Polarimetric Sar Image Classification Using 3d Generative Adversarial Network
2020 2ND INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE COMMUNICATION AND NETWORK SECURITY (CSCNS2020)(2021)
摘要
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.
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关键词
Polarimetric SAR image classification, Generative adversarial network, Three-dimensional convolutional
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