Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: He, Chu, Xiong, Dehui, Zhang, Qingyi, Liao, Mingsheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
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
_version_ 1783402584326799360
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
work_keys_str_mv AT hechu parallelconnectedgenerativeadversarialnetworkwithquadraticoperationforsarimagegenerationandapplicationforclassification
AT xiongdehui parallelconnectedgenerativeadversarialnetworkwithquadraticoperationforsarimagegenerationandapplicationforclassification
AT zhangqingyi parallelconnectedgenerativeadversarialnetworkwithquadraticoperationforsarimagegenerationandapplicationforclassification
AT liaomingsheng parallelconnectedgenerativeadversarialnetworkwithquadraticoperationforsarimagegenerationandapplicationforclassification