Experimental performance analysis of clutter removal techniques in IR images

This work deals with the problem of background removal in infra-red (IR) image sequences. Background removal is a basic step to detect small targets in surveillance systems based on IR images. The paper refers to a general background removal procedure that consists in estimating and subtracting the background in each frame of the IR sequence. The estimation step is accomplished by means of linear and non linear filters. The focus of this work is on techniques adopting four different filters: 1) the 2D window average filter; 2) the 2D MEDIAN filter; 3) the max/median filter; 4) the max/mean filter. In the paper a methodology to experimentally compare the performance of the different techniques is described and the results obtained over real IR data are discussed.

[1]  Inder J. Gupta,et al.  A novel signal processing technique for clutter reduction in GPR measurements of small, shallow land mines , 2000, IEEE Trans. Geosci. Remote. Sens..

[2]  Meng Hwa Er,et al.  Max-mean and max-median filters for detection of small targets , 1999, Optics & Photonics.

[3]  Luis Mateus Rocha,et al.  Singular value decomposition and principal component analysis , 2003 .

[4]  Benjamin Friedlander,et al.  A Frequency Domain Algorithm for Multiframe Detection and Estimation of Dim Targets , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Jianyu Yang,et al.  A Back-projection algorithm to stepped-frequency synthetic aperture through-the-wall radar imaging , 2007, 2007 1st Asian and Pacific Conference on Synthetic Aperture Radar.

[6]  Lawrence K. Saul,et al.  Maximum likelihood and minimum classification error factor analysis for automatic speech recognition , 2000, IEEE Trans. Speech Audio Process..

[7]  Tamar Peli,et al.  Morphology-based algorithm for point target detection in infrared backgrounds , 1993, Defense, Security, and Sensing.

[8]  Ram M. Narayanan,et al.  Enhanced resolution in SAR/ISAR imaging using iterative sidelobe apodization , 2005, IEEE Transactions on Image Processing.

[9]  R.M. Gagliardi,et al.  Application of Three-Dimensional Filtering to Moving Target Detection , 1983, IEEE Transactions on Aerospace and Electronic Systems.

[10]  F. Ahmad,et al.  A wideband, synthetic aperture beamformer for through-the-wall imaging , 2003, IEEE International Symposium on Phased Array Systems and Technology, 2003..

[11]  David S. K. Chan,et al.  Spatial processing techniques for the detection of small targets in IR clutter , 1990, Defense + Commercial Sensing.

[12]  G. W. Wei,et al.  Generalized Perona-Malik equation for image restoration , 1999, IEEE Signal Processing Letters.

[13]  Jing Zhang,et al.  Factor analysis based anomaly detection , 2003, IEEE Systems, Man and Cybernetics SocietyInformation Assurance Workshop, 2003..

[14]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[15]  Braham Barkat,et al.  Signal processing techniques for landmine detection using impulse ground penetrating radar , 2002 .

[16]  H.B.D. Sorensen,et al.  Comparison of PCA and ICA based clutter reduction in GPR systems for anti-personal landmine detection , 2001, Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing (Cat. No.01TH8563).

[17]  I. Reed,et al.  Adaptive Optical Target Detection Using Correlated Images , 1985, IEEE Transactions on Aerospace and Electronic Systems.

[18]  Henry Leung,et al.  A multiple-model prediction approach for sea clutter modeling , 2003, IEEE Trans. Geosci. Remote. Sens..

[19]  C. H. Chen,et al.  ICA and Factor Analysis Application in Seismic Profiling , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[20]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[21]  John T. Barnett,et al.  Nonlinear morphological processors for point-target detection versus an adaptive linear spatial filter: a performance comparison , 1993, Defense, Security, and Sensing.

[22]  T. Ens,et al.  Blind signal separation : statistical principles , 1998 .

[23]  Konstantinos I. Diamantaras,et al.  PCA neural models and blind signal separation , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[24]  Guo Xiaosong,et al.  An Improvement Algorithm of Principal Component Analysis , 2007, 2007 8th International Conference on Electronic Measurement and Instruments.

[25]  A. Jostingmeier,et al.  Clutter removal for landmine using different signal processing techniques , 2004, Proceedings of the Tenth International Conference on Grounds Penetrating Radar, 2004. GPR 2004..

[26]  Eric L. Miller,et al.  Statistical method to detect subsurface objects using array ground-penetrating radar data , 2002, IEEE Trans. Geosci. Remote. Sens..

[27]  Sungjin Hong.  Warped image factor analysis , 2005, 1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005..

[28]  Steven D. Blostein,et al.  Detecting small, moving objects in image sequences using sequential hypothesis testing , 1991, IEEE Trans. Signal Process..

[29]  Nasser M. Nasrabadi,et al.  Eigenspace transformation for automatic clutter rejection , 2001 .

[30]  B. Zhang,et al.  GPR Ground Bounce Removal Methods Based on Blind Source Separation , 2006 .

[31]  L. van Kempen,et al.  Signal processing techniques for clutter parameters estimation and clutter removal in GPR data for landmine detection , 2001, Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing (Cat. No.01TH8563).

[32]  F. Abujarad,et al.  GPR Data Processing Using the Component-Separation Methods PCA and ICA , 2006, Proceedings of the 2006 IEEE International Workshop on Imagining Systems and Techniques (IST 2006).

[33]  H. Krim,et al.  Robust independent component analysis , 2005, IEEE/SP 13th Workshop on Statistical Signal Processing, 2005.