Moving dim point target detection with three-dimensional wide-to-exact search directional filtering

doi:10.1016/j.patrec.2006.07.006

Pattern Recognition Letters

Volume 28, Issue 2, 15 January 2007, Pages 246-253

https://doi.org/10.1016/j.patrec.2006.07.006 Get rights and content

Abstract

We assess the performance of a novel three-dimensional wide-to-exact search directional filtering (3DWESDF) algorithm for detecting and tracking a weak moving dim target against a cluttered background in real infrared (IR) image sequence. This paper proposed a novel 3DWESDF to decrease the 3D directional filter’s (3DDF) computational requirements and increase the target energy accumulation ability further. Prior to the filtering, a newly pre-whitening method termed three-dimensional spatial-temporal adaptive prediction filter (3DSTAPF) used here to suppress complex cluttered background. Extensive experimental results demonstrate the proposed algorithm’s ability in detecting weak dim point target against a complex cloud-cluttered background in real IR image sequence. Finally, performance comparisons of the proposed algorithm and 3DDF, on real IR image data, are presented in which the advantages of the proposed 3DWESDF filters are shown.

Section snippets

Introduction and motivation

A crucial problem in spaceborne and IR (infrared) surveillance system today is the detection and recognition of weak moving targets at low signal-to-noise/clutter ratios. Algorithms are currently available to reduce background clutter and to enhance target detectability. There are two basic target detection approaches: detect-before-track (DBT) and track-before-detect (TBD) methods. The former can have a reasonable performance only for high signal-to-noise/clutter ratio (SNCR). But for low

Clutter suppression

Let a sequence of two-dimensional (2D) images be taken at uniform time intervals by IR sensors whose field of view is fixed with respect to background, and the background is relatively holding still. Stacking the image sequence yields a 3D image sequence that can be described in spatial-temporal coordinates (x, y, t). Here, (x, y) are the spatial coordinates, while t is the time coordinates.

In order to make the 3DWESDF work well, clutter suppression is necessary. The usual ways are local mean

3D directional (matched) filter theory

A directional filter designed to give enhancement to signals, with straight line features in a certain direction embedded in whitening noise (i.e. Gaussian noise or quasi-Gaussian noise).

When a moving target crosses the field-of-view, it leaves a trace (or a signature) in the 3D image. The trace is manifested by a change of magnitude or gray in the pixels around the target trajectory. The change in magnitude is roughly proportional to the time spent by the target on the specific pixel. The

3D wide-to-exact search directional filter (3DWESDF)

In order to realize real-time processing, we develop 3DWESDF to decrease the computational requirements greatly and increase the target energy accumulation ability further. Let us consider the 3DDF operation in the spatial-temporal domain, using inverse Fourier transform to formula (11), the 3D impulse response expressed as $g (\bar{r}, t) = h (\bar{r}, t) * s (\bar{r}, t) = \frac{T^{2}}{N_{0}} \int \int \int {sinc}^{2} [(\bar{ω} \cdot \bar{v} + ω_{t}) T / 2] \exp {- j \bar{ω} \cdot \bar{r_{0}}} \exp {- j (ω_{t} t + \bar{ω} \cdot \bar{r})} d ω_{t} d \bar{ω} = \frac{T}{N_{0}} δ^{2} (\bar{r} - \bar{r_{0}} - \bar{v} t) Λ (t / T)$ where $h (\bar{r}, t)$ is the spatial-temporal domain expression of $H (\bar{ω}$

Experimental results

We use ISCR increasing and the range of intensity value (RIV) of the image to evaluate the background suppression ability. Fig. 7 depicts the performance comparison of TDLMS and 3DSTAPF algorithm. We list the ISCRs of original image sequence and TDLMS result and 3DSTAPF result in Table 1, including their RIVs. The original image sequence is a real infrared (IR) image sequence, and the target trace in it is synthetic. The velocity of the target is 1 p/f in its magnitude and 30° in its direction.

