Abstract— A cooperative spatial filtering method is presented to detect small targets around horizontal region for infrared search and track (IRST). Double window filter (DWF) can enhance signal-to-noise ratio then directional background removal filter (DBRF) can subtract horizontal background structure. Experimental results present upgraded detection rate and false alarm rate.

I. INTRODUCTION AND BACKGROUND EA-based IRST system should be able to detect distant targets such as sea-skimming missiles, fighter planes, and

asymmetric ships as quickly as possible. Those distant targets are projected into images around horizontal line according to the geometric analysis as shown in Fig. 1. The simplest spatial filter such as mean subtraction filter (MSF) can detect all point targets1. This method also generates many false alarms since infrared sensors are weak to thermal noise. MSF can miss threat targets if they enter structural region such as horizontal line or scan noise line where MSF generates strong edge response. Double window filter (DWF, center-surround difference) can be one of good solutions to reduce false alarms by salt and pepper noise by enhancing signal-to-noise ratio (SNR). However, it may miss true targets by the strong edge response as MSF2. Bouma et al proposed directional background removal filter (DBRF) which is directly applied to an input image to subtract background structure3. However, it cannot reduce the noise problem.

In this paper, we present a cooperative filtering method to solve both the noise problem (false alarm) and the structural response problem (target missing) by combining the DWF and the DBRF. The proposed method is described in Section II and experimental results are presented in Section III. We conclude and discuss in Section IV.

Fig. 1 Relation between distance and projected target position

II. PROPOSED SMALL TARGET DETECTION METHOD Fig. 2 summarizes the flow of small target detection method.

For an input image, DWF generates a SNR-enhanced image. After DWF, DBRF removes horizontal background structures. Final detection results are obtained through the Hysteresis threshold based detection method3. The key contribution of this paper is to conduct the DWF and the DBRF consecutively to

upgrade detection rate and reduce false alarm rate. DBRF consists of directional background estimation and removal from DWF results. Candidate target information (position + size) is obtained by a consecutive pre-thresholding and clustering. Final targets are selected by a final threshold.

Detection Method

Input image DWF DBRF Hysteresis Threshold

based DetectionResults

Spatial filtering

Fig. 2 The proposed small target detection system DWF Process: Double window filter is composed of inner region (target signal estimation part) and outer region (background signal estimation part). Signals are estimated by averaging two regions separately then differential output is produced. DWF is the same name as box filtering or center-surround difference filtering. If we denote an input image as ( , )I x y where x is column position and y is row position, DWF results ( ( , )DWFI x y ) are obtained by eq. (1).

( , ) ( , )

1( , ) ( , ) ( , )T B

DWF ijOUTi j W i j W

I x y w I x i y j I x i y jN∈ ∈

= − − − − −∑ ∑ (1)

where TW represents inner window region to estimate target signal and BW represents outer window region to estimate background signal. We use 3×3 window for target region and 11×11 window which excludes internal 3×3 window. ijw is the weight coefficients such as [0.7 0.8 0.7; 0.8 1 0.8; 0.7 0.8 0.7]/7 to match target shape. OUTN is the number of pixels belonging to the outer background window. DBRF Process: Currently, we are developing a scan-based IRST sensor system which uses 1D detectors. In such system, row pixels have similar responses around horizontal region (see the scan lines in Fig. 1). Basically, we assume that horizontal line is aligned to the row axis of an image. So, it is suitable to estimate background along the scan direction for each row. For each row, the number of target pixels is much smaller than that of background pixels. This means that target pixels are regarded as outliers and background pixels are regarded as inliers. Directional background estimation (DBE) is the same as the estimation of inliers. There are many papers related to the robust estimators such as median, Least Median of Squares (LMedS), RANdom SAmple Consensus (RANSAC), m-estimator, and so on4. In this paper, we use the simplest estimator, median which has 50% of breakdown point. The breakdown point is defined as the smallest percentage of

Sungho Kim, Yukyung Yang, and Joohyoung Lee Agency for Defense Development, Daejeon 305-600, Korea

