Automated synthesis of distortion-invariant filters: AutoMinace

2007

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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). In our previous work, we used the MSTAR public database benchmark three-class problem and demonstrated better results than all prior work. In this paper, we address classification (including variants) and object and clutter rejection tests on the more challenging MSTAR ten-class public database. The Minace algorithm is shown to generalize well to this larger classification problem. We use several filters per object, but fewer DIFs per object than prior work did. We use our autoMinace algorithm that automates selection of the Minace filter parameter c and selection of the training set images to be included in the filter. No confuser, clutter, or test set data are present in the training or the validation set. 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 also address tests with proper use of SAR pose estimates in MSTAR recognition and the use of multilook SAR data to improve performance.

“…8; it is reproduced here for the sake of completeness. This section describes the current version 17 of the original Minace filter 2 that we use.…”

Section: Minace Filter Theory and Formulationmentioning

confidence: 97%

Section: Automated Minace Synthesis Overviewmentioning

confidence: 99%

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SAR classification and confuser and clutter rejection tests on MSTAR ten-class data using Minace filters

2007

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“…4. It is also detailed in several conference papers 2,3 . Performance results on the Comanche database are presented in Sect.…”

Section: Introductionmentioning

confidence: 99%

Minace filter tests on the Comanche IR database

2007

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This paper presents our IR automatic target recognition (ATR) work on the Comanche database using the minimum noise and correlation energy (MINACE) distortion-invariant filter (DIF). The Comanche database contains real IR data of eight targets with aspect view and thermal state variations. We consider recognition of six of these targets and we consider rejecting two targets (confusers) and clutter. To handle the full 360° range of aspect view in Comanche data, we use a set of Minace filters for each object; each filter should recognize the object in some angular range. We use our autoMinace algorithm that uses a training and a validation set to select the Minace filter parameter c (which selects emphasis on recognition or discrimination) and to select 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. No confuser, clutter, or test set data are present in the training or the validation set. Use of the peak-to-correlation energy (PCE) ratio is found to perform better than the use of 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.

“…4.2) in our new autoMinace 13 synthesis algorithm. In our autoMinace 13 algorithm, no confuser, clutter, or test set data are present in the training or the validation set. In this paper, the peak-to-correlation energy (PCE) ratio is used as the correlation plane metric in both filter synthesis and in tests, since it has been shown 13 to work better than the correlation peak height metric for the Minace DIF.…”

Section: Introductionmentioning

confidence: 99%

Minace filter infrared target tracking, recognition, and rejection tests with aspect view, depression angle, and scale variations

2007

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We examine the sensitivity of minimum noise and correlation energy (MINACE) filters to three different types of distortion variations (aspect view, depression angle, and scale) that are typically present in infrared (IR) imagery used for automatic target recognition (ATR) and tracking applications. Prior DIF (distortion-invariant filter) ATR work has addressed at most two simultaneous variations -aspect view and depression angle variations for SAR data, and aspect view and thermal state variations for IR data. No prior Minace ATR work has addressed scale variations. In our tests, we consider all three simultaneous variations -aspect view, depression angle, and scale. This is new. Our goal is to determine if one Minace filter per object can handle full 360° aspect view variations and can handle small depression angle variations, and to determine the range of scales that one Minace filter per object can handle after training on data at one or more scales. This determines when new Minace filters are needed in an image closing sequence. In all cases, shifts of the target test inputs are considered. We use our autoMinace algorithm that automates selection of the Minace filter parameter c and the training set images to be included in the filter. We also consider rejection of unseen confuser objects and clutter. No confuser, clutter, or test set data are present in the training or the validation set. We present test results using both real and CAD IR data.