As introduced in Section Feature extraction is of crucial importance to the overall performance of the entire ATR system. The evaluation results for the 10 targets at depression angles 17° and 15° are listed in Tables Furthermore, the average estimation error of the above tests is illustrated as bar figure in Figures The various target poses introduce great variations into the SAR images. Since 1990s, SAR ATR has been intensively studied with many useful techniques. By continuing you agree to the Copyright © 2020 Elsevier B.V. or its licensors or contributors. learning framework. Introduction A synthetic aperture radar (SAR) target recognition method is proposed in this study based on target outlines. It is suggested that we employ one fixed mother wavelet for the entire recognition scheme. However, targets with partial defected contour shapes that are caused by the shadow effect may suffer from poor pose estimation accuracies.
Any author submitting a COVID-19 paper should notify us at Illustration of targets with complete contour shapes.Illustration of targets with defected contour shapes.Illustration of the proposed pose estimation method.Illustration of the proposed pose estimation method, where the red rectangle is the MBR and the green line is the estimation result of the Radon transform. Several features have already been exploited in SAR ATR [Certain features are not feasible to be directly applied to classification due to their high dimensionality [The nearest neighbour classifier is one of the most used classifiers, where the extracted features are directly fed into the classifier to achieve the classification results [The SAR images are known for their indistinct appearances, variations in target appearances, and small number of available training samples. The proposed method is evaluated with the public release database for moving and stationary target acquisition and recognition (MSTAR). However, a classifier learned from massive amounts of high varying data is not guaranteed to achieve good performance in classification and may yield large feature dimensions. This is because as the number of iterations increases, the wrongly labelled samples would be much less in number but have much larger weights. It has been experimentally proven in several researches that rotating images in certain directions or introducing rotationally invariant features is beneficial for improving classification accuracy [To evaluate the outlier rejection performance of the proposed method, a varying threshold for the log-likelihood test, which is introduced in Section The effectiveness of the proposed ATR scheme is tested in this section. It is ideally preferable to extract features that have characteristics of high discrimination ability (or, in other words, high interclass variation) and high tolerance to target translation. Since the final objective is classification, even if the learned distributions may not converge to the true distributions, the constructed discriminative models tend to have better discrimination performance than the generative models [In binary classification case, which can be naturally extended to the more general In most cases, it is impossible to have access to the true conditional distributions The recently proposed method named discriminative tree estimates the multivariate distributions We digress here to introduce the method for constructing models in generative fashion and then provide the method for constructing models in discriminative fashion. Moreover, SAR images are resized, shifted, and rotated to predefined standards.
The methods proposed in [In fact, the tactical ground targets show rectangular shaped boundaries, which can be used for pose estimation. This is an open access article distributed under the We are committed to sharing findings related to COVID-19 as quickly and safely as possible. ScienceDirect ® is a registered trademark of Elsevier B.V.Automatic target recognition of synthetic aperture radar images via gaussian mixture modeling of target outlinesScienceDirect ® is a registered trademark of Elsevier B.V. According to the experimental results, the proposed method achieves a high recognition accuracy of 98.34% in the 10-class recognition problem under the standard operating condition (SOC). The generative methods attempt to construct a model that is the same as the underling model of the classification target.
It is noted in Figure In this section, we compare the performance of feature extraction using the wavelet 192 (three-level wavelet decomposition) and the wavelet 768 (two-level wavelet decomposition).
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