Polarimetric SAR image classification using 3D generative adversarial network
| Autor: | Liu Lu, Feng Guobao |
|---|---|
| Jazyk: | English<br />French |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | MATEC Web of Conferences, Vol 336, p 08012 (2021) |
| Druh dokumentu: | article |
| ISSN: | 2261-236X 20213360 |
| DOI: | 10.1051/matecconf/202133608012 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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