Abstract

This paper proposes a synthetic aperture radar (SAR) automatic target recognition (ATR) method by target reconstruction based on attributed scattering centers (ASCs). The extracted ASCs can effectively describe the electromagnetic scattering characteristics of the target, while eliminating the background clutters and noises. Therefore, the ASCs are discriminative features for SAR ATR. The neighbor matching algorithm was used to build the correspondence between the test ASC set and corresponding template ASC set. Afterwards, the selected template ASCs were used to reconstruct the template image, whereas all the test ASCs were used to reconstruct the test image based on the ASC model. A similarity measure was further designed based on the reconstructed images for target recognition. Compared with traditional ASC matching methods, the complex one-to-one correspondence between two ASC sets was avoided. Moreover, all the attributes of the ASCs were utilized during the target reconstruction. Therefore, the proposed method can better exploit the discriminability of ASCs to improve the ATR performance. To evaluate the effectiveness and robustness of the proposed method, extensive experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset were conducted under both the standard operating condition (SOC) and typical extended operating conditions (EOCs).

Highlights

  • With its all-weather, all-day, and high-resolution capabilities, synthetic aperture radar (SAR) has been widely applied in both military and civilian applications

  • ConclTuhsiisonpsaper proposes a target recognition method of SAR images based on attributed scattering centers (ASCs)

  • This paper proposes a target recognition method of SAR images based on ASCs

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Summary

Introduction

With its all-weather, all-day, and high-resolution capabilities, synthetic aperture radar (SAR) has been widely applied in both military and civilian applications. A SAR ATR method involves two parts: feature extraction and classification. We call the second category “projection features” These features are extracted by projecting the original SAR image to low-dimensional manifolds using some transformation algorithms. Some manifold learning methods have been used for SAR feature extraction, with good ATR performances [10,11,12]. Because of the powerful classification capability, different kinds of CNNs have been designed for SAR target recognition with notably high effectiveness [31,32]. These classifiers may only adapt to features with uniform forms, for example, PCA feature vectors of same dimensionality.

Attribute Scattering Center Model
ASC Extraction
Data Preparation
Recognition under SOC
Recognition under EOCs
Configuration Variance
Large Depression Angle Variance
Method
Findings
Conclusions
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