Robust Horizontal Target Detection with Cooperative Spatial Filtering

S

978-1-4244-5417-4/09/$26.00 ©2009 IEEE978-1-4244-5417-4/09/$26.00 ©2009 IEEE

contaminated data (outliers) that can cause the estimator to take on arbitrarily large aberrant values. The simple mean estimator shows 0% of breakdown point. The proposed DBRF ( ( )DBRFI y ) is defined as eq. (2) and (3). After calculation of eq. (2), each row of background image has the same value. Then DBRF is obtained by subtraction ( )DBEI y from ( , )DWFI x y .

{1,2, , }( ) ( ( , ))DBE DWFx X

I y median I x y∈

=L

(2)

( , ) ( , ) ( )DBRF DWF DBEI x y I x y I y= − (3)

where X represents maximal column position. Target detection by hysteresis threshold: We use a conventional target detection method such as hysteresis threshold. This method has two thresholds. Threshold 1 is very small, which generates candidate target regions. Threshold 2 is relatively high depending on the operational condition (detection rate, false alarm rate). Through the threshold 2, we can detect final targets.

III. EXPERIMENTAL RESULTS The proposed spatial filtering method can enhance both the

signal-to-clutter ratio (SCR) and the detection ratio. We validate the performance of the proposed method by applying it to sea-sky background images (100) acquired by a long wave infrared camera. As indicated by the arrows in Fig. 3 (a), there are a ship target in horizon, an aerial target, and several ship targets near sensor. We regarded several buoys on the sea as clutters. In the first evaluation, we use the SCR, improvement of SCR (ISCR), background suppression factor (BSF) metrics defined in Ref. 5 to measure the performance of spatial filter before detection. We compared the results of baseline method (DWF only) and the proposed (DWF+DBRF) method. As shown in Table 1, the proposed method outperforms the baseline method in terms of ISCR and BSF for 10 frames. Fig. 3 (b) and Fig. 3 (c) represent the results of DWF and DBRF, respectively. Note that DBRF can remove background structures such as scan noise and horizontal contrast. This leads to upgraded ISCR and BSF as shown in Table 1. In the second evaluation, we compared the detection rate and false alarms per image to evaluate the detection performance. As summarized in Table 1, the statistical detection performances are much better than the others with the same threshold (th1=13, th2=7). Fig 2 (d), (e) show an example of target detection result with the baseline method and the proposed method, respectively.

IV. CONCLUSIONS A novel cooperative spatial filtering method is presented to

detect small horizontal targets robustly. DBRF after DWF is optimal combination in terms of filtering performance and detection. The proposed method will be exported to an IRST system.

(a) input

(b) DWF

(c) DWF+DBRF

(d) Results of DWF

(e) Results of DWF+DBRF

Fig. 3 Target detection results: DWF vs. Proposed (DWF+DBRF) Table 1. Statistical performance between DWF and Proposed

Metrics Baseline (DWF only)

Proposed (DWF+DBRF)

ISCR 4.50 7.38 BSF 7.37 14.8

Detection rate [%] 33 91 False alarms/image 31 24

REFERENCES [1] R. C. Warren, ‘Detection of distant airborne targets in cluttered

backgrounds in infrared image sequences’, Ph.D. Thesis, University of South Australia, 2002.

[2] Z. Chen et al., ‘Small target detection algorithm based on average absolute difference maximum and background forecast’, Int. J. Infrared Milli Waves, 28, 2007, pp. 87-97

[3] H. Bouma. et al., ‘Automatic detection of small surface targets with electro-optical sensors in a harbour environment’, Proc. of SPIE, 7114, 2008

[4] P. Meer, ‘Robust Computer Vision: An interdisciplinary challenge’, Computer Vision and Image Understanding, 78, 2000, pp. 1-7

[5] B. Zhang et al., ‘Fast new small-target detection algorithm based on a modified partial differential equation in infrared clutter’, Optical Engineering, 46, 10, 2007, 106401