Deep Learning for Direct Automatic Target Recognition from SAR Data

Automatic target recognition for synthetic aperture radar involves three processing steps: imaging, identifying regions of interests and processing of the identified regions for target classification. Motivated by the successes of data driven approach in various fields, we establish a framework for synthetic aperture radar target recognition that does not require image formation. We present a deep neural network architecture that classifies targets directly from the slow-time and fast-time sampled received signal. Classification decisions are made based on how closely optimized sets of vectors corresponding to each class represent a new sample, and these vectors from all classes under consideration collectively forms a dictionary.

[1]  Thomas S. Huang,et al.  Deep Networks for Image Super-Resolution with Sparse Prior , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[2]  Raghu G. Raj,et al.  Robust Sonar ATR Through Bayesian Pose-Corrected Sparse Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Bariscan Yonel,et al.  Deep Learning for Passive Synthetic Aperture Radar , 2017, IEEE Journal of Selected Topics in Signal Processing.

[4]  Chris Kreucher,et al.  Modern approaches in deep learning for SAR ATR , 2016, SPIE Defense + Security.

[5]  Vishal Monga,et al.  Histopathological Image Classification Using Discriminative Feature-Oriented Dictionary Learning , 2015, IEEE Transactions on Medical Imaging.

[6]  Haipeng Wang,et al.  Application of deep-learning algorithms to MSTAR data , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[7]  Birsen Yazici,et al.  Deep learning for waveform estimation and imaging in passive radar , 2018, IET Radar, Sonar & Navigation.

[8]  Mujdat Cetin,et al.  SAR ATR in the phase history domain using deep convolutional neural networks , 2018, Remote Sensing.

[9]  Birsen Yazici,et al.  Moving Target Artifacts in Bistatic Synthetic Aperture Radar Images , 2015, IEEE Transactions on Computational Imaging.

[10]  Haipeng Wang,et al.  Target Classification Using the Deep Convolutional Networks for SAR Images , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Yoshua Bengio,et al.  Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach , 2011, ICML.

[12]  Vishal Monga,et al.  Fast Low-Rank Shared Dictionary Learning for Image Classification , 2016, IEEE Transactions on Image Processing.

[13]  Birsen Yazici,et al.  Passive Synthetic Aperture Hitchhiker Imaging of Ground Moving Targets—Part 1: Image Formation and Velocity Estimation , 2014, IEEE Transactions on Image Processing.

[14]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  G. S. Sammelmann.  High-frequency images of proud and buried 3D-targets , 2003, Oceans 2003. Celebrating the Past ... Teaming Toward the Future (IEEE Cat. No.03CH37492).

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

[17]  Birsen Yazici,et al.  Ground Moving Target Imaging Using Ultranarrowband Continuous Wave Synthetic Aperture Radar , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Naveen Kumar,et al.  An Overview of Automatic Target Recognition Systems for Underwater Mine Classification , 2016 .

[19]  Hidetoshi Furukawa,et al.  Deep Learning for End-to-End Automatic Target Recognition from Synthetic Aperture Radar Imagery , 2018, ArXiv.

[20]  Simon Wagner,et al.  A deep learning SAR ATR system using regularization and prioritized classes , 2017, 2017 IEEE Radar Conference (RadarConf).

[21]  D. Donoho.  For most large underdetermined systems of equations, the minimal 𝓁1‐norm near‐solution approximates the sparsest near‐solution , 2006 .

[22]  Zongxu Pan,et al.  Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data , 2017, Remote. Sens..

[23]  David Morgan,et al.  Deep convolutional neural networks for ATR from SAR imagery , 2015, Defense + Security Symposium.

[24]  Lei Xue,et al.  RECOGNITION OF SAR TARGET BASED ON MULTILAYER AUTO-ENCODER AND SNN , 2013 .

[25]  Raghu G. Raj,et al.  What's mine is yours: Pretrained CNNs for limited training sonar ATR , 2017, OCEANS 2017 – Anchorage.

[26]  Jason T. Isaacs,et al.  Discriminative sparsity for Sonar ATR , 2015, OCEANS 2015 - MTS/IEEE Washington.

[27]  Jason Weston,et al.  A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.

[28]  Patrick L. Combettes,et al.  Signal Recovery by Proximal Forward-Backward Splitting , 2005, Multiscale Model. Simul..

[29]  Armin Walter Doerry.  Basics of Polar-Format algorithm for processing Synthetic Aperture Radar images. , 2012 .

[30]  Birsen Yazici,et al.  Deep learning for SAR image formation , 2017, Defense + Security.

[31]  Birsen Yazici,et al.  Deep learning for radar , 2017, 2017 IEEE Radar Conference (RadarConf).