Conclusion

In this letter, we have presented an improved algorithm 3DWESDF and a useful background algorithm 3DSTAPF. We evaluated the performance of the 3DWESDF and 3DSTAPF methods as applied to IR image sequences under real world conditions. We also illustrate the performance comparisons of the proposed 3DWESDF algorithm and 3DDF on real IR image data. With our algorithms, we can improve the ISCR from 0.2317 to 13.7651 step by step. And we can decrease the computing time from 24 min and 54 s to 2 min and 4

Acknowledgements

This work was supported by the National Natural Science Foundation of PR China (No. 60135020) and the National emphatic pre-research project of PR China (No. 113020402).

References (17)

K. Akira et al.
Motion detection using 3D-FFT spectrum
IEEE Internat. Conf. Neural Networks Signal Process.
(2003)
J.P.J. Arnold
Detection and tracking of low-observable targets through dynamic programming
Proc. SPIE
(1990)
Y. Barniv
Application of velocity filtering to optical-flow passive ranging
IEEE Trans. Aerospace Electronic Systems
(1992)
D.S.K. Chan et al.
Spatial processing techniques for the detection of small targets in IR clutter
Proc. SPIE
(1990)
Y. Chen
On suboptimal detection of three-dimensional moving targets
IEEE Trans. Aerospace Electronic Systems
(1989)
M. Fernandez et al.
Detecting and tracking low-observable targets using IR
Proc. SPIE
(1990)
Fries, R.W., 1989. Three-dimensional matched filtering. Society of Photo-Optical Instrumentation Engineers, Code 1050...
A.L. George et al.
Filtering of moving targets using SBIR sequential frames
IEEE Trans. Aerospace Electronic Systems
(1995)

There are more references available in the full text version of this article.

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Citation Excerpt :
Li et al. [21] developed a three-dimensional double directional filtering (3DDDF) algorithm against the complex cluttered background. And a new 3-D wide-to-exact search directional filtering [22] is proposed by Zhang et al., which can accurately obtain the target’s speed and improve the energy-accumulation ability. However, the computation of exhaustive search is too large by these methods.
In a space-based infrared system, the enhancement of dim and moving targets plays a crucial role in improving detection capabilities and energy accumulation is an effective way of it. However, the performance of existing approaches of energy accumulation mainly depends on the accuracy of the trajectory extraction, which suffers from limitations due to the low signal-to-noise ratio (SNR) and unknown target-location. Therefore, this paper proposed a new enhancement method based on the extraction of the target’s precise trajectory to break this constraint. In this work, maximum projection is improved to estimate the rough location of the target by spatial and temporal information. Then, the precise trajectory is extracted by the optimization of the multi-frame shift stacking based on this location. Finally, the target’s energy is accumulated along the precise trajectory. Thus, the random noise is suppressed and the SNR is improved notably in the infrared images. Experimental results indicate that this proposed method is effective and robust for the enhancement of dim and moving targets compared with state-of-the-art algorithms. In addition, this method can be used in the infrared detection system and provides convenience for follow-up processing.
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View full text

Moving dim point target detection with three-dimensional wide-to-exact search directional filtering

Abstract

Section snippets

Introduction and motivation

Clutter suppression

3D directional (matched) filter theory

3D wide-to-exact search directional filter (3DWESDF)

Experimental results

Conclusion

Acknowledgements

Motion detection using 3D-FFT spectrum

IEEE Internat. Conf. Neural Networks Signal Process.

Detection and tracking of low-observable targets through dynamic programming

Proc. SPIE

Application of velocity filtering to optical-flow passive ranging

IEEE Trans. Aerospace Electronic Systems

Spatial processing techniques for the detection of small targets in IR clutter

Proc. SPIE

On suboptimal detection of three-dimensional moving targets

IEEE Trans. Aerospace Electronic Systems

Detecting and tracking low-observable targets using IR

Proc. SPIE

Filtering of moving targets using SBIR sequential frames

IEEE Trans. Aerospace Electronic Systems