Confuser rejection performance of EMACH filters for MSTAR ATR

Casasent, David P.; Nehemiah, Avinash

doi:10.1117/12.664206

Cited by 8 publications

(3 citation statements)

References 7 publications

(45 reference statements)

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“…The quoted P D scores at P CFA = 2.5% and 5% were actually for the case when no variants were present in the test set (as we detail in Ref. 14). The true clutter rejection EER point was 12%, when variants were detected.…”

Section: Comparisons To Prior Mstar Resultsmentioning

confidence: 99%

MSTAR object classification and confuser and clutter rejection using Minace filters

Patnaik

Casasent

2006

Automatic Target Recognition XVI

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This paper presents the status of our SAR automatic target recognition (ATR) work on the Moving and Stationary Target Acquisition and Recognition (MSTAR) public database using the minimum noise and correlation energy (MINACE) distortion-invariant filter (DIF). We use a subset of the MSTAR public database for the benchmark three-class problem and we address confuser and clutter rejection. To handle the full 360° range of aspect view in MSTAR data, we use a set of Minace filters for each object; each filter should recognize the object (and its variants) in some angular range. We use fewer DIFs per object than prior work did. The Minace parameter c trades-off distortion-tolerance (recognition) versus discrimination (confuser/clutter rejection) performance. Our filter synthesis algorithm automatically selects the Minace filter parameter c and selects the training set images to be included in the filter, so that the filter can achieve both good recognition and good confuser and clutter rejection performance; this is achieved using a training and validation set. In our new filter synthesis method, no confuser, clutter, or test set data are used. The peak-to-correlation energy (PCE) ratio is used as the correlation plane metric in both filter synthesis and in tests, since it works better than correlation peak height. In tests, we do not assume that the test input's pose is known (as most prior work does), since pose estimation of SAR objects has a large margin of error; we describe our procedure for proper use of pose estimates in MSTAR recognition. The use of circular versus linear correlations is addressed. We also address the use of multi-look SAR data to improve performance.

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Section: Comparisons To Prior Mstar Resultsmentioning

confidence: 99%

MSTAR object classification and confuser and clutter rejection using Minace filters

Patnaik

Casasent

2006

Automatic Target Recognition XVI

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“…The average classification accuracies of the five methods for the different experimental scenarios are compared in Table XXII. Perhaps the first work to address the problem of clutter and confuser rejection in SAR ATR is [46]. In this work the outlier rejection capability of the EMACH filter [23] is demonstrated for a subset of the MSTAR data with three target classes -BMP2, BTR70, and T72.…”

Section: A Experimental Set-upmentioning

confidence: 99%

SAR Automatic Target Recognition Using Discriminative Graphical Models

Srinivas

Monga

Raj

2014

IEEE Trans. Aerosp. Electron. Syst.

162

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The problem of automatically classifying sensed imagery such as synthetic aperture radar (SAR) into a canonical set of target classes is widely known as automatic target recognition (ATR). A typical ATR algorithm comprises the extraction of a meaningful set of features from target imagery followed by a decision engine that performs class assignment. While ATR algorithms have significantly increased in sophistication over the past two decades, two outstanding challenges have been identified in the rich body of ATR literature: 1) the desire to mine complementary merits of distinct feature sets (also known as feature fusion), and 2) the ability of the classifier to excel even as training SAR images are limited. We propose to apply recent advances in probabilistic graphical models to address these challenges. In particular we develop a two-stage target recognition framework that combines the merits of distinct SAR image feature representations with discriminatively learned graphical models. The first stage projects the SAR image chip to informative feature spaces that yield multiple complementary SAR image representations. The second stage models each individual representation using graphs and combines these initially disjoint and simple graphs into a thicker probabilistic graphical model by leveraging a recent advance in discriminative graph learning. Experimental results on the benchmark moving and stationary target acquisition and recognition (MSTAR) data set confirm the benefits of our framework over existing ATR algorithms in terms of improvement in recognition rates. The proposed graphical classifiers Manuscript are particularly robust when feature dimensionality is high and number of training images is small, a commonly observed constraint in SAR imagery-based target recognition.

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“…Other DIFs such as the EMACH 3 and the OTSDF 4 filters require the selection of several parameters; the selection of these filter parameters is typically not automated. Our other work on the use of EMACH filters for SAR ATR 5 shows that the EMACH filter requires much test set data for parameter selection (which is not realistic). The selection of the training set images to be included in the filters is also typically not automated in other DIF work, i.e., all training set images are often included in the filter.…”

Section: Introductionmentioning

confidence: 98%

Automated synthesis of distortion-invariant filters: AutoMinace

Casasent

Patnaik

2006

SPIE Proceedings

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This paper presents our automated filter-synthesis algorithm for the minimum noise and correlation energy (MINACE) distortion-invariant filter (DIF). We discuss use of this autoMinace filter in face recognition and automatic target recognition (ATR), in which we consider both true-class object classification and rejection of non-database objects (impostors in face recognition and confusers in ATR). We use at least one Minace filter per object class to be recognized; a separate Minace filter or a set of Minace filters is synthesized for each object class. The Minace parameter c trades-off distortion-tolerance (recognition) versus discrimination (impostor/confuser/clutter rejection) performance. Our automated Minace filter-synthesis algorithm (autoMinace) automatically selects the Minace filter parameter c and selects the training set images to be included in the filter, so that we achieve both good recognition and good impostor/confuser and clutter rejection performance; this is achieved using a training and validation set. No impostor/confuser, clutter or test set data is present in the training or validation sets. Use of the peak-to-correlation energy (PCE) ratio is found to perform better than the correlation peak height metric. The use of circular versus linear correlations is addressed; circular correlations require less storage and fewer online computations and are thus preferable. Representative test results for three different databases -visual face, IR ATR, and SAR ATR -are presented. We also discuss an efficient implementation of Minace filters for detection applications, where the filter template is much smaller than the input target scene.

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Confuser rejection performance of EMACH filters for MSTAR ATR

Cited by 8 publications

References 7 publications

MSTAR object classification and confuser and clutter rejection using Minace filters

MSTAR object classification and confuser and clutter rejection using Minace filters

SAR Automatic Target Recognition Using Discriminative Graphical Models

Automated synthesis of distortion-invariant filters: AutoMinace

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