Synthetic aperture radar ship discrimination, generation and latent variable extraction using information maximizing generative adversarial networks

Schwegmann, C. P.; Kleynhans, W.; Salmon, B.P.; Mdakane, L.W.; Meyer, R. G. V.

doi:10.1109/igarss.2017.8127440

Cited by 15 publications

(6 citation statements)

References 4 publications

(10 reference statements)

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“…Then we use one of measures ( 13), (15), or (17) to calculate the measure of properties for the each synthesized SAR image from the set. The relative measure of the rotation with respect to the reference image I 0 is calculated using…”

Section: B Relation Of the Properties And Latent Codesmentioning

confidence: 99%

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Analytical Interpretation of Latent Codes in InfoGAN with SAR Images

Feng¹,

Daković²,

Ji³

et al. 2022

Preprint

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Generative Adversarial Networks (GANs) can synthesize abundant photo-realistic synthetic aperture radar (SAR) images. Some recent GANs (e.g., InfoGAN), are even able to edit specific properties of the synthesized images by introducing latent codes. It is crucial for SAR image synthesis since the targets in real SAR images are with different properties due to the imaging mechanism. Despite the success of InfoGAN in manipulating properties, there still lacks a clear explanation of how these latent codes affect synthesized properties, thus editing specific properties usually relies on empirical trials, unreliable and time-consuming. In this paper, we show that latent codes are disentangled to affect the properties of SAR images in a non-linear manner. By introducing some property estimators for latent codes, we are able to provide a completely analytical nonlinear model to decompose the entangled causality between latent codes and different properties. The qualitative and quantitative experimental results further reveal that the properties can be calculated by latent codes, inversely, the satisfying latent codes can be estimated given desired properties. In this case, properties can be manipulated by latent codes as we expect.

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Section: B Relation Of the Properties And Latent Codesmentioning

confidence: 99%

“…Although InfoGAN can generate SAR images with semantically meaningful properties by latent codes, the relation between properties and latent codes still lacks clear analytical interpretation [15], [17]. It brings in two problems: (1) How to obtain the property value from latent codes?…”

Section: Introductionmentioning

confidence: 99%

Analytical Interpretation of Latent Codes in InfoGAN with SAR Images

Feng¹,

Daković²,

Ji³

et al. 2022

Preprint

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“…Most applications of GANs and neural networks using radar data focus on images generated from radar signals using synthetic aperture radar (SAR) [16]- [18] and time-of-flight algorithms [19]. At the time of this paper, to the best of our knowledge, the application of GANs for one-dimensional radar signal data has not been published.…”

Section: Previous Work: Generative Adversarial Network For Radarmentioning

confidence: 99%

Generative Adversarial Network for Radar Signal Synthesis

Truong

Yanushkevich

2019

2019 International Joint Conference on Neural Networks (IJCNN)

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A major obstacle in radar based methods for concealed object detection on humans and seamless integration into security and access control system is the difficulty in collecting high quality radar signal data. Generative adversarial networks (GAN) have shown promise in data generation application in the fields of image and audio processing. As such, this paper proposes the design of a GAN for application in radar signal generation. Data collected using the Finite-Difference Time-Domain (FDTD) method on three concealed object classes (no object, large object, and small object) were used as training data to train a GAN to generate radar signal samples for each class. The proposed GAN generated radar signal data which was indistinguishable from the training data by qualitative human observers.

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“…Gao et al used the discriminator of deep convolutional generative adversarial networks (DCGAN) to implement the ATR task [26]. Schwegmann et al proposed information maximizing generative adversarial networks (InfoGAN) to perform SAR ship recognition [27]. Although previous studies on GANs have addressed the performance of semi-supervised learning in the recognition task, research has yet to explore whether the generated SAR target samples can be moved from the generation architecture and used in other available ATR frameworks [28].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Counterfactual Sample Generation and Filtering Approach for SAR Automatic Target Recognition with a Small Sample Set

et al. 2021

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Although automatic target recognition (ATR) models based on data-driven algorithms have achieved excellent performance in recent years, the synthetic aperture radar (SAR) ATR model often suffered from performance degradation when it encountered a small sample set. In this paper, an integrated counterfactual sample generation and filtering approach is proposed to alleviate the negative influence of a small sample set. The proposed method consists of a generation component and a filtering component. First, the proposed generation component utilizes the overfitting characteristics of generative adversarial networks (GANs), which ensures the generation of counterfactual target samples. Second, the proposed filtering component is built by learning different recognition functions. In the proposed filtering component, multiple SVMs trained by different SAR target sample sets provide pseudo-labels to the other SVMs to improve the recognition rate. Then, the proposed approach improves the performance of the recognition model dynamically while it continuously generates counterfactual target samples. At the same time, counterfactual target samples that are beneficial to the ATR model are also filtered. Moreover, ablation experiments demonstrate the effectiveness of the various components of the proposed method. Experimental results based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) and OpenSARship dataset also show the advantages of the proposed approach. Even though the size of the constructed training set was 14.5% of the original training set, the recognition performance of the ATR model reached 91.27% with the proposed approach.

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Synthetic aperture radar ship discrimination, generation and latent variable extraction using information maximizing generative adversarial networks

Cited by 15 publications

References 4 publications

Analytical Interpretation of Latent Codes in InfoGAN with SAR Images

Analytical Interpretation of Latent Codes in InfoGAN with SAR Images

Generative Adversarial Network for Radar Signal Synthesis

An Integrated Counterfactual Sample Generation and Filtering Approach for SAR Automatic Target Recognition with a Small Sample Set

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