (2010). Complex-valued multi-layer perceptrons - an application to polarimetric SAR data. Photogrammetric Engineering & Remote Sensing, 76(9), 1081–1088. Hänsch, R., & Hellwich, O. (2010). Complex-valued convolutional neural networks for object detection in PolSAR data. In 8th European Conference on Synthetic Aperture Radar (pp. 1–4). De, S., Bruzzone, L., Bhattacharya, A., Bovolo, F., & Chaudhuri, S. (2017). A novel technique based on deep learning and a synthetic target database for classification of urban areas in PolSAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(1), 154–170. Cao, Y., Wu, Y., Zhang, P., Liang, W., & Li, M. (2019). Pixel-wise PolSAR image classification via a novel complex-valued deep fully convolutional network. Remote Sensing, 11(22), 2653. Zhang, Z., Wang, H., Xu, F., & Jin, Y.-Q. (2017). Complex-valued convolutional neural network and its application in polarimetric SAR image classification. IEEE Transactions on Geoscience and Remote Sensing, 55(12), 7177–7188. Sun, Q., Li, X., Li, L., Liu, X., Liu, F., & Jiao, L. (2019). Semi-supervised complex-valued GAN for polarimetric SAR image classification. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019) (pp. 3245–3248). Zhao, J., Datcu, M., Zhang, Z., Xiong, H., & Yu, W. (2019). Learning physical scattering patterns from PolSAR images by using complex-valued CNN. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019) (pp. 10019–10022). Qin, X., Zou, H., Yu, W., & Wang, P. (2021). Superpixel-oriented classification of polsar images using complex-valued convolutional neural network driven by hybrid data. IEEE Transactions on Geoscience and Remote Sensing, 59(12), 10094–10111. Kuroe, Y., Yoshid, M., & Mori, T. (2003). On activation functions for complex-valued neural networks: existence of energy functions. Artificial Neural Networks and Neural Information Processing, ICANN/ICONIP 2003 (pp. 985–992). Berlin, Heidelberg: Springer. Wirtinger, W. (1927). Zur formalen theorie der funktionen von mehr komplexen veränderlichen. Mathematische Annalen, 97(1), 357–375. Barrachina, J. A., Ren, C., Morisseau, C., Vieillard, G., & Ovarlez, J.-P. (2021). Complex-valued vs. real-valued neural networks for classification perspectives: An example on non-circular data. In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 2990–2994). https://doi.org/10.1109/ICASSP39728.2021.9413814 Hirose, A. (2012). Complex-valued neural networks (Vol. 400). Tokyo, Japan: Springer. Hirose, A. (2009). Complex-valued neural networks: The merits and their origins. In 2009 International Joint Conference on Neural Networks (pp. 1237–1244). Mönning, N., & Manandhar, S. (2018). Evaluation of complex-valued neural networks on real-valued classification tasks. arXiv preprint arXiv:1811.12351 Barrachina, J. A. (2020). NEGU93/CVNN: Complex Valued Neural Networks (CVNN). Zenodo. Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press, Genetic Programming and Evolvable Machines. He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1026–1034). Trabelsi, C., Bilaniuk, O., Zhang, Y., Serdyuk, D., Subramanian, S., Santos, J. F., Mehri, S., Rostamzadeh, N., Bengio, Y., & Pal, C. J. (2017). Deep complex networks. arXiv preprint arXiv:1705.09792 Zafar, I., Tzanidou, G., Burton, R., Patel, N., & Araujo, L. (2018). Hands-on convolutional neural networks with tensorflow: solve computer vision problems with modeling in tensorflow and Python. Birmingham, UK: Packt Publishing Ltd. Trabelsi, C., Bilaniuk, A., Zhang, Y., Serdyuk, D., Subramanian, S., Santos, J. F., Mehri, S., Rostamzadeh, N., Bengi, Y., & Pal, J. C. (2018). Deep Complex Networks. Lee, J. S., & Pottier, E. (2017). Polarimetric radar imaging: from basics to applications. Boca Raton: CRC press. Jiao, L., & Liu, F. (2016). Wishart deep stacking network for fast polsar image classification. IEEE Transactions on Image Processing, 25(7), 3273–3286. Guo, Y., Wang, S., Gao, C., Shi, D., Zhang, D., & Hou, B. (2015). Wishart RBM based DBN for polarimetric synthetic radar data classification. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 1841–1844). McGill, R., Tukey, J. W., & Larsen, W. A. (1978). Variations of box plots. The American Statistician, 32(1), 12–16. Chambers, J. M. (2018). Graphical methods for data analysis. Florida, US: CRC Press.