Background suppression for cloud clutter using temporal difference projection

Abstract To remove high intensity cloud clutter in infrared image sequence containing point target with high velocity, based on the optimal log-likelihood ratio detector test (LLRDT) together with exploratory temporal data analysis, a method called standardized maximum projection of temporal difference on adjacent frames (SMPTDAF) is proposed. First, cloud scenario is classified and analysis according to temporal features. Second, mathematical difference models of adjacent frames for all regions are presented. Third, to obtain the optimal temporal performance under LLRDT operator, based on the models, projection method after differencing and its simplified method for practical application are established. Finally in the paper, we compared the proposed method against classical temporal suppression method named Moving Target Indicator (MTI) and wavelet method by test image sequence. Experimental results show that the average SCR gain exceeds 11 when the target SCR is from 1.0 and 3, which is better than results of some representative multi-frame filters mentioned above.

[1]  John J. Soraghan,et al.  Small-target detection in sea clutter , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Tianxu Zhang,et al.  Clutter suppression algorithm based on bidirectional local binary pattern for moving point target detection in infrared image sequences , 2010 .

[3]  Zhang Jianqi,et al.  Homogeneous background prediction algorithm for detection of point target , 2011 .

[4]  Jonathan Martin Mooney,et al.  Temporal filters for tracking weak slow point targets in evolving cloud clutter , 1996 .

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

[6]  H. V. Trees Detection, Estimation, And Modulation Theory , 2001 .

[7]  Fabio Lavagetto Automatic target detection in infrared sequences through semantic labeling , 1990, Optics & Photonics.

[8]  Hong Li,et al.  Small infrared target detection based on harmonic and sparse matrix decomposition , 2013 .

[9]  Xuelong Li,et al.  Track infrared point targets based on projection coefficient templates and non-linear correlation combined with Kalman prediction , 2013 .

[10]  Qingyu Hou,et al.  Scattering near specular direction for horizontally oriented ice discs , 2013, Other Conferences.

[11]  Dana H. Brooks,et al.  Detecting small moving objects using temporal hypothesis testing , 2002 .

[12]  Peter L. Chu Optimal projection for multidimensional signal detection , 1988, IEEE Trans. Acoust. Speech Signal Process..

[13]  Thia Kirubarajan,et al.  Integrated Clutter Estimation and Target Tracking using Poisson Point Processes , 2012, IEEE Transactions on Aerospace and Electronic Systems.

[14]  Jun Xu,et al.  An improved infrared dim and small target detection algorithm based on the contrast mechanism of human visual system , 2012 .

[15]  Stephen Marshall,et al.  Logic-based Nonlinear Image Processing , 2006 .

[16]  N. C. Mohanty Computer Tracking of Moving Point Targets in Space , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[18]  Jong-Sen Lee,et al.  Digital Image Enhancement and Noise Filtering by Use of Local Statistics , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Guoyou Wang,et al.  Efficient method for multiscale small target detection from a natural scene , 1996 .

[20]  Marco Diani,et al.  Space-time processing for the detection of airborne targets in IR image sequences , 2001 .

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

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

[23]  Offer Kella,et al.  Dynamic Programming Solution for Detecting Dim Moving Targets Part II: Analysis , 1987, IEEE Transactions on Aerospace and Electronic Systems.

[24]  J. Brown,et al.  Adaptive spatial-temporal filtering methods for clutter removal and target tracking , 2008, IEEE Transactions on Aerospace and Electronic Systems.

[25]  A. Tartakovsky,et al.  Nonstationary EO/IR Clutter Suppression and Dim Object Tracking , 2010 .

[26]  Ronald Jones,et al.  Algorithms for the Decomposition of Gray-Scale Morphological Operations , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Fei Zhang,et al.  Edge directional 2D LMS filter for infrared small target detection , 2012 .

[28]  James R. Zeidler,et al.  Adaptive whitening filters for small target detection , 1992, Defense, Security, and Sensing.

[29]  Peter L. Chu Efficient Detection of Small Moving Objects , 1989 .

[30]  I. Reed,et al.  A Detection Algorithm for Optical Targets in Clutter , 1987, IEEE Transactions on Aerospace and Electronic Systems.

[31]  Yair Barniv,et al.  Dynamic Programming Solution for Detecting Dim Moving Targets , 1985, IEEE Transactions on Aerospace and Electronic Systems.

[32]  J. R. Zeidler,et al.  Performance analysis of LMS adaptive prediction filters , 1990, Proc. IEEE.