Absfrucf--In this paper, a method based on adaptive filtering is proposed to detect dim small moving targets in strong undulant clutter background. This method ...
Dim Small Target Detection in Strong Undulant Clutter Background Based on Adaptive Filter Zhengzhou Li, Nengli Dong, Gang Jin (Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu , 610209, China) suppress clutter and detect targets. Ideally, the filter would
Absfrucf--In this paper, a method based on adaptive filtering is proposed to detect dim small moving targets in strong undulant clutter background. This method utilizes the difference between target and clutter with regard to statistic characteristic, the consistency of moving target’s trajectory, and the random fluctuation of noise. After the elimination of clutter, the original correlated image is reduced to independent random signal with Gaussian distribution. This is desirable yet diffcult for some usual algorithms, such as LMS filter. A test based on infrared sequence images captured outfield is presented. The results indicate that this method can suppress the strong undulant clutter and enhance targets efficiently; therefore, it can greatly improve the performance to detecting small targets. Key words: Strong undulant clutter, Dim small targets, Adaptive filter, Motion analysis
I. INTRODUCTION The technique of detecting and tracking dim small moving targets in heavy cluttered background has been widely researched for many years. Target detection algorithms have been steadily improving, whereas many of them failed to work robustly during applications involving changing backgrounds that are frequently encountered. In general, a small target embedded in cloudy background presents as a gray spot in image, which also contains bright illuminated terrain or sunlit clouds. In this case, clutter is often much more intensive than both sensor noise and the target signal, and therefore a single threshold technique is insufficient to discriminate the target from these bright clutter.
produce a strong response only in the presence of targets. To design this filter, knowledge about the characteristics of targets and clutter is required. But in many situations it is quite difficult to obtain this knowledge actually. Therefore it is a main obstacle for target detection, and the suppression of clutter becomes one of the most important problems in the target detection procedure. In general, the signal included in image is a sort of non-stationary random signal, and can be mostly divided into two classes. One class of signal has stationary variance but non-stationary mean, such as that of the clear sky background. The other one has both non-stationary mean and variance, such as that of the cloudy background. For the case of first class of signal, some adaptive and lineadnonlinear methods based on two-order stationary random signal analysis [ldl, such as the least-mean-square (LMS) filtering method, can effectively suppress the clutter and finally detect targets by a certain decision-making, But for the case of second class, it is di&cult to suppress clutter for these algorithms mentioned above. For example, Zhu [’I applied LMS filter to suppress the strong undulant clutter background with bright clouds. There exists much colored noise at the edge of clouds, and as a res$ the false-alarm ratio is very high because that it is difficult to eliminate these noise points efficiently by means of usual motion analysis without a-prior knowledge about the clouds. In [7]and [8], Jerry Silverman proposed a triple temporal filter (TTF) making use of the motion difference between targets and clouds to suppress slowly moving clouds. The TTF technique had been found many applications in the Staring Infrared Search and Track (SIST) systems, and got much
Since targets and clutter have different spatial frequency
achievement. But for these applications the parameters about moving clouds must be known a-prior. Li 19] put
characteristics, a spatial filter could be. designed to
forward a method based on gradient-inverse-weighted
neighborhood averaging, (GIWNA). filter . . to suppress.the
occupied only a few pixels is modeled as;,
strong undulant clouds. Although 'the performance is somehow improved, there was still much colored noise at the edge of clouds because some crucial parameters did not be adjusted according to the changing background. So
where k, is the size of target at time k , and Ai,j,k is an
..
.
..
.,
' ,
.
. !
,
the algorithm needs to be farther improved. In this paper,
GIWNA filter can greatly enhance the'target because it can
unknown signal intensity. In the image with strong undulant clutter background, the intensity of clutter is greater than that of target and instrument noise, therefore it is necessaly to eliminate clutter firstly.
effectively suppress the strong undulant clutter. The
'B. Clutter Suppression Technique
we propose a new method to adjust those parameters of GIWNA filter adaptively according to the statistical Characteristics of local neighborhood area. This modified
filtered image contains
Because the cloud's radiation is non-uniform, the sky background has strong undulant clutter. The 'clutter'is a non-stationary random signal, especially at. the edge of clouds. In [9], one method using GIWNA filter was put forward to suppress the undulant clutter: In this method, because some crucial parameters were not 'adjusted according to the changing background, there was much noise at the edge of cloud clutter after the filtering. As far as gray, the cloud clutter's space correlation is greater than that of target. Our algorithm utilizes this ,difference between clutter and target to adjust these parameters in time. The method is described as follows.
