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

Patnaik, Rohit; Casasent, David P.

doi:10.1117/12.717318

Cited by 11 publications

(10 citation statements)

References 13 publications

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“…In both cases the improved performance of IGT is readily apparent. A visual comparison also shows the improvements in IGT over the results in [47,48].…”

Section: A Experimental Set-upmentioning

confidence: 92%

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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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“…In both cases the improved performance of IGT is readily apparent. A visual comparison also shows the improvements in IGT over the results in [47,48].…”

Section: A Experimental Set-upmentioning

confidence: 92%

“…The D7 and 2S1 classes are treated as confusers. Extensions of this work include experiments on all ten MSTAR classes with Gaussian kernel SVM as classifier [47], and similar comparisons using the Minace filter [48]. It must be noted that in each case, no clutter or confuser images are used in the training phase.…”

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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show abstract

“…In the 2-class case, the two solution vectors h 1 (2) In (2), there are N 1 and N 2 samples in each class. We note that the output for the second class is specified to be ≤ p (and not -T or -1, as in the standard SVM).…”

Section: Svrdm Algorithmmentioning

confidence: 99%

“…However, most of them did not consider rejection of unseen confuser objects when designing correlation filters as summarized in [2] (SAR), [3] (IR), and elsewhere; thus much correlation filter work did not address such a realistic and important issue in ATR. Other comparisons of ATR classifiers [4,5] show large false alarm rates for various standard classifiers (such as SVMs), while other comparisons consider classifiers trained on the false class to be rejected [6,7], which is not realistic.…”

Section: Introductionmentioning

confidence: 97%

A new SVM for scale, aspect, and depression angle tolerant IR object recognition

Wang

Casasent

2007

SPIE Proceedings

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In most ATR applications, objects are not only present with thermal and aspect view angle variations, its size (range) also changes as the sensor approaches the target, and depression angle variations can exist. Therefore, it is important and realistic to know how to handle these variations. We apply our new SVRDM (support vector representation and discrimination machine) classifier to address these problems. The SVRDM classifier has good generalization (like the standard SVM does), and it has the added property of a good rejection ability. In other words, it not only gives very promising recognition results on the true target classes, it is also able to reject other unseen objects (referred to as confusers). We address the following variation issues: the scale range one SVRDM can recognize when trained on data at one or more ranges, the depression angle difference one SVRDM can recognize when trained on data at only one (or several) depression angles, and the number of aspect views needed to be included in the training set to handle recognition of targets with aspect variations, and the classification and rejection performance. Thus, our results are most unique and worthwhile but are not easily compared to prior work. Recognition and rejection test results are presented on both simulated and real infra-red (IR) data.

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“…It is noticeable that the MINACE filter and SVM are used in recognition of the three-class targets, respectively [3,5]. Then, the two methods are extended to the recognition of the 10-class targets [15,16]. Therefore, in this paper, SRC is extended to classify the 10 targets, which really is more challenging than three-class targets.…”

Section: Introductionmentioning

confidence: 99%

Sparse Representation-Based SAR Image Target Classification on the 10-Class MSTAR Data Set

Song

Zhang

et al. 2016

Applied Sciences

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Recent years have witnessed an ever-mounting interest in the research of sparse representation. The framework, Sparse Representation-based Classification (SRC), has been widely applied as a classifier in numerous domains, among which Synthetic Aperture Radar (SAR) target recognition is really challenging because it still is an open problem to interpreting the SAR image. In this paper, SRC is utilized to classify a 10-class moving and stationary target acquisition and recognition (MSTAR) target, which is a standard SAR data set. Before the classification, the sizes of the images need to be normalized to maintain the useful information, target and shadow, and to suppress the speckle noise. Specifically, a preprocessing method is recommended to extract the feature vectors of the image, and the feature vectors of the test samples can be represented by the sparse linear combination of basis vectors generated by the feature vectors of the training samples. Then the sparse representation is solved by l 1 -norm minimization. Finally, the identities of the test samples are inferred by the reconstructive errors calculated through the sparse coefficient. Experimental results demonstrate the good performance of SRC. Additionally, the average recognition rate under different feature spaces and the recognition rate of each target are discussed.

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

Cited by 11 publications

References 13 publications

SAR Automatic Target Recognition Using Discriminative Graphical Models

SAR Automatic Target Recognition Using Discriminative Graphical Models

A new SVM for scale, aspect, and depression angle tolerant IR object recognition

Sparse Representation-Based SAR Image Target Classification on the 10-Class MSTAR Data Set

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