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Parallel Connected Generative Adversarial Network with Quadratic Operation for SAR Image Generation and Application for Classification
Thanks to the availability of large-scale data, deep Convolutional Neural Networks (CNNs) have witnessed success in various applications of computer vision. However, the performance of CNNs on Synthetic Aperture Radar (SAR) image classification is unsatisfactory due to the lack of well-labeled SAR d...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412347/ https://www.ncbi.nlm.nih.gov/pubmed/30791500 http://dx.doi.org/10.3390/s19040871 |
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author | He, Chu Xiong, Dehui Zhang, Qingyi Liao, Mingsheng |
author_facet | He, Chu Xiong, Dehui Zhang, Qingyi Liao, Mingsheng |
author_sort | He, Chu |
collection | PubMed |
description | Thanks to the availability of large-scale data, deep Convolutional Neural Networks (CNNs) have witnessed success in various applications of computer vision. However, the performance of CNNs on Synthetic Aperture Radar (SAR) image classification is unsatisfactory due to the lack of well-labeled SAR data, as well as the differences in imaging mechanisms between SAR images and optical images. Therefore, this paper addresses the problem of SAR image classification by employing the Generative Adversarial Network (GAN) to produce more labeled SAR data. We propose special GANs for generating SAR images to be used in the training process. First, we incorporate the quadratic operation into the GAN, extending the convolution to make the discriminator better represent the SAR data; second, the statistical characteristics of SAR images are integrated into the GAN to make its value function more reasonable; finally, two types of parallel connected GANs are designed, one of which we call PWGAN, combining the Deep Convolutional GAN (DCGAN) and Wasserstein GAN with Gradient Penalty (WGAN-GP) together in the structure, and the other, which we call CNN-PGAN, applying a pre-trained CNN as a discriminator to the parallel GAN. Both PWGAN and CNN-PGAN consist of a number of discriminators and generators according to the number of target categories. Experimental results on the TerraSAR-X single polarization dataset demonstrate the effectiveness of the proposed method. |
format | Online Article Text |
id | pubmed-6412347 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-64123472019-04-03 Parallel Connected Generative Adversarial Network with Quadratic Operation for SAR Image Generation and Application for Classification He, Chu Xiong, Dehui Zhang, Qingyi Liao, Mingsheng Sensors (Basel) Article Thanks to the availability of large-scale data, deep Convolutional Neural Networks (CNNs) have witnessed success in various applications of computer vision. However, the performance of CNNs on Synthetic Aperture Radar (SAR) image classification is unsatisfactory due to the lack of well-labeled SAR data, as well as the differences in imaging mechanisms between SAR images and optical images. Therefore, this paper addresses the problem of SAR image classification by employing the Generative Adversarial Network (GAN) to produce more labeled SAR data. We propose special GANs for generating SAR images to be used in the training process. First, we incorporate the quadratic operation into the GAN, extending the convolution to make the discriminator better represent the SAR data; second, the statistical characteristics of SAR images are integrated into the GAN to make its value function more reasonable; finally, two types of parallel connected GANs are designed, one of which we call PWGAN, combining the Deep Convolutional GAN (DCGAN) and Wasserstein GAN with Gradient Penalty (WGAN-GP) together in the structure, and the other, which we call CNN-PGAN, applying a pre-trained CNN as a discriminator to the parallel GAN. Both PWGAN and CNN-PGAN consist of a number of discriminators and generators according to the number of target categories. Experimental results on the TerraSAR-X single polarization dataset demonstrate the effectiveness of the proposed method. MDPI 2019-02-19 /pmc/articles/PMC6412347/ /pubmed/30791500 http://dx.doi.org/10.3390/s19040871 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article He, Chu Xiong, Dehui Zhang, Qingyi Liao, Mingsheng Parallel Connected Generative Adversarial Network with Quadratic Operation for SAR Image Generation and Application for Classification |
title | Parallel Connected Generative Adversarial Network with Quadratic Operation for SAR Image Generation and Application for Classification |
title_full | Parallel Connected Generative Adversarial Network with Quadratic Operation for SAR Image Generation and Application for Classification |
title_fullStr | Parallel Connected Generative Adversarial Network with Quadratic Operation for SAR Image Generation and Application for Classification |
title_full_unstemmed | Parallel Connected Generative Adversarial Network with Quadratic Operation for SAR Image Generation and Application for Classification |
title_short | Parallel Connected Generative Adversarial Network with Quadratic Operation for SAR Image Generation and Application for Classification |
title_sort | parallel connected generative adversarial network with quadratic operation for sar image generation and application for classification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412347/ https://www.ncbi.nlm.nih.gov/pubmed/30791500 http://dx.doi.org/10.3390/s19040871 |
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