mainly target points and
approximately white noise with Gaussian distribution. Then a decision-making hlised on constant false-alarm ratio (CFAR) is utilized to segment the' filtered image, and many features of targets are extracted aftenvaid. In cases of very low SNR or SCR, there would be many points including real targets and strong noise in the filtered iniage. As it is known, the real target's trajectory has the
characteristics of 'consistency, so the target signal is relative between the . consecutive. frames and can be modeled as Markov random field. Nevertheless the noise
is fluctuating randomly. Therefore we design a 'fufther filter utilizing these features extracted previously, and use it to identify the real targets from noise. A sequence of infrared images acquired outfield has been tested to verify the detection performance of the proposed algorithm, and the results so far indicate that it can suppress the strong undulant clutter.and enhance targets effectively, and as a result the target detection ability is improved greatly. ''
In the k Th frame of the image sequence; the pixel's correlation function R(x, y , k ) with the neighborhood
around can be measured by the gradient-inverse absolute value.
4Wk)
11. STRONG UNDULANT BACKGROUND SUPPRESSION
where p is the order of GIWNA filter. Because the
A. Image Signal Modeling
parameter 0 in paper [9] describes the weight coeffcient
The image sequence with small target submerged in
of the pixels that have the same gray value as central
clutter can be modeled as:
pixelf(x, y , k ) , the relation function can be simplified as follows.
where ,x and y represent the spatial coordinates of the acquired image with size of NX x NY , k is frame number at the temporal axis, S ( x , y , k ) , N ( x , y , k ) , B ( x , Y , ~ ) where
denote the target signal, the zero mean measurement noise, and clutter background respectively. The small target
(m,n) denotes binary mark
information with value of zero or one
784
. . . . .
...
. with, Gaussian .:,
..........
j .
j ( x , y , k ) . 1s;assumed as .whiten . . . . noise . . . . . . . , . . x
i
i,
. . . .
The . probability ' -'distribution:. :function
distribution. Considering the effect. of quantization noise, the
as equation (12).
equation (5) should be modified as follows.
.
.
.
. . . . . ..
.
. . .. . .
.
. . . ,;.
The bigger the correlation function R ( x , y , k ) is, the greater' the parameter 0 should he: So parameter 0 should be a linear. or nonlinear .increasing. function of R ( x , y , k ) .Here, we use a linear function'fonimlated as: ',' ~'
B(x, y , k ) = K R(x, y , k )
(7)
where K is a positive constant. For a given pixel, the inverse gradient in its neighborhood is defined as:
4
d,,,,,k(m
.
-
.
.
.
. ., ._ , ., . , where p,D are the mean and variance of signalf(x,p,k)
..
,
.
.
. . . . . -. . . is segmented by .the . .following criteria.
.
. I
&,y,k)' , ,..
.,
others
.
.
constant false-alarm ratio (CFAR) Pfa. Then the'signal
'
1
. .
. .
respectively. Mostly, the intensityof target signal Is greater than.. that, of. noise. We can estimate' the mean p .and variance . o ,. and. calculate the threshold..,:Ti .by. the
.
,:.
.,
,
.
. .
.,
,
.
J
.
(13)
. .
The two-dimension filter
.
hx,y,k (m,n ) , is expressed as:
. .
h,,y,k( m d i:
,
. . . . . . . .. ,
.. . .
,
,
,
.
.
. . . . .
e
.
. . . . . . . . . .. .
1II:~TARGET.IDENTIFICATION BASED ON MULTI-FEATURES.ASSOCIATEDFILTER I
. .
others
'
After .segmentation;. we, can. determine. the,. total
number Tn of potential 'targets by. cohesion, and~.extract . ,. , ,_ , , . f,,', .and spatial 'coordinate features including gray 'mean . .. . . . . , . . . . , , . :. #
...
ri,k by combing the' binary image"f(x,y,'k) and the . . . .
The output of the filter is denoted by & , y , k )
' i ,
.
.
.
.
.
.
.
.. . .
.
..
original image f ( x , y , k ) . And so far, we constyct the : ..
'i-,
r
.
.
.
.
featureset Y , k ) =f ( x , y , k ) - & , k )
. .. . . . . . . . . . . ,
I
The feature set includes not only real target but also intensive white noise, so. it.is necessary to apply temporal filter to filter those:noise father: As .it .is known, white noise .is fluctuating,,and:has.little correlation.between the consecutive images. ,Nevertheless,the. motion ..of:target obeys determinate.dynamical rules. .Therefore the small target's trajectory has the characteristics of consistency and conjunction. These characteristics,determine that the target will appear in neighborhood at .the following frames with
(11)
C. Image Segmenting with CFAR Atter eliminating the background clutter, the signal
785
spot cycled by a white square is the target detected by our
maximum probability. At the same time, the illumination of target in short period keeps constant. Thus we can
method.
define the conjunction function as follows.
where
w is
a coefficient, and fYis a positive constant
which denotes the adjacent domain in successive frames. For any point, if the following equation is satisfied, it is likely a potential target point, otherwise noise.
