Interference Mitigation for Synthetic Aperture Radar Based on Deep Residual Network

Radio Frequency Interference (RFI) is a key issue for Synthetic Aperture Radar (SAR) because it can seriously degrade the imaging quality, leading to the misinterpretation of the target scattering characteristics and hindering the subsequent image analysis. To address this issue, we present a narrow-band interference (NBI) and wide-band interference (WBI) mitigation algorithm based on deep residual network (ResNet). First, the short-time Fourier transform (STFT) is used to characterize the interference-corrupted echo in the time–frequency domain. Then, the interference detection model is built by a classical deep convolutional neural network (DCNN) framework to identify whether there is an interference component in the echo. 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 simulated and measured SAR data with strip mode and terrain observation by progressive scans (TOPS) mode. Moreover, in comparison with the notch filtering and the eigensubspace filtering, the proposed interference mitigation algorithm can improve the interference mitigation performance, while reducing the computation complexity.

[1]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Jie Li,et al.  Narrowband RFI Suppression for SAR System via Efficient Parameter-Free Decomposition Algorithm , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[3]  John W. Fisher,et al.  Attributing scatterer anisotropy for model-based ATR , 2000, SPIE Defense + Commercial Sensing.

[4]  Huchuan Lu,et al.  Multi-Focus Image Fusion With a Natural Enhancement via a Joint Multi-Level Deeply Supervised Convolutional Neural Network , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[5]  Bernd Freisleben,et al.  Fast Cloud Segmentation Using Convolutional Neural Networks , 2018, Remote. Sens..

[6]  Zheng-Hua Tan,et al.  Conditional Generative Adversarial Networks for Speech Enhancement and Noise-Robust Speaker Verification , 2017, INTERSPEECH.

[7]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[8]  Irena Hajnsek,et al.  Very-High-Resolution Airborne Synthetic Aperture Radar Imaging: Signal Processing and Applications , 2013, Proceedings of the IEEE.

[9]  Xianrong Wan,et al.  Computationally Efficient RF Interference Suppression Method With Closed-Form Maximum Likelihood Estimator for HF Surface Wave Over-The-Horizon Radars , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Vishal M. Patel,et al.  Generative adversarial network-based restoration of speckled SAR images , 2017, 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

[11]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Qi Li,et al.  Detail preserved fusion of visible and infrared images using regional saliency extraction and multi-scale image decomposition , 2015 .

[13]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[14]  Francesco De Zan,et al.  TOPSAR: Terrain Observation by Progressive Scans , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Laurent Ferro-Famil,et al.  Interference suppression in synthesized SAR images , 2005, IEEE Geoscience and Remote Sensing Letters.

[16]  Yoshua Bengio,et al.  Generative Adversarial Networks , 2014, ArXiv.

[17]  Jian Xie,et al.  Time-Varying SAR Interference Suppression Based on Delay-Doppler Iterative Decomposition Algorithm , 2018, Remote. Sens..

[18]  Zuoyong Li,et al.  Gray level difference-based transition region extraction and thresholding , 2009, Comput. Electr. Eng..

[19]  Feng Zhou,et al.  Research on Methods for Narrow-Band Interference Suppression in Synthetic Aperture Radar Data , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[20]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Jie Li,et al.  Narrowband RFI Suppression for SAR System via Fast Implementation of Joint Sparsity and Low-Rank Property , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Jian Xie,et al.  Narrow-Band Interference Suppression via RPCA-Based Signal Separation in Time–Frequency Domain , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[23]  Franz J. Meyer,et al.  Correction and Characterization of Radio Frequency Interference Signatures in L-Band Synthetic Aperture Radar Data , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[24]  J. Dudczyk,et al.  Optimizing the minimum cost flow algorithm for the phase unwrapping process in SAR radar , 2014 .

[25]  Y. Liu,et al.  RFI suppression in SAR based on approximated spectral decomposition algorithm , 2012 .

[26]  J. Dudczyk,et al.  Applying the distance and similarity functions to radar signals identification , 2008, 2008 International Radar Symposium.

[27]  Léon Bottou,et al.  Wasserstein Generative Adversarial Networks , 2017, ICML.

[28]  Mengdao Xing,et al.  Eigensubspace-Based Filtering With Application in Narrow-Band Interference Suppression for SAR , 2007, IEEE Geoscience and Remote Sensing Letters.

[29]  Lei Yang,et al.  A Robust Motion Compensation Approach for UAV SAR Imagery , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[30]  Jian Li,et al.  RFI Suppression for Synchronous Impulse Reconstruction UWB Radar Using RELAX , 2013 .

[31]  Feng Zhou,et al.  A new method for SAR radio frequency interference mitigation based on maximum a posterior estimation , 2017, 2017 XXXIInd General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS).

[32]  Feng Zhou,et al.  Wideband Interference Mitigation in High-Resolution Airborne Synthetic Aperture Radar Data , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[33]  Lam H. Nguyen,et al.  Suppression of radio frequency inteference (RFI) for synchronous impulse reconstruction ultra-wideband radar , 2005, SPIE Defense + Commercial Sensing.

[34]  Jan Matuszewski,et al.  Radar signal identification using a neural network and pattern recognition methods , 2018, 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET).

[35]  Yunkai Deng,et al.  Application of Subband Spectral Cancellation for SAR Narrow-Band Interference Suppression , 2012, IEEE Geoscience and Remote Sensing Letters.

[36]  Firooz A. Sadjadi.  New experiments in inverse synthetic aperture radar image exploitation for maritime surveillance , 2014, Defense + Security Symposium.

[37]  한보형,et al.  Learning Deconvolution Network for Semantic Segmentation , 2015 .

[38]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[40]  I. Hajnsek,et al.  A tutorial on synthetic aperture radar , 2013, IEEE Geoscience and Remote Sensing Magazine.

[41]  Michael W. Spencer,et al.  RFI Characterization and Mitigation for the SMAP Radar , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[42]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[43]  Bing Sun,et al.  Multiclass Radio Frequency Interference Detection and Suppression for SAR Based on the Single Shot MultiBox Detector , 2018, Sensors.

[44]  Dacheng Tao,et al.  Perceptual Adversarial Networks for Image-to-Image Transformation , 2017, IEEE Transactions on Image Processing.

[45]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[46]  T. Lamont-Smith,et al.  Filtering approaches for interference suppression in low-frequency SAR , 2006 .

[47]  Mengdao Xing,et al.  Narrow-Band Interference Suppression for SAR Based on Complex Empirical Mode Decomposition , 2009, IEEE Geoscience and Remote Sensing Letters.