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: firstname.lastname@example.org
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
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