Radar target identification using spatial matched filters

The application of spatial matched filter classifiers to the synthetic aperture radar (SAR) automatic target recognition (ATR) problem is being investigated at MIT Lincoln Laboratory. Initial studies investigating the use of several different spatial matched filter classifiers in the framework of a 2D SAR ATR system are summarized. In particular, a new application is presented of a shift-invariant, spatial frequency domain, 2D pattern-matching classifier to SAR data. Also, the performance of this classifier is compared with three other classifiers: the synthetic discriminant function, the minimum average correlation energy filter, and the quadratic distance correlation classifier. Introduction In support of the DARPA-sponsored Critical Mobile Target (CMT) program, MIT Lincoln Laboratory has developed a complete, end-to-end, 2D SAR automatic target recognition (ATR) system. This baseline ATR system performs three basic functions: first, a CFAR (constant false alarm rate) detector locates candidate targets in a SAR image on the basis of radar amplitude. Next, a target-size matched filter is used to accurately locate the candidate targets and determine their orientation; textural discriminants (fractal dimension, standard deviation, and ranked fill ratio) are then used to reject natural-clutter false alarms [1]. Finally, a pattern-matching classifier is used to reject cultural false alarms (man-made clutter discretes) and classify the remaining detections. High resolution (0.3 m × 0.3 m), fully polarimetric target and clutter data gathered by the Lincoln Laboratory Millimeter-wave Sensor have been used to evaluate the performance of the ATR system [2]. Prior to ATR processing, an optimal polarimetric processing technique known as the PWF (polarimetric whitening filter) is used to combine the HH, HV, and VV polarization components into minimumspeckle SAR imagery [3]. This processing technique has been shown to improve the performance of the detection, discrimination, and classification algorithms by reducing the clutter variance and by enhancing the target signature relative to the clutter background. The robustness of the ATR system has been demonstrated by testing it against targets with and without radar camouflage. The ultimate goals of this system are to operate in a widearea search mode, maintain a very low false alarm density on the order of 1 false alarm per 1000 km search area), and provide a high probability of detection (Pd ≈ 0.8). To meet the stringent false alarm requirement, it is essential for the classifier to reliably reject man-made clutter discretes. Also, to maintain a high probability of correct classification (Pcc), the classifier must provide good separation between classes and must be robust with respect to target variability. We have recently implemented several spatial matched filter classifiers, as possible alternatives to the patternmatching classifier used in the baseline ATR system. This paper describes them and presents preliminary performance results. Preprocessing methods and target variability issues are addressed. Performance results are given for a shift-invariant template matcher that we have recently developed. Algorithms are compared by presenting classifier-performance confusion matrices, which indicate the probability of correct and incorrect classification. The ability of each classifier to reject cultural false alarms (buildings, bridges, etc.) is quantified. ATR System Review This paper focuses on target classification algorithms. This section describes our complete SAR ATR system (detection, discrimination, and classification), in order to place the classification stage into a more general system context. A simplified block diagram of the multi-stage ATR system is shown in Figure 1; each stage of the multi-stage system is briefly discussed below. Stage 1. In the first stage of processing, a two-parameter CFAR detector [4] is used as a prescreener; this stage of processing locates potential targets in the image on the basis of radar amplitude. Since a single target may produce multiple detections, the CFAR detections are clustered (grouped together). Then a 128 pixel × 128 pixel region of interest (ROI) around the centroid of each cluster is passed to the next stage of the algorithm for further processing. Figure 1: Simplified block diagram of baseline automatic target recognition (ATR) system. Stage 2. The second stage of processing, called discrimination, takes as its input each ROI and analyzes it. The goal of discrimination processing is to reject natural-clutter false alarms while accepting real targets, This stage consists of three steps: (1) determining the position and orientation of the detected object, (2) computing simple textural features, and (3) combining the features into a discrimination statistic that measures how "target-like" the detected object is. In order to determine the position and orientations of the detected object, a target-size rectangular template is placed on the image and of slid and rotated until the energy within the template is maximized. This operation is computationally quick, since it is performed only on the ROIs obtained by the Stage 1 CFAR detector. Mathematically, this operation is equivalent to processing with a 2D matched filter where the orientation of the detected object is unknown. In step 2 of the discrimination stage, three textural features are calculated: (1) The standard deviation of the data within the targetsize template. This feature measures the statistical fluctuation of the data. Targets typically exhibit significantly larger standard deviations than natural clutter. (2) The fractal dimension of the pixels in the ROI. This feature provides information about the spatial distribution of the brightest scatterers of the detected object. It is complementary to the standard deviation feature, which depends only on the intensities of the scatterers and not on their spatial locations. (3) The ranked fill ratio of the data within the target-size template. This feature measures the fraction of the total target energy contained in the brightest 5% of the detected object’s scatterers. For targets, a significant portion of the total power is due to a small number of very bright scatterers; for natural clutter, the total power is distributed more evenly among the scatterers. Reference [1] provides a detailed description of the three textural discrimination features used in the baseline ATR system. Stage 3. The third and final stage of ATR processing is classification. Here a 2D pattern-matching algorithm is used to (1) reject cultural false alarms caused by man-made clutter discretes (buildings, bridges, etc.) and (2) classify the remaining detected objects. A four-class classifier (tank, APC, self-propelled gun, and clutter) is used. Those detected objects that pass the discrimination stage are matched against stored reference templates of the tank, APC, and gun targets. If none of the matches exceeds a minimum required score, the detected object is classified as clutter; otherwise, the detected object is assigned to the class with the highest match score. Data Description For this preliminary study, tactical target data of tanks, APCs, and self-propelled guns gathered in the 1989 Stockbridge, New York, data collection was used. Figure 2 shows photographs of each of the tactical targets. The target data consisted of imagery collected in spotlight mode at l deg azimuthal increments. In evaluating the performance of each classification algorithm, every other image (i.e. odd frame numbers) was used for training, and algorithm testing was performed using the even numbered frames. All of the targets used in these evaluations were bare (i.e. uncamouflaged). Tank