Mohamed Alkanhal

Improving the false alarm capabilities of the maximum average correlation height correlation filter

We show that the maximum average correlation height (MACH) correlation filter overemphasizes the importance of the average training image leading to poor discrimination of the desired class images from clutter images. To overcome this, two new metrics termed the all image correlation height (AICH) and the modified average similarity measure (MASM) are introduced and optimized in a new correlation de- sign. The resulting filter exhibits improved clutter rejection performance while retaining the attractive distortion tolerance feature of the MACH filter. Simulation results based on a simulated synthetic aperture radar (SAR) image database are presented to illustrate the new filters proper- ties.

Polynomial distance classifier correlation filter for pattern recognition

We introduce what is to our knowledge a new nonlinear shift-invariant classifier called the polynomial distance classifier correlation filter (PDCCF). The underlying theory extends the original linear distance classifier correlation filter [Appl. Opt. 35, 3127 (1996)] to include nonlinear functions of the input pattern. This new filter provides a framework (for combining different classification filters) that takes advantage of the individual filter strengths. In this new filter design, all filters are optimized jointly. We demonstrate the advantage of the new PDCCF method using simulated and real multi-class synthetic aperture radar images.

New two-stage correlation-based approach for target detection and tracking in forward-looking infrared imagery using filters based on extended maximum average correlation height and polynomial distance classifier correlation

A novel approach is proposed to recognize and track multiple identical and/or dissimilar targets in forward-looking infrared (FLIR) image sequences using a combination of an extended maximum average correlation height (EMACH) filter and polynomial distance classifier correlation filter (PDCCF). The EMACH filter and PDCCF are trained a priori using representative training images of targets with expected size and orientation variations. In the first step, the input scene is correlated with all EMACH filters (one for each desired or expected target class). Based on the regions with higher correlation peak values in the combined correlation output, a sufficient number of regions of interest (ROIs) are selected from the input scene. In the second step, a PDCCF is applied to these ROIs to identify target types and reject clutter and background. Moving-target detection and tracking is accomplished by applying this technique independently to all incoming image frames. Independent tracking of target(s) from one frame to the other allows the system to handle complicated situations such as a target disappearing in a few frames and then reappearing in later frames. This method yields robust performance for challenging FLIR imagery in terms of accurate detection and classification as well as tracking of the targets.

Eigen-extended maximum average correlation height (EEMACH) filters for automatic target recognition

Correlation filters are attractive for SAR automatic target recognition (ATR) due to their distortion tolerance ability. Recently, a new filter called the extended maximum average correlation height (EMACH) filter was shown to exhibit low false alarm rate while providing good distortion tolerance. The trade-off between distortion tolerance and clutter rejection is achieved in the EMACH filter by selecting a parameter (beta) . The performance of this filter was examined using a simulated SAR database. In this paper, we develop a new filter called the eigen EMACH filter. This filter is based on decomposing the EMACH filter using the eigen-analysis. We show that this filter has better generalization ability compared to the EMACH filter. Also, we illustrate that this filter exhibits a consistent performance over a wide range of (beta) values. In this paper, we show that this filter provides better representation of the desired class while retaining the clutter rejection capability of the original EMACH filter. We use the MSTAR databases to test the performance of this filter.

Automatic target recognition and tracking in FLIR imagery using extended maximum average correlation height filter and polynomial distance classifier correlation filter (Invited Paper)

Over the last two decades, researchers investigated various approaches for detection and classification of targets in forward looking infrared (FLIR) imagery using correlation based techniques. In this paper, a novel technique is proposed to recognize and track single as well as multiple identical and/or dissimilar targets in real life FLIR sequences using a combination of extended maximum average correlation height (EMACH) and polynomial distance classifier correlation filter (PDCCF). The EMACH filters are used for the detection stage and PDCCF filter is used for the classification stage for improving the detection and discrimination capability. The EMACH and PDCCF filters are trained a priori using target images with expected size and orientation variations. In the first step, the input scene is correlated with all the detection filters (one for each desired or expected target class) and the resulting correlation outputs are combined. The regions of interest (ROI) are selected from the input scene based on the regions with higher correlation peak values in the combined correlation output. In the second step, PDCCF filter is applied to these ROIs to identify target types and reject clutters/backgrounds based on a distance measure and a threshold. Moving target detection and tracking is accomplished by applying this technique independently to all incoming image frames. Independent tracking of target(s) from one frame to the other allows the system to handle complicated situations such as a target disappearing in few frames and then reappearing in later frames. This method has been found to yield robust performance for challenging FLIR imagery in terms of faster and accurate detection and classification as well as tracking of the targets.

Improved clutter rejection in automatic target recognition (ATR) synthetic aperture radar (SAR) imagery using the extended maximum average correlation height (EMACH) filter

Correlation filters are attractive for synthetic aperture radar (SAR) automatic target recognition (ATR) because of their shift invariance and potential for distortion-tolerant pattern recognition. In particular, the maximum average correlation height (MACH) filter exhibits better distortion tolerance than other linear correlation filters. Despite its attractive features, it has been shown that the MACH filter relies perhaps too heavily on the average training image leading to poor clutter rejection performance. To improve the clutter rejection performance, we have introduced the extended MACH (EMACH) filter. We have shown that this new filter is better at rejecting clutter images while retaining the distortion tolerance feature of the original MACH filter. In this paper, we introduce a method to decompose the EMACH filter to further improve its performance. The paper describes the theory of this method and shows its potential advantages. Test results of this method using the public domain MSTAR data base are shown.

Analysis of signal-to-noise ratio of polynomial correlation filters

In this paper we present a variation of the polynomial correlation filter (PCF) called constrained correlation polynomial filter (CPCF). We investigate the performance of this filter in the presence of noise. The peak-to-sidelobe ratio measure and the public MSTAR images database are used for evaluation. The effect of different terms in the polynomial filter is examined by simulation. Then, we introduce a theoretical framework called energy projection to predict the effectiveness of different terms in the CPCF.

Combining multiple correlators using neural networks

Designing a pattern classifier remains a difficult problem especially in the presence of noise and other degradations. Combination of multiple classifiers appears to be a good way of retaining the strengths of different classifiers while avoiding their weaknesses. Different combination schemes were proposed in the literature. As a special case of combining multiple classifiers, we consider combining correlators. Correlators are attractive for use in Automatic Target Recognition systems. Many correlation filter designs have been developed, each with its own features. Some filter designs maximize noise tolerance but do not provide sharp peaks. On the other hand, some correlation filters yield sharp correlation peaks but are overly sensitive to input noise. In this research effort, we explore the use of artificial neural network as a tool for combining correlators. Results of this implementation show improvements and indicate that combination of multiple correlators can potentially improve the classification performance.

Improving the false alarm capabilities of composite correlation filters

Despite much prior work, one of the problems that still persists in using composite correlation filters for Automatic Target Recognition (ATR) is the high false alarm rate due to clutter. In this paper, we propose two methods (one based on clustering and another based on extending the maximum average correlation height or MACH filter) to improve the clutter rejection capability of composite filters. Initial numerical results are presented to illustrate the potential improvements.

Effect of constraint phases on the clutter rejection performance of SDF filters

Synthetic Discriminant Function (SDF) filters are characterized by hard constraints placed on correlation peak values. It is shown that we can obtain more control on the clutter rejection performance of the SDF filters by using complex constraints. Also, analytical expressions are derived that connect the average correlation peak intensity to constraint phases.