Abstract
Synthetic aperture radar (SAR) is an advanced microwave imaging system of great importance. The recognition of real-world targets from SAR images, i.e., automatic target recognition (ATR), is an attractive but challenging issue. The majority of existing SAR ATR methods are designed for single-view SAR images. However, multiview SAR images contain more abundant classification information than single-view SAR images, which benefits automatic target classification and recognition. This paper proposes an end-to-end deep feature extraction and fusion network (FEF-Net) that can effectively exploit recognition information from multiview SAR images and can boost the target recognition performance. The proposed FEF-Net is based on a multiple-input network structure with some distinct and useful learning modules, such as deformable convolution and squeeze-and-excitation (SE). Multiview recognition information can be effectively extracted and fused with these modules. Therefore, excellent multiview SAR target recognition performance can be achieved by the proposed FEF-Net. The superiority of the proposed FEF-Net was validated based on experiments with the moving and stationary target acquisition and recognition (MSTAR) dataset.
Highlights
Luca Pallotta and Christos IlioudisSynthetic aperture radar (SAR) [1,2,3] is an important modern microwave sensor system, with powerful capabilities, including high-resolution imaging, day-and-night use, and all-weather operation
The moving and stationary target acquisition and recognition (MSTAR) program collected a significant quantity of SAR images to evaluate the performances of advanced SAR automatic target classification or recognition (ATR) methods
The MSTAR dataset includes a large number of 0.3 m × 0.3 m resolution SAR images processed with an X-band spotlight
Summary
Synthetic aperture radar (SAR) [1,2,3] is an important modern microwave sensor system, with powerful capabilities, including high-resolution imaging, day-and-night use, and all-weather operation. Those qualities make it superior to other sensors, such as infrared and optical sensors, for some applications. With advances in SAR signal processing and imaging performance, people have been paying more attention to classifying or recognizing targets of interest from SAR images. Automatic target classification or recognition (ATR) has become an attractive but challenging problem in SAR research and application areas [4,5,6,7,8]. Many SAR ATR methods or algorithms have been employed in the past few decades, such as support vector machine (SVM) [13], conditional
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