A deep convolutional generative adversarial networks (DCGANs)-based semi-supervised method for object recognition in synthetic aperture radar (SAR) images

Research output: Research - peer-reviewArticle

Standard

A deep convolutional generative adversarial networks (DCGANs)-based semi-supervised method for object recognition in synthetic aperture radar (SAR) images. / Gao, Fei; Yang, Yue; Wang, Jun; Sun, Jinping; Yang, Erfu; Zhou, Huiyu.

In: Remote Sensing, Vol. 10, No. 6, 846, 29.05.2018.

Research output: Research - peer-reviewArticle

Harvard

Gao, F, Yang, Y, Wang, J, Sun, J, Yang, E & Zhou, H 2018, 'A deep convolutional generative adversarial networks (DCGANs)-based semi-supervised method for object recognition in synthetic aperture radar (SAR) images' Remote Sensing, vol 10, no. 6, 846. DOI: 10.3390/rs10060846

APA

Gao, F., Yang, Y., Wang, J., Sun, J., Yang, E., & Zhou, H. (2018). A deep convolutional generative adversarial networks (DCGANs)-based semi-supervised method for object recognition in synthetic aperture radar (SAR) images. Remote Sensing, 10(6), [846]. DOI: 10.3390/rs10060846

Vancouver

Gao F, Yang Y, Wang J, Sun J, Yang E, Zhou H. A deep convolutional generative adversarial networks (DCGANs)-based semi-supervised method for object recognition in synthetic aperture radar (SAR) images. Remote Sensing. 2018 May 29;10(6). 846. Available from, DOI: 10.3390/rs10060846

Author

Gao, Fei ; Yang, Yue ; Wang, Jun ; Sun, Jinping ; Yang, Erfu ; Zhou, Huiyu. / A deep convolutional generative adversarial networks (DCGANs)-based semi-supervised method for object recognition in synthetic aperture radar (SAR) images. In: Remote Sensing. 2018 ; Vol. 10, No. 6.

BibTeX - Download

@article{b1143e76e76f44a4aedfcad1bfa2af23,
title = "A deep convolutional generative adversarial networks (DCGANs)-based semi-supervised method for object recognition in synthetic aperture radar (SAR) images",
abstract = "Synthetic aperture radar automatic target recognition (SAR-ATR) has made great progress in recent years. Most of the established recognition methods are supervised, which have strong dependence on image labels. However, obtaining the labels of radar images is expensive and time-consuming. In this paper, we present a semi-supervised learning method that is based on the standard deep convolutional generative adversarial networks (DCGANs). We double the discriminator that is used in DCGANs and utilize the two discriminators for joint training. In this process, we introduce a noisy data learning theory to reduce the negative impact of the incorrectly labeled samples on the performance of the networks. We replace the last layer of the classic discriminators with the standard softmax function to output a vector of class probabilities so that we can recognize multiple objects. We subsequently modify the loss function in order to adapt to the revised network structure. In our model, the two discriminators share the same generator, and we take the average value of them when computing the loss function of the generator, which can improve the training stability of DCGANs to some extent. We also utilize images of higher quality from the generated images for training in order to improve the performance of the networks. Our method has achieved state-of-the-art results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, and we have proved that using the generated images to train the networks can improve the recognition accuracy with a small number of labeled samples.",
keywords = "SAR target recognition, semi-supervised, DCGANs, joint training",
author = "Fei Gao and Yue Yang and Jun Wang and Jinping Sun and Erfu Yang and Huiyu Zhou",
year = "2018",
month = "5",
doi = "10.3390/rs10060846",
volume = "10",
journal = "Remote Sensing",
issn = "2072-4292",
number = "6",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - A deep convolutional generative adversarial networks (DCGANs)-based semi-supervised method for object recognition in synthetic aperture radar (SAR) images

AU - Gao,Fei

AU - Yang,Yue

AU - Wang,Jun

AU - Sun,Jinping

AU - Yang,Erfu

AU - Zhou,Huiyu

PY - 2018/5/29

Y1 - 2018/5/29

N2 - Synthetic aperture radar automatic target recognition (SAR-ATR) has made great progress in recent years. Most of the established recognition methods are supervised, which have strong dependence on image labels. However, obtaining the labels of radar images is expensive and time-consuming. In this paper, we present a semi-supervised learning method that is based on the standard deep convolutional generative adversarial networks (DCGANs). We double the discriminator that is used in DCGANs and utilize the two discriminators for joint training. In this process, we introduce a noisy data learning theory to reduce the negative impact of the incorrectly labeled samples on the performance of the networks. We replace the last layer of the classic discriminators with the standard softmax function to output a vector of class probabilities so that we can recognize multiple objects. We subsequently modify the loss function in order to adapt to the revised network structure. In our model, the two discriminators share the same generator, and we take the average value of them when computing the loss function of the generator, which can improve the training stability of DCGANs to some extent. We also utilize images of higher quality from the generated images for training in order to improve the performance of the networks. Our method has achieved state-of-the-art results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, and we have proved that using the generated images to train the networks can improve the recognition accuracy with a small number of labeled samples.

AB - Synthetic aperture radar automatic target recognition (SAR-ATR) has made great progress in recent years. Most of the established recognition methods are supervised, which have strong dependence on image labels. However, obtaining the labels of radar images is expensive and time-consuming. In this paper, we present a semi-supervised learning method that is based on the standard deep convolutional generative adversarial networks (DCGANs). We double the discriminator that is used in DCGANs and utilize the two discriminators for joint training. In this process, we introduce a noisy data learning theory to reduce the negative impact of the incorrectly labeled samples on the performance of the networks. We replace the last layer of the classic discriminators with the standard softmax function to output a vector of class probabilities so that we can recognize multiple objects. We subsequently modify the loss function in order to adapt to the revised network structure. In our model, the two discriminators share the same generator, and we take the average value of them when computing the loss function of the generator, which can improve the training stability of DCGANs to some extent. We also utilize images of higher quality from the generated images for training in order to improve the performance of the networks. Our method has achieved state-of-the-art results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, and we have proved that using the generated images to train the networks can improve the recognition accuracy with a small number of labeled samples.

KW - SAR target recognition

KW - semi-supervised

KW - DCGANs

KW - joint training

UR - http://www.mdpi.com/journal/remotesensing

U2 - 10.3390/rs10060846

DO - 10.3390/rs10060846

M3 - Article

VL - 10

JO - Remote Sensing

T2 - Remote Sensing

JF - Remote Sensing

SN - 2072-4292

IS - 6

M1 - 846

ER -

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