Abstract
Synthetic Aperture Radar (SAR), as one of the important and significant methods for obtaining target characteristics in the field of remote sensing, has been applied to many fields including intelligence search, topographic surveying, mapping, and geological survey. In SAR field, the SAR automatic target recognition (SAR ATR) is a significant issue. However, on the other hand, it also has high application value. The development of deep learning has enabled it to be applied to SAR ATR. Some researchers point out that existing convolutional neural network (CNN) paid more attention to texture information, which is often not as good as shape information. Wherefore, this study designs the enhanced-shape CNN, which enhances the target shape at the input. Further, it uses an improved attention module, so that the network can highlight target shape in SAR images. Aiming at the problem of the small scale of the existing SAR data set, a small sample experiment is conducted. Enhanced-shape CNN achieved a recognition rate of 99.29% when trained on the full training set, while it is 89.93% on the one-eighth training data set.
Highlights
High-resolution radar images in range and azimuth can be obtained by Synthetic Aperture Radar (SAR), which includes synthetic aperture principle, pulse compression technology, and signal processing technology
SAR automatic target recognition (SAR ATR) has become an important and promising field of remote sensing image processing. is paper proposed a method from the perspective of shape enhancement with filtering and enhancing target area at the input and synthesizing to strengthen the connection between channels
The information loss due to ordinary pooling is reduced by the application of SoftPool in convolutional neural network (CNN)
Summary
High-resolution radar images in range and azimuth can be obtained by Synthetic Aperture Radar (SAR), which includes synthetic aperture principle, pulse compression technology, and signal processing technology. Compared with the modelbased method, it does not need to extract features manually and has achieved a high recognition rate in the field of SAR target recognition. The enhanced-shape CNN strengthened the shape features of the target at the input, constructing a three-channel pseudocolor image as data set, so that the convolutional neural network can tend to pay more attention to target shape. E channel attention module mechanism, i.e., Squeeze-and-Excitation (SE) module [30], can effectively increase the channel weights that are beneficial for recognition and suppress feature that are less useful in CNNs. SE module distributes channel weights more evenly in target recognition, such that there is essentially the same as CNN, as noted in paper [29].
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