A New Algorithm for SAR Image Target Recognition Based on an Improved Deep Convolutional Neural Network

In an attempt to exploit the automatic feature extraction ability of biologically-inspired deep learning models, and enhance the learning of target features, we propose a novel deep learning algorithm. This is based on a deep convolutional neural network (DCNN) trained with an improved cost function, and combined with a support vector machine (SVM). Specifically, class separation information, which explicitly facilitates intra-class compactness and inter-class separability in the process of learning features, is added to an improved cost function as a regularization term, to enhance the DCNN’s feature extraction ability. The enhanced DCNN is applied to learn the features of Synthetic Aperture Radar (SAR) images, and the SVM is utilized to map features into output labels. Simulation experiments are performed using benchmark SAR image data from the Moving and Stationary Target Acquisition and Recognition (MSTAR) database. Comparative results demonstrate the effectiveness of our proposed method, with an average accuracy of 99% on ten types of targets, including variants and articulated targets. We conclude that our proposed DCNN method has significant potential to be exploited for SAR image target recognition, and can serve as a new benchmark for the research community.

[1]  Peijun Du,et al.  Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging , 2016, Neurocomputing.

[2]  Bir Bhanu,et al.  Recognizing articulated objects in SAR images , 2001, Pattern Recognit..

[3]  Simon Wagner,et al.  Morphological Component Analysis in SAR images to improve the generalization of ATR systems , 2015, 2015 3rd International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa).

[4]  Bhim Singh,et al.  Back-Propagation Algorithm-Based Controller for Autonomous Wind–DG Microgrid , 2016, IEEE Transactions on Industry Applications.

[5]  Kaizhu Huang,et al.  Reducing and Stretching Deep Convolutional Activation Features for Accurate Image Classification , 2018, Cognitive Computation.

[6]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[7]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[8]  Hongwei Liu,et al.  Convolutional Neural Network With Data Augmentation for SAR Target Recognition , 2016, IEEE Geoscience and Remote Sensing Letters.

[9]  Erfu Yang,et al.  Visual Attention Model Based Vehicle Target Detection in Synthetic Aperture Radar Images: A Novel Approach , 2015, Cognitive Computation.

[10]  Yann LeCun,et al.  Large-scale Learning with SVM and Convolutional for Generic Object Categorization , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[11]  Haipeng Wang,et al.  SAR target recognition based on deep learning , 2014, 2014 International Conference on Data Science and Advanced Analytics (DSAA).

[12]  Raghu G. Raj,et al.  SAR Automatic Target Recognition Using Discriminative Graphical Models , 2014, IEEE Transactions on Aerospace and Electronic Systems.

[13]  Gangyao Kuang,et al.  Classification on the Monogenic Scale Space: Application to Target Recognition in SAR Image , 2015, IEEE Transactions on Image Processing.

[14]  Timothy D. Ross,et al.  Evaluation of SAR ATR algorithm performance sensitivity to MSTAR extended operating conditions , 1998, Defense, Security, and Sensing.

[15]  Shutao Li,et al.  Decision fusion of sparse representation and support vector machine for SAR image target recognition , 2013, Neurocomputing.

[16]  S. Susan Young,et al.  Assessing the accuracy of image tracking algorithms on visible and thermal imagery using a deep restricted Boltzmann machine , 2012, Defense + Commercial Sensing.

[17]  Erfu Yang,et al.  Biologically Inspired Progressive Enhancement Target Detection from Heavy Cluttered SAR Images , 2016, Cognitive Computation.

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

[19]  Kilian Q. Weinberger,et al.  On Calibration of Modern Neural Networks , 2017, ICML.

[20]  Lei Deng,et al.  Temperature based Restricted Boltzmann Machines , 2016, Scientific Reports.

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

[22]  Jinchang Ren,et al.  High-Accuracy Sub-Pixel Motion Estimation From Noisy Images in Fourier Domain , 2010, IEEE Transactions on Image Processing.

[23]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[24]  Nihat Ay,et al.  Refinements of Universal Approximation Results for Deep Belief Networks and Restricted Boltzmann Machines , 2010, Neural Computation.

[25]  Zhe Liu,et al.  Structured covariance principal component analysis for real-time onsite feature extraction and dimensionality reduction in hyperspectral imaging. , 2014, Applied optics.

[26]  Simon Wagner.  Combination of convolutional feature extraction and support vector machines for radar ATR , 2014, 17th International Conference on Information Fusion (FUSION).

[27]  Qun Zhao,et al.  Support vector machines for SAR automatic target recognition , 2001 .

[28]  Danyang Li,et al.  Ensemble of Deep Neural Networks with Probability-Based Fusion for Facial Expression Recognition , 2017, Cognitive Computation.

[29]  Junwei Han,et al.  Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing , 2014 .

[30]  Jong-Il Park,et al.  Modified polar mapping classifier for SAR automatic target recognition , 2014, IEEE Transactions on Aerospace and Electronic Systems.

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

[32]  Ning Li,et al.  SAR ATR based on displacement- and rotation-insensitive CNN , 2016 .

[33]  Jinchang Ren,et al.  Brushstroke based sparse hybrid convolutional neural networks for author classification of Chinese ink-wash paintings , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[34]  Michael Lee Bryant,et al.  Standard SAR ATR evaluation experiments using the MSTAR public release data set , 1998, Defense, Security, and Sensing.

[35]  Peijun Du,et al.  Corrigendum to 'Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging' Neurocomputing 185 (2016) 1-10 , 2016 .

[36]  G. J. Owirka,et al.  Template-based SAR ATR performance using different image enhancement techniques , 1999, Defense, Security, and Sensing.

[37]  Lei Guo,et al.  Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[38]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[39]  Xuan Li,et al.  SAR ATR based on dividing CNN into CAE and SNN , 2015, 2015 IEEE 5th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR).

[40]  Raghu G. Raj,et al.  Automatic target recognition using discriminative graphical models , 2011, 2011 18th IEEE International Conference on Image Processing.

[41]  Bhagavatula Vijaya Kumar,et al.  Performance of the extended maximum average correlation height (EMACH) filter and the polynomial distance classifier correlation filter (PDCCF) for multiclass SAR detection and classification , 2002, SPIE Defense + Commercial Sensing.

[42]  Na Wang,et al.  Sparse Representation of Monogenic Signal: With Application to Target Recognition in SAR Images , 2014, IEEE Signal Processing Letters.

[43]  Michael F. Lynch,et al.  Emotion: A Unified Mechanistic Interpretation from a Cognitive Architecture , 2015, Cognitive Computation.

[44]  Charu C. Aggarwal,et al.  Neural Networks and Deep Learning , 2018, Springer International Publishing.

[45]  Chuang Lin,et al.  Optimizing Kernel PCA Using Sparse Representation-Based Classifier for MSTAR SAR Image Target Recognition , 2013 .