Fig.1 The original image
where E is a positive constant. By the way, the small target will be detected and noise will be discarded after several images.
V. EXPERIMENT AND ANALYSIS
Fig.2 The binary image processed by LMS
The data used in experiment is an infrared image sequence captured outfield in summer. The clutter is mainly the strong undulant and bright cloud. Because the target is far away from the sensor, it presents as a little spot. Fig. 1 is one frame of the original images. The target is submerged in the cloud. The signal to noise or clutter ratio ( S N R or SCR) is very low. For observation, the target is cycled with a white cycle. Fig. 2 is the binary image
Fig.3 The binary image processed by paper 191
processed by LMS algorithm. From the figure we can fmd that the target is lost. In fact, the residual image is not white noise with non-Gaussian probability distribution, thus the threshold calculated with Gaussian probability distribution may fail. Fig. 3 is the binary image processed by the algorithm mentioned in [9] with the parameters:
p=5,6=10,PB =IO4
.
Fig. 4 is the binary image
Fig.4 The binary image processed by proposed met hod
processed by our algorithm with p = 5, K = 5 , pB = iod. By comparing Fig.3 and Fig.4, we can summarize the main difference as follows. In Fig.3 there is much colored noise at the edges of cloud because the clutter is not whiten sufficiently. The signal at the edge of cloud is strong non-stationary random signal, and the correlation between the pixel and its neighborhood is quite weak. SO the filter with constant 6 is not suitable for the changing signal. Fig. 5 is the detection result image, and the white
186
Fig.5 The detection result
VI. CONCLUSION As the strong undulant clutter is non-stationary signal,
Infrared Technology and Application XXIII, Proceeding of SPIE ~01.3061,pp.496-507, 1997. [8] Caefer, C.E., Silverman, J., Mooney, J.M., “Optimization of Point Target Tracking Filters”, IEEE
it can’t he suppressed effectively by those methods based on stationary signal analysis.
Our proposed method utilizes the difference between targets and clutter with regard to statistic character, the consistency of moving target’s trajectory, and the fluctuation of noise as well. This approach can suppress strong undulant clutter and enhance targets effectively. A test using infrared image sequence with strong undulant clouds is carried out, and the results indicate that this algorithm to detecting dim small moving targets is highly effective.
degree in applied physics from Northeastern University,
REFERENCES
China, in 1999. Since 1999, He has been a doctoral candidate in the Institute of Optics and Electronics,
Trans. on Aerospace and Electronic Systems, vo1.36, 110.4, pp.15-25,2000, [9] Li Jicheng, Shen Zhenkang, “Small Moving Target Detection in Clutter Infrared Background”, Infrared and Laser Engineering, vo1.26, no.6,pp. 8-13, 1997. Brief Biography: Zhengzhou Li was born in 1974. He received the BS
[l] A. Morin, “Adaptive Spatial Filtering Techniques for
Chinese Academy of Sciences, China. His research
the Detection of Targets in Infrared Image Seekers”,
interests including pattern recognition, image processing,
In
and target detection.
Acquisition,
Tracking, and
Pointing
XN,
Proceeding of SPIE, ~01.4052,pp.182-193,2000, [2] Askar H., Xiaofeng Li, Zaiming Li, “Background Clutter Suppression and Dim Moving Point Targets Detection Using Nonparametric Method”, IEEE 2002 International Conference on Communications, Circuits and Systems, V01.2, pp.982-986.2002. [3] Zhu Hang, Zhao Yigong, “Detection of Weak and Small Moving Infrared Targets By Adaptive Prediction of Background”, J. Infrared Millim. Waves, vol. 18,no.4,pp.305-310, 1999. ’[4] Yang Weiping, Shen Zhengkang, “The Detection Method of Adaptive Thresholding in Undulant Background”, .I. Infrared Millim. Waves, vol. 18, no.2, pp.120-124,1999.
[SI Nengli Dong, Gang Jin, et al. “New Approach to Detect Dim Moving Point Targets Based on Motion Analysis”, Signal and Data Processing of Small Targets, Proceeding of SPIE vol. 4473, pp.34-42,2001. [6] Zhenfu Zhu, Zhongling Liu, Haochen Liang, “ Morphological Filter and It’s Application in TV Tracker”, Image and Signal Processing for Remote Sensing, Proceeding of SPIE ~01.4170, pp. 265-271, 2001. [7] Jeny Silverman, Jonathan M.Mooney and Charlene E.Caefer, “Tracking Point Targets in Cloud Clutter”,
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