Abstract
At present, the neural network is often based on the real field of operation, research shows that, compared with the real field, the complex has incomparable advantages in the field of image processing, such as the complex represents more information, such as the phase information and modulus value, which play a great role in some fields. To take full advantage of complex data, This paper mainly studies CNN network, and through complex value processing, and get Complex Convolutional Neural Networks(CCN), complete the construction of complex convolution neural network. In order to study complex neural network, we start from two aspects, one is convolution operation, the other is network construction. In this paper, we use ENet as the basic structure of the model, replace the convolutional structure, pooling structure, and BatchNorm structure with the complex form, use it in the Flevoland dataset, and get a good test results.
This work was supported in part by the National Natural Science Foundation of China (Nos. 61906150, 62076192), the State Key Program of National Natural Science of China (No. 61836009), the Major Research Plan of the National Natural Science Foundation of China (Nos. 91438201, 91438103).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Harris, R.A.: Envisat: ASAR science and applications (1998)
Ulaby, F.T., Elachi, C.: Radar polarimetry for geoscience applications. 5(3), 38 (1990)
Khan, A., Sohail, A., Zahoora, U., et al.: A survey of the recent architectures of deep convolutional neural networks. Artif. Intell. Rev. (2020)
Tran, D., Bourdev, L., Fergus, R., et al.: Learning spatiotemporal features with 3D convolutional networks (2014)
Liu, F., Shen, C., Lin, G.: Deep convolutional neural fields for depth estimation from a single image. IEEE (2014)
Lecun, Y., Kavukcuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems. IEEE (2010)
Paszke, A., Chaurasia, A., Kim, S., et al.: ENet: a deep neural network architecture for real-time semantic segmentation (2016)
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions (2016)
Eigen, D., Fergus, R.: Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: 2015 IEEE International Conference on Computer Vision (ICCV) (2014)
Liu, F., Shen, C., Lin, G.: Deep convolutional neural fields for depth estimation from a single image. IEEE (2014)
Hadji, I., Wildes, R.P.: What do we understand about convolutional networks? (2018)
Zhou, Y., Wang, H., Xu, F., et al.: Polarimetric SAR image classification using deep convolutional neural networks. IEEE Geosci. Remote Sens. Lett. 13(12), 1935–1939 (2017)
Trabelsi, C., Bilaniuk, O., Zhang, Y., et al.: Deep complex networks (2017)
Liu, F., Jiao, L., Tang, X.: Task-oriented GAN for PolSAR image classification and clustering. IEEE Trans. Neural Netw. Learn. Syst. 1–13 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 IFIP International Federation for Information Processing
About this paper
Cite this paper
Li, L., Sun, Y., Ma, F., Ma, J., Jiao, L., Liu, F. (2022). Deep Complex Convolutional Neural Networks for Remote Sensing Image Classification. In: Shi, Z., **, Y., Zhang, X. (eds) Intelligence Science IV. ICIS 2022. IFIP Advances in Information and Communication Technology, vol 659. Springer, Cham. https://doi.org/10.1007/978-3-031-14903-0_27
Download citation
DOI: https://doi.org/10.1007/978-3-031-14903-0_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-14902-3
Online ISBN: 978-3-031-14903-0
eBook Packages: Computer ScienceComputer Science (R0)
