Sparsity-motivated automatic target recognition.

We present an automatic target recognition algorithm using the recently developed theory of sparse representations and compressive sensing. We show how sparsity can be helpful for efficient utilization of data for target recognition. We verify the efficacy of the proposed algorithm in terms of the recognition rate and confusion matrices on the well known Comanche (Boeing-Sikorsky, USA) forward-looking IR data set consisting of ten different military targets at different orientations.

[1]  Guillermo Sapiro,et al.  Sparse representations for image classification: learning discriminative and reconstructive non-parametric dictionaries , 2008 .

[2]  Babak Hassibi,et al.  On the Reconstruction of Block-Sparse Signals With an Optimal Number of Measurements , 2008, IEEE Transactions on Signal Processing.

[3]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[4]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[5]  Rama Chellappa,et al.  Sparsity inspired automatic target recognition , 2010, Defense + Commercial Sensing.

[6]  Michael P. Friedlander,et al.  Probing the Pareto Frontier for Basis Pursuit Solutions , 2008, SIAM J. Sci. Comput..

[7]  Pierre Vandergheynst,et al.  Compressed Sensing and Redundant Dictionaries , 2007, IEEE Transactions on Information Theory.

[8]  Yaakov Tsaig,et al.  Extensions of compressed sensing , 2006, Signal Process..

[9]  Michael Elad,et al.  On the stability of the basis pursuit in the presence of noise , 2006, Signal Process..

[10]  M. Yuan,et al.  Model selection and estimation in regression with grouped variables , 2006 .

[11]  J. Tropp,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, Commun. ACM.

[12]  Firooz A Sadjadi,et al.  Infrared target detection with probability density functions of wavelet transform subbands. , 2004, Applied optics.

[13]  Jonathan Phillips,et al.  Matching pursuit filters applied to face identification , 1994, Optics & Photonics.

[14]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[15]  M. de Groot,et al.  Benthic habitat mapping with autonomous underwater vehicles , 2008, OCEANS 2008.

[16]  D.M. Kocak,et al.  Underwater imaging and optics: Recent advances , 2008, OCEANS 2008.

[17]  Lawrence Carin,et al.  Infrared-Image Classification Using Hidden Markov Trees , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Richard G. Baraniuk,et al.  Compressive Sensing , 2008, Computer Vision, A Reference Guide.

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

[20]  Deanna Needell,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.

[21]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[22]  Xiaoming Huo,et al.  Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.

[23]  Nasser M. Nasrabadi,et al.  An Application of Wavelet-Based Vector Quantization in Target Recognition , 1997, Int. J. Artif. Intell. Tools.

[24]  Rama Chellappa,et al.  Experimental Evaluation of FLIR ATR Approaches - A Comparative Study , 2001, Comput. Vis. Image Underst..

[25]  Guillermo Sapiro,et al.  Dictionary learning and sparse coding for unsupervised clustering , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[26]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[27]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[28]  P. Bühlmann,et al.  The group lasso for logistic regression , 2008 .

[29]  Yonina C. Eldar,et al.  Block-Sparse Signals: Uncertainty Relations and Efficient Recovery , 2009, IEEE Transactions on Signal Processing.

[30]  Nasser M. Nasrabadi,et al.  Automatic Target Recognition Using a Modular Neural Network. , 1998 .

[31]  Yonina C. Eldar,et al.  Robust Recovery of Signals From a Structured Union of Subspaces , 2008, IEEE Transactions on Information Theory.

[32]  Emmanuel J. Candès,et al.  Signal recovery from random projections , 2005, IS&T/SPIE Electronic Imaging.