MATEC Web Conf.
Volume 336, 20212020 2nd International Conference on Computer Science Communication and Network Security (CSCNS2020)
|Number of page(s)||5|
|Section||Network and Information Security|
|Published online||15 February 2021|
Polarimetric SAR image classification using 3D generative adversarial network
1 School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
2 Shaanxi Key Laboratory for Network Computing and Security Technology, Xi’an University of Technology, Xi’an 710048, China
3 National Key Lab of Science and Technology on Space Microwave, China Academy of Space Technology, Xi’an 710100, China
* Corresponding author: email@example.com
In this paper, a new architecture of three-dimensional deep convolutional generative adversarial network(3D-DCGAN) is specially defined to solve the unstable training problem of GAN and make full use of the information involved in polarimetric data. Firstly, a data cube with nine components of polarimetric coherency matrix are directly used as the input features of DCGAN. After that, a 3D convolutional model is designed as the components of generator and discriminator to construct the 3D-DCGAN, which considers the effective feature extraction capability of 3D convolutional neural network(CNN). Finally parameters of the network are fine-tuned to realize the polarimetric SAR image classification. The experiments results show the feasibility and efficiency of the proposed method.
Key words: Polarimetric SAR image classification / Generative adversarial network / Three-dimensional convolutional
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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