Radar Remote Sensing Data Augmentation Method Based on Generative Adversarial Network

doi:10.16182/j.issn1004731x.joss.20-0953

Abstract

Abstract:

In the research field of radar remote sensing, both the completeness and diversity of radar data samples cannot meet the requirement of effective training of deep learning models, and the models are prone to over-fitting, which significantly limits the wide application of deep learning techniques in this field. Targeting on the needs of intelligent application in radar remote sensing, a microwave imaging radar suited data augmentation method is proposed to solve the issue of insufficient radar data samples by leveraging the general framework of generative adversarial network. Aiming at the features of radar samples being not obvious, the label smoothing regularization technique is utilized to automatically classify the augmentated radar samples. The augmentated samples together with the real samples are collaboratively used to implement the robust training of deep learning models. The proposed method is verified by the experiments based on the extensive open-sourse radar remote sensing data.

Key words: deep learning, radar remote sensing, generative adversarial network, data augmentation

CLC Number:

TP391.9

Xu Kang, Xiaofeng Zhang. Radar Remote Sensing Data Augmentation Method Based on Generative Adversarial Network[J]. Journal of System Simulation, 2022, 34(4): 920-927.

Figures/Tables 9

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目标类别	俯仰角15°	俯仰角17°
合计	2 426	2 747
BMP2	196	233
BTR70	196	233
T72	196	232
BRT60	195	256
2S1	274	299
BRDM2	274	298
D7	274	299
T62	273	299
ZIL131	274	299
ZSU23/4	274	299

数据集	Boost	SVM	FCN	LeNet	DCNN
17#	92.8	93.1	92.4	95.5	96.1
17#Ex	93.3	95.2	96.2	96.7	97.2

数据集	Boost	SVM	FCN	LeNet	DCNN
17#	92.8	93.1	92.4	95.5	96.1
17#Ex0.5	94.5	94.3	93.2	95.8	96.5
17#Ex1.0	93.3	95.2	96.2	96.7	97.2
17#Ex1.5	92.7	94.6	95.2	96.1	97.3
17#Ex2.0	93.1	95.0	94.9	95.6	95.9
17#Ex2.5	92.2	93.1	93.5	94.6	94.3

模型	Boost	SVM	FCN	LeNet	DCNN
wGAN-cp	89.5	88.7	89.3	91.1	91.1
wGAN-gp	91.2	90.5	90.2	94.8	95.2
LSGAN	92.9	93.3	94.7	95.4	95.6
Radar-GAN	93.3	95.2	96.2	96.7	97.2

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