Proceedings ArticleDOI
Interference Mitigation for Synthetic Aperture Radar Using Deep Learning
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TLDR
An interference mitigation algorithm based on the deep residual network (ResNet) and the classical convolutional neural network framework to identify whether the echoes exist interference signal component and transform the time-frequency spectrum of the recovered signal into the time domain.Abstract:
In this paper, we present a narrow-band interference (NBI) and wide-band interference (WBI) mitigation algorithm based on the deep residual network (ResNet). First, the short-time Fourier transform (STFT) is utilized to characterize the interference-corrupted echo in the time-frequency domain. Then, the interference detection model is built by the classical convolutional neural network (CNN) framework to identify whether the echoes exist interference signal component. Furthermore, the time-frequency feature of the target signal is extracted and reconstructed by utilizing the ResNet. Finally, the inverse time-frequency Fourier transform (ISTFT) is utilized to transform the time-frequency spectrum of the recovered signal into the time domain. The effectiveness of the interference mitigation algorithm is verified on the simulation and measured SAR data of the terrain observation by progressive scans (TOPS) mode. Moreover, the performance comparison with the notch filtering and eigensubspace filtering demonstrates the superiority of the proposed interference mitigation algorithm.read more
Citations
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Journal ArticleDOI
A Modified 2-D Notch Filter Based on Image Segmentation for RFI Mitigation in Synthetic Aperture Radar
TL;DR: In this paper , the adaptive gamma correction (AGC) approach was utilized to enhance the SAR image with RFI in the range-frequency and azimuth-time domain, and the modified selective binary and Gaussian filtering regularized level set (SBGFRLS) model was used to further process the image after AGC to accurately extract the contour of the useful signals with interference.
Journal ArticleDOI
Learning Time–Frequency Information With Prior for SAR Radio Frequency Interference Suppression
TL;DR: In this paper , a prior-induced interference suppression network (PISNet) is proposed to achieve RFI suppression and useful signal recovery in the time-frequency domain (TFD), where both narrowband and wideband interferences are uniformly modeled as a sparse distribution in the TFD, and the stationarity of SAR echoes determines its lowrank characteristic.
Proceedings ArticleDOI
An Interference Suppression Method For Spaceborne Sar Image Via Space-Channel Attention Network
TL;DR: In this article , the authors proposed a Space-Channel Attention Network (SCANet) to preserve the texture features of the original images while eliminating interference in space-borne SAR images.
Journal ArticleDOI
Learning Time–Frequency Information With Prior for SAR Radio Frequency Interference Suppression
TL;DR: In this paper , a prior-induced learning framework (PISNet) is proposed to achieve RFI suppression and useful signal recovery in time-frequency domain, where both narrowband and wideband interference are uniformly modeled as a sparse distribution in timefrequency domain and the stationarity of SAR echoes determines its low-rank characteristic.
An Interference Suppression Method For Spaceborne Sar Image Via Space-Channel Attention Network
TL;DR: In this article , the authors proposed a Space-Channel Attention Network (SCANet) to preserve the texture features of the original images while eliminating interference in space-borne synthetic aperture radar images.
References
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TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings ArticleDOI
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Christian Ledig,Lucas Theis,Ferenc Huszar,Jose Caballero,Andrew Cunningham,Alejandro Acosta,Andrew Peter Aitken,Alykhan Tejani,Johannes Totz,Zehan Wang,Wenzhe Shi +10 more
TL;DR: SRGAN as mentioned in this paper proposes a perceptual loss function which consists of an adversarial loss and a content loss, which pushes the solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images.
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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Christian Ledig,Lucas Theis,Ferenc Huszar,Jose Caballero,Andrew Cunningham,Alejandro Acosta,Andrew Peter Aitken,Alykhan Tejani,Johannes Totz,Zehan Wang,Wenzhe Shi +10 more
TL;DR: SRGAN, a generative adversarial network (GAN) for image super-resolution (SR), is presented, to its knowledge, the first framework capable of inferring photo-realistic natural images for 4x upscaling factors and a perceptual loss function which consists of an adversarial loss and a content loss.
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Generative Adversarial Networks
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: In this article, a generative adversarial network (GAN) is proposed to estimate generative models via an adversarial process, in which two models are simultaneously trained: a generator G and a discriminator D that estimates the probability that a sample came from the training data rather than G.
Journal ArticleDOI
A tutorial on synthetic aperture radar
Alberto Moreira,Pau Prats-Iraola,Marwan Younis,Gerhard Krieger,Irena Hajnsek,Konstantinos Papathanassiou +5 more
TL;DR: This paper provides first a tutorial about the SAR principles and theory, followed by an overview of established techniques like polarimetry, interferometry and differential interferometric as well as of emerging techniques (e.g., polarimetric SARinterferometry, tomography and holographic